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null | null | # Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed] | {"license": "openrail"} | WUMEICHARLIE/mytrineo | null | [
"arxiv:1910.09700",
"license:openrail",
"region:us"
] | null | 2024-04-20T02:25:11+00:00 | [
"1910.09700"
] | [] | TAGS
#arxiv-1910.09700 #license-openrail #region-us
| # Model Card for Model ID
This modelcard aims to be a base template for new models. It has been generated using this raw template.
## Model Details
### Model Description
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- Shared by [optional]:
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- Finetuned from model [optional]:
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## Uses
### Direct Use
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### Out-of-Scope Use
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
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## Technical Specifications [optional]
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### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This should link to a Dataset Card if possible. -->
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
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## Technical Specifications [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed] | {"library_name": "transformers", "tags": ["unsloth"]} | weifar/llama3-8b-500 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"unsloth",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-20T02:27:12+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #unsloth #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
|
# Model Card for Model ID
## Model Details
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- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-v3-xsmall-otat-recommened-hp
This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) on the DandinPower/review_onlytitleandtext dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0799
- Accuracy: 0.6391
- Macro F1: 0.6372
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| 0.97 | 1.14 | 500 | 0.9598 | 0.5957 | 0.5847 |
| 0.8311 | 2.29 | 1000 | 0.8698 | 0.6371 | 0.6267 |
| 0.7452 | 3.43 | 1500 | 0.8271 | 0.6457 | 0.6471 |
| 0.678 | 4.57 | 2000 | 0.8802 | 0.6421 | 0.6359 |
| 0.6161 | 5.71 | 2500 | 0.9048 | 0.6457 | 0.6463 |
| 0.5784 | 6.86 | 3000 | 0.9604 | 0.6439 | 0.6452 |
| 0.5068 | 8.0 | 3500 | 1.0170 | 0.6453 | 0.6452 |
| 0.4247 | 9.14 | 4000 | 1.0799 | 0.6391 | 0.6372 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"language": ["en"], "license": "mit", "tags": ["nycu-112-2-datamining-hw2", "generated_from_trainer"], "datasets": ["DandinPower/review_onlytitleandtext"], "metrics": ["accuracy"], "base_model": "microsoft/deberta-v3-xsmall", "model-index": [{"name": "deberta-v3-xsmall-otat-recommened-hp", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "DandinPower/review_onlytitleandtext", "type": "DandinPower/review_onlytitleandtext"}, "metrics": [{"type": "accuracy", "value": 0.6391428571428571, "name": "Accuracy"}]}]}]} | DandinPower/deberta-v3-xsmall-otat-recommened-hp | null | [
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"nycu-112-2-datamining-hw2",
"generated_from_trainer",
"en",
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"base_model:microsoft/deberta-v3-xsmall",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T02:30:25+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #deberta-v2 #text-classification #nycu-112-2-datamining-hw2 #generated_from_trainer #en #dataset-DandinPower/review_onlytitleandtext #base_model-microsoft/deberta-v3-xsmall #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| deberta-v3-xsmall-otat-recommened-hp
====================================
This model is a fine-tuned version of microsoft/deberta-v3-xsmall on the DandinPower/review\_onlytitleandtext dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0799
* Accuracy: 0.6391
* Macro F1: 0.6372
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 4.5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 10
### Training results
### Framework versions
* Transformers 4.39.3
* Pytorch 2.2.2+cu121
* Datasets 2.18.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4.5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.2.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #safetensors #deberta-v2 #text-classification #nycu-112-2-datamining-hw2 #generated_from_trainer #en #dataset-DandinPower/review_onlytitleandtext #base_model-microsoft/deberta-v3-xsmall #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4.5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.2.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
text-generation | transformers |
# Poscye-oohdl3s-8B
poscye-oohdl3s-8B is a merge of the following models using [mergekit](https://github.com/arcee-ai/mergekit):
* [mlabonne/OrpoLlama-3-8B](https://huggingface.co/mlabonne/OrpoLlama-3-8B)
* [Muhammad2003/Llama3-8B-OpenHermes-DPO](https://huggingface.co/Muhammad2003/Llama3-8B-OpenHermes-DPO)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: mlabonne/OrpoLlama-3-8B
layer_range: [0, 32]
- model: Muhammad2003/Llama3-8B-OpenHermes-DPO
layer_range: [0, 32]
merge_method: slerp
base_model: mlabonne/OrpoLlama-3-8B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
``` | {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["text-generation", "autotrain_compatible", "endpoints_compatible", "chatml", "text-generation-inference", "transformers", "slerp", "llama-3-8B", "merge", "mergekit"], "datasets": ["mlabonne/orpo-dpo-mix-40k", "mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha"], "base_model": ["mlabonne/OrpoLlama-3-8B", "Muhammad2003/Llama3-8B-OpenHermes-DPO"], "pipeline_tag": "text-generation", "thumbnail": "https://huggingface.co/pabloce/poscye-oohdl3s-8B/resolve/main/poscye-oohdl3s-8B.png"} | pabloce/poscye-oohdl3s-8B | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"chatml",
"text-generation-inference",
"slerp",
"llama-3-8B",
"merge",
"mergekit",
"conversational",
"en",
"dataset:mlabonne/orpo-dpo-mix-40k",
"dataset:mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha",
"base_model:mlabonne/OrpoLlama-3-8B",
"base_model:Muhammad2003/Llama3-8B-OpenHermes-DPO",
"license:apache-2.0",
"region:us"
] | null | 2024-04-20T02:31:17+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #llama #text-generation #autotrain_compatible #endpoints_compatible #chatml #text-generation-inference #slerp #llama-3-8B #merge #mergekit #conversational #en #dataset-mlabonne/orpo-dpo-mix-40k #dataset-mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha #base_model-mlabonne/OrpoLlama-3-8B #base_model-Muhammad2003/Llama3-8B-OpenHermes-DPO #license-apache-2.0 #region-us
|
# Poscye-oohdl3s-8B
!image/png
poscye-oohdl3s-8B is a merge of the following models using mergekit:
* mlabonne/OrpoLlama-3-8B
* Muhammad2003/Llama3-8B-OpenHermes-DPO
## Configuration
| [
"# Poscye-oohdl3s-8B\n\n!image/png\n\nposcye-oohdl3s-8B is a merge of the following models using mergekit:\n* mlabonne/OrpoLlama-3-8B\n* Muhammad2003/Llama3-8B-OpenHermes-DPO",
"## Configuration"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #autotrain_compatible #endpoints_compatible #chatml #text-generation-inference #slerp #llama-3-8B #merge #mergekit #conversational #en #dataset-mlabonne/orpo-dpo-mix-40k #dataset-mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha #base_model-mlabonne/OrpoLlama-3-8B #base_model-Muhammad2003/Llama3-8B-OpenHermes-DPO #license-apache-2.0 #region-us \n",
"# Poscye-oohdl3s-8B\n\n!image/png\n\nposcye-oohdl3s-8B is a merge of the following models using mergekit:\n* mlabonne/OrpoLlama-3-8B\n* Muhammad2003/Llama3-8B-OpenHermes-DPO",
"## Configuration"
] |
text-classification | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed] | {"library_name": "transformers", "tags": []} | tiya1012/suicidepost_BERT | null | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T02:35:29+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
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## Uses
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
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- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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## Technical Specifications [optional]
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### Compute Infrastructure
#### Hardware
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[optional]
BibTeX:
APA:
## Glossary [optional]
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## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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[More Information Needed]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | zhenchuan/code-search-net-tokenizer | null | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T02:37:33+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
null | null |
<style>
.title-container {
display: flex;
justify-content: center;
align-items: center;
height: 100vh; /* Adjust this value to position the title vertically */
}
.title {
font-size: 2.5em;
text-align: center;
color: #333;
font-family: "lucida sans unicode", "lucida grande", sans-serif;
font-style: italic;
font-weight: bold;
font-variant: small-caps;
letter-spacing: 0.05em;
padding: 0.5em 0;
background: transparent;
}
.title span {
background: -webkit-linear-gradient(45deg, #99E2FF, #FF5C95);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
}
.custom-table {
table-layout: fixed;
width: 100%;
border-collapse: collapse;
margin-top: 0em;
}
.custom-table td {
width: 50%;
vertical-align: top;
padding: 10px;
box-shadow: 0px 0px 0px 0px rgba(0, 0, 0, 0.15);
}
.custom-image-container {
position: relative;
width: 100%;
margin-bottom: 0em;
overflow: hidden;
border-radius: 10px;
transition: transform .7s;
/* Smooth transition for the container */
}
.custom-image-container:hover {
transform: scale(1.05);
/* Scale the container on hover */
}
.custom-image {
width: 100%;
height: auto;
object-fit: cover;
border-radius: 10px;
transition: transform .7s;
margin-bottom: 0em;
}
.custom-button {
width: auto;
height: 100px;
object-fit: cover;
border-radius: 10px;
transition: transform .7s;
margin-bottom: 0em;
display: block;
margin-left: auto;
margin-right: auto;
}
</style>
<h1 class="title">
<span>Pettanko Rouramashin PonySR v1</span>
</h1>
<table class="custom-table">
<tr>
<td>
<div class="custom-image-container">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/other_samples/PonySR_v1/sample_01.png" alt="sample1">
</div>
</td>
<td>
<div class="custom-image-container">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/other_samples/PonySR_v1/sample_02.png" alt="sample2">
</div>
</td>
<td>
<div class="custom-image-container">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/other_samples/PonySR_v1/sample_03.png" alt="sample1">
</div>
</td>
</tr>
</table>
<table class="custom-table">
<tr>
<td>
<div class="custom-image-container">
<a href="https://www.buymeacoffee.com/pettankoutei" target="_blank"><img class="custom-button" src="https://huggingface.co/pettankoutei/Loras/resolve/main/bmac_button.png" alt="Buy me a cookie!" style="height: 100px;" /></a>
</div>
</td>
</tr>
</table>
> Support me on Buy Me a Coffee! for early access of new content and exclusive models!
## About
This model attempts to transfer the style of SD1.5 based Rouramashin SR model, into a SDXL Pony based model.
Its style sits between a '2.5D' animated model and a '3D render' model. You can use quality tags to help push the style either way.
When using 2D/anime character LoRAs you might want to use tags such as (raw photo, realistic) to help maintain its style, in addition to properly adjusting the LoRAs weights.
Version 1.0 consists of a custom block merge recipe between multiple Pony v6 based models and privately trained style lycoris models, with additional fine-tuning made from carefully selected images generated from Rouramashin SR v2 model.
The series is developed with the following goals:
- Flexible style with good vibrant colors.
- Prompting with booru tags.
- Custom character proportions and composition.
- Good quality with low noise for pictures without hires fix or upscaling.
- Good adherence to complex/long prompts with default CFG value of 7.
## Recommended parameters:
- Positive prompt: score_9, score_8_up, score_7_up, rating_safe BREAK
- Negative prompt: score_4, score_5, score_6, simple background, white background, source_furry, source_pony, source_cartoon
- Clip skip: 2
- Sampling method: Euler a, DPM++ 2M Karras, DPM++ SDE Karras (suggested, use whatever you prefer)
- Sampling steps: 20 (minimum)
- CFG Scale: 7 (default during tests)
- Hires. fix:
- Upscaler: 4x-AnimeSharp (recommended)
- Hires steps: 10
- Denoise: 0,34
- Upscale by: 1.5
For this model hires. fix is not strictly necessary, but if characters are far away, hires. fix or ADetailer (facedetailer) might be necessary.
## Disclamer
All the models that I have developed and published on this platform are for entertainment purposes only. They are derived from the base Stable Diffusion model, which means it also includes its inherent issues, limitations and unfiltered capabilities. With that in mind, I am in no way responsible for any content that the user creates with malicious intent while using any of these models. The user assumes complete responsibility for the misuse of these tools and any unethical content created with them. This disclaimer is subject to the laws of the United States of America and the state of California. I reserve the right to remove or report any content that violates the terms of service, ethical standards, or applicable laws.
## License conditions
All my content can be used for personal, non-commercial purposes only. All of them include publicly available resources in its development, each with various licenses. You are not allowed to upload any of these models elsewhere or use any of them in online generation services. Feel free to use this for any merges, but I'd be glad if you mentioned this model in the merge description.
| {"license": "unknown"} | pettankoutei/pettankoRouramashin_PonySRv1 | null | [
"license:unknown",
"region:us"
] | null | 2024-04-20T02:37:46+00:00 | [] | [] | TAGS
#license-unknown #region-us
|
<style>
.title-container {
display: flex;
justify-content: center;
align-items: center;
height: 100vh; /* Adjust this value to position the title vertically */
}
.title {
font-size: 2.5em;
text-align: center;
color: #333;
font-family: "lucida sans unicode", "lucida grande", sans-serif;
font-style: italic;
font-weight: bold;
font-variant: small-caps;
letter-spacing: 0.05em;
padding: 0.5em 0;
background: transparent;
}
.title span {
background: -webkit-linear-gradient(45deg, #99E2FF, #FF5C95);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
}
.custom-table {
table-layout: fixed;
width: 100%;
border-collapse: collapse;
margin-top: 0em;
}
.custom-table td {
width: 50%;
vertical-align: top;
padding: 10px;
box-shadow: 0px 0px 0px 0px rgba(0, 0, 0, 0.15);
}
.custom-image-container {
position: relative;
width: 100%;
margin-bottom: 0em;
overflow: hidden;
border-radius: 10px;
transition: transform .7s;
/* Smooth transition for the container */
}
.custom-image-container:hover {
transform: scale(1.05);
/* Scale the container on hover */
}
.custom-image {
width: 100%;
height: auto;
object-fit: cover;
border-radius: 10px;
transition: transform .7s;
margin-bottom: 0em;
}
.custom-button {
width: auto;
height: 100px;
object-fit: cover;
border-radius: 10px;
transition: transform .7s;
margin-bottom: 0em;
display: block;
margin-left: auto;
margin-right: auto;
}
</style>
<h1 class="title">
<span>Pettanko Rouramashin PonySR v1</span>
</h1>
<table class="custom-table">
<tr>
<td>
<div class="custom-image-container">
<img class="custom-image" src="URL alt="sample1">
</div>
</td>
<td>
<div class="custom-image-container">
<img class="custom-image" src="URL alt="sample2">
</div>
</td>
<td>
<div class="custom-image-container">
<img class="custom-image" src="URL alt="sample1">
</div>
</td>
</tr>
</table>
<table class="custom-table">
<tr>
<td>
<div class="custom-image-container">
<a href="URL target="_blank"><img class="custom-button" src="URL alt="Buy me a cookie!" style="height: 100px;" /></a>
</div>
</td>
</tr>
</table>
> Support me on Buy Me a Coffee! for early access of new content and exclusive models!
## About
This model attempts to transfer the style of SD1.5 based Rouramashin SR model, into a SDXL Pony based model.
Its style sits between a '2.5D' animated model and a '3D render' model. You can use quality tags to help push the style either way.
When using 2D/anime character LoRAs you might want to use tags such as (raw photo, realistic) to help maintain its style, in addition to properly adjusting the LoRAs weights.
Version 1.0 consists of a custom block merge recipe between multiple Pony v6 based models and privately trained style lycoris models, with additional fine-tuning made from carefully selected images generated from Rouramashin SR v2 model.
The series is developed with the following goals:
- Flexible style with good vibrant colors.
- Prompting with booru tags.
- Custom character proportions and composition.
- Good quality with low noise for pictures without hires fix or upscaling.
- Good adherence to complex/long prompts with default CFG value of 7.
## Recommended parameters:
- Positive prompt: score_9, score_8_up, score_7_up, rating_safe BREAK
- Negative prompt: score_4, score_5, score_6, simple background, white background, source_furry, source_pony, source_cartoon
- Clip skip: 2
- Sampling method: Euler a, DPM++ 2M Karras, DPM++ SDE Karras (suggested, use whatever you prefer)
- Sampling steps: 20 (minimum)
- CFG Scale: 7 (default during tests)
- Hires. fix:
- Upscaler: 4x-AnimeSharp (recommended)
- Hires steps: 10
- Denoise: 0,34
- Upscale by: 1.5
For this model hires. fix is not strictly necessary, but if characters are far away, hires. fix or ADetailer (facedetailer) might be necessary.
## Disclamer
All the models that I have developed and published on this platform are for entertainment purposes only. They are derived from the base Stable Diffusion model, which means it also includes its inherent issues, limitations and unfiltered capabilities. With that in mind, I am in no way responsible for any content that the user creates with malicious intent while using any of these models. The user assumes complete responsibility for the misuse of these tools and any unethical content created with them. This disclaimer is subject to the laws of the United States of America and the state of California. I reserve the right to remove or report any content that violates the terms of service, ethical standards, or applicable laws.
## License conditions
All my content can be used for personal, non-commercial purposes only. All of them include publicly available resources in its development, each with various licenses. You are not allowed to upload any of these models elsewhere or use any of them in online generation services. Feel free to use this for any merges, but I'd be glad if you mentioned this model in the merge description.
| [
"## About\n\nThis model attempts to transfer the style of SD1.5 based Rouramashin SR model, into a SDXL Pony based model.\nIts style sits between a '2.5D' animated model and a '3D render' model. You can use quality tags to help push the style either way.\nWhen using 2D/anime character LoRAs you might want to use tags such as (raw photo, realistic) to help maintain its style, in addition to properly adjusting the LoRAs weights.\n\nVersion 1.0 consists of a custom block merge recipe between multiple Pony v6 based models and privately trained style lycoris models, with additional fine-tuning made from carefully selected images generated from Rouramashin SR v2 model.\n\nThe series is developed with the following goals:\n- Flexible style with good vibrant colors.\n- Prompting with booru tags.\n- Custom character proportions and composition.\n- Good quality with low noise for pictures without hires fix or upscaling.\n- Good adherence to complex/long prompts with default CFG value of 7.",
"## Recommended parameters:\n\n- Positive prompt: score_9, score_8_up, score_7_up, rating_safe BREAK\n- Negative prompt: score_4, score_5, score_6, simple background, white background, source_furry, source_pony, source_cartoon\n- Clip skip: 2\n- Sampling method: Euler a, DPM++ 2M Karras, DPM++ SDE Karras (suggested, use whatever you prefer)\n- Sampling steps: 20 (minimum)\n- CFG Scale: 7 (default during tests)\n- Hires. fix:\n- Upscaler: 4x-AnimeSharp (recommended)\n - Hires steps: 10\n - Denoise: 0,34\n - Upscale by: 1.5\n\nFor this model hires. fix is not strictly necessary, but if characters are far away, hires. fix or ADetailer (facedetailer) might be necessary.",
"## Disclamer\n\nAll the models that I have developed and published on this platform are for entertainment purposes only. They are derived from the base Stable Diffusion model, which means it also includes its inherent issues, limitations and unfiltered capabilities. With that in mind, I am in no way responsible for any content that the user creates with malicious intent while using any of these models. The user assumes complete responsibility for the misuse of these tools and any unethical content created with them. This disclaimer is subject to the laws of the United States of America and the state of California. I reserve the right to remove or report any content that violates the terms of service, ethical standards, or applicable laws.",
"## License conditions\n\nAll my content can be used for personal, non-commercial purposes only. All of them include publicly available resources in its development, each with various licenses. You are not allowed to upload any of these models elsewhere or use any of them in online generation services. Feel free to use this for any merges, but I'd be glad if you mentioned this model in the merge description."
] | [
"TAGS\n#license-unknown #region-us \n",
"## About\n\nThis model attempts to transfer the style of SD1.5 based Rouramashin SR model, into a SDXL Pony based model.\nIts style sits between a '2.5D' animated model and a '3D render' model. You can use quality tags to help push the style either way.\nWhen using 2D/anime character LoRAs you might want to use tags such as (raw photo, realistic) to help maintain its style, in addition to properly adjusting the LoRAs weights.\n\nVersion 1.0 consists of a custom block merge recipe between multiple Pony v6 based models and privately trained style lycoris models, with additional fine-tuning made from carefully selected images generated from Rouramashin SR v2 model.\n\nThe series is developed with the following goals:\n- Flexible style with good vibrant colors.\n- Prompting with booru tags.\n- Custom character proportions and composition.\n- Good quality with low noise for pictures without hires fix or upscaling.\n- Good adherence to complex/long prompts with default CFG value of 7.",
"## Recommended parameters:\n\n- Positive prompt: score_9, score_8_up, score_7_up, rating_safe BREAK\n- Negative prompt: score_4, score_5, score_6, simple background, white background, source_furry, source_pony, source_cartoon\n- Clip skip: 2\n- Sampling method: Euler a, DPM++ 2M Karras, DPM++ SDE Karras (suggested, use whatever you prefer)\n- Sampling steps: 20 (minimum)\n- CFG Scale: 7 (default during tests)\n- Hires. fix:\n- Upscaler: 4x-AnimeSharp (recommended)\n - Hires steps: 10\n - Denoise: 0,34\n - Upscale by: 1.5\n\nFor this model hires. fix is not strictly necessary, but if characters are far away, hires. fix or ADetailer (facedetailer) might be necessary.",
"## Disclamer\n\nAll the models that I have developed and published on this platform are for entertainment purposes only. They are derived from the base Stable Diffusion model, which means it also includes its inherent issues, limitations and unfiltered capabilities. With that in mind, I am in no way responsible for any content that the user creates with malicious intent while using any of these models. The user assumes complete responsibility for the misuse of these tools and any unethical content created with them. This disclaimer is subject to the laws of the United States of America and the state of California. I reserve the right to remove or report any content that violates the terms of service, ethical standards, or applicable laws.",
"## License conditions\n\nAll my content can be used for personal, non-commercial purposes only. All of them include publicly available resources in its development, each with various licenses. You are not allowed to upload any of these models elsewhere or use any of them in online generation services. Feel free to use this for any merges, but I'd be glad if you mentioned this model in the merge description."
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# arazn-whisper-small-v2
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "openai/whisper-small", "model-index": [{"name": "arazn-whisper-small-v2", "results": []}]} | ahmedheakl/arazn-whisper-small | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T02:38:45+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #base_model-openai/whisper-small #license-apache-2.0 #endpoints_compatible #region-us
|
# arazn-whisper-small-v2
This model is a fine-tuned version of openai/whisper-small on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| [
"# arazn-whisper-small-v2\n\nThis model is a fine-tuned version of openai/whisper-small on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 100\n- num_epochs: 1\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #base_model-openai/whisper-small #license-apache-2.0 #endpoints_compatible #region-us \n",
"# arazn-whisper-small-v2\n\nThis model is a fine-tuned version of openai/whisper-small on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 100\n- num_epochs: 1\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
null | transformers |
# Uploaded model
- **Developed by:** smsaurabhv
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.2-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl"], "base_model": "unsloth/mistral-7b-instruct-v0.2-bnb-4bit"} | smsaurabhv/mistralnew1 | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T02:39:04+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #text-generation-inference #unsloth #mistral #trl #en #base_model-unsloth/mistral-7b-instruct-v0.2-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: smsaurabhv
- License: apache-2.0
- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit
This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
| [
"# Uploaded model\n\n- Developed by: smsaurabhv\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
"TAGS\n#transformers #safetensors #text-generation-inference #unsloth #mistral #trl #en #base_model-unsloth/mistral-7b-instruct-v0.2-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Uploaded model\n\n- Developed by: smsaurabhv\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | Grayx/sad_llama_5 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T02:42:14+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
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"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
null | adapter-transformers |
# Adapter `BigTMiami/seq_bn_amz_10k_help_class_no_pretrain_0_adapter` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/amazon_10k_helpfulness_dataset](https://huggingface.co/datasets/BigTMiami/amazon_10k_helpfulness_dataset/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("BigTMiami/seq_bn_amz_10k_help_class_no_pretrain_0_adapter", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> | {"tags": ["roberta", "adapter-transformers"], "datasets": ["BigTMiami/amazon_10k_helpfulness_dataset"]} | BigTMiami/seq_bn_amz_10k_help_class_no_pretrain_0_adapter | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/amazon_10k_helpfulness_dataset",
"region:us"
] | null | 2024-04-20T02:43:21+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/amazon_10k_helpfulness_dataset #region-us
|
# Adapter 'BigTMiami/seq_bn_amz_10k_help_class_no_pretrain_0_adapter' for roberta-base
An adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_10k_helpfulness_dataset dataset and includes a prediction head for classification.
This adapter was created for usage with the Adapters library.
## Usage
First, install 'adapters':
Now, the adapter can be loaded and activated like this:
## Architecture & Training
## Evaluation results
| [
"# Adapter 'BigTMiami/seq_bn_amz_10k_help_class_no_pretrain_0_adapter' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_10k_helpfulness_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] | [
"TAGS\n#adapter-transformers #roberta #dataset-BigTMiami/amazon_10k_helpfulness_dataset #region-us \n",
"# Adapter 'BigTMiami/seq_bn_amz_10k_help_class_no_pretrain_0_adapter' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_10k_helpfulness_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
text-generation | transformers |
# solar-merge-v1.0
solar-merge-v1.0 is a Mixture of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0)
* [heavytail/kullm-solar](https://huggingface.co/heavytail/kullm-solar)
## 🧩 Configuration
```yaml
base_model: upstage/SOLAR-10.7B-v1.0
dtype: float16
experts:
- source_model: upstage/SOLAR-10.7B-Instruct-v1.0
positive_prompts: ["당신은 친절한 보편적인 어시스턴트이다."]
- source_model: heavytail/kullm-solar
positive_prompts: ["당신은 친절한 어시스턴트이다."]
gate_mode: cheap_embed
tokenizer_source: base
```
## 💻 Usage
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "jieunhan/solar-merge-v1.0"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)
messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"license": "apache-2.0", "tags": ["moe", "frankenmoe", "merge", "mergekit", "lazymergekit", "upstage/SOLAR-10.7B-Instruct-v1.0", "heavytail/kullm-solar"], "base_model": ["upstage/SOLAR-10.7B-Instruct-v1.0", "heavytail/kullm-solar"]} | jieunhan/solar-merge-v1.0 | null | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"moe",
"frankenmoe",
"merge",
"mergekit",
"lazymergekit",
"upstage/SOLAR-10.7B-Instruct-v1.0",
"heavytail/kullm-solar",
"base_model:upstage/SOLAR-10.7B-Instruct-v1.0",
"base_model:heavytail/kullm-solar",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T02:43:48+00:00 | [] | [] | TAGS
#transformers #safetensors #mixtral #text-generation #moe #frankenmoe #merge #mergekit #lazymergekit #upstage/SOLAR-10.7B-Instruct-v1.0 #heavytail/kullm-solar #base_model-upstage/SOLAR-10.7B-Instruct-v1.0 #base_model-heavytail/kullm-solar #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# solar-merge-v1.0
solar-merge-v1.0 is a Mixture of Experts (MoE) made with the following models using LazyMergekit:
* upstage/SOLAR-10.7B-Instruct-v1.0
* heavytail/kullm-solar
## Configuration
## Usage
| [
"# solar-merge-v1.0\n\nsolar-merge-v1.0 is a Mixture of Experts (MoE) made with the following models using LazyMergekit:\n* upstage/SOLAR-10.7B-Instruct-v1.0\n* heavytail/kullm-solar",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #mixtral #text-generation #moe #frankenmoe #merge #mergekit #lazymergekit #upstage/SOLAR-10.7B-Instruct-v1.0 #heavytail/kullm-solar #base_model-upstage/SOLAR-10.7B-Instruct-v1.0 #base_model-heavytail/kullm-solar #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# solar-merge-v1.0\n\nsolar-merge-v1.0 is a Mixture of Experts (MoE) made with the following models using LazyMergekit:\n* upstage/SOLAR-10.7B-Instruct-v1.0\n* heavytail/kullm-solar",
"## Configuration",
"## Usage"
] |
translation | transformers.js | ERROR: type should be string, got "\n\nhttps://hf.co/vinai/vinai-translate-en2vi-v2 with ONNX weights to be compatible with Transformers.js.\n\nPlease check out this demo using the model:\n[](https://huggingface.co/spaces/huuquyet/translator-tamagui)\n\n\n# A Vietnamese-English Neural Machine Translation System\n\nOur pre-trained VinAI Translate models are state-of-the-art text translation models for Vietnamese-to-English and English-to-Vietnamese, respectively. The general architecture and experimental results of VinAI Translate can be found in [our paper](https://openreview.net/forum?id=CRg-RaxKnai):\n\n\n @inproceedings{vinaitranslate,\n title = {{A Vietnamese-English Neural Machine Translation System}},\n author = {Thien Hai Nguyen and \n Tuan-Duy H. Nguyen and \n Duy Phung and \n Duy Tran-Cong Nguyen and \n Hieu Minh Tran and \n Manh Luong and \n Tin Duy Vo and \n Hung Hai Bui and \n Dinh Phung and \n Dat Quoc Nguyen},\n booktitle = {Proceedings of the 23rd Annual Conference of the International Speech Communication Association: Show and Tell (INTERSPEECH)},\n year = {2022}\n }\n \nPlease **CITE** our paper whenever the pre-trained models or the system are used to help produce published results or incorporated into other software.\nFor further information or requests, please go to [VinAI Translate's homepage](https://github.com/VinAIResearch/VinAI_Translate)!" | {"language": ["vi", "en"], "license": "wtfpl", "library_name": "transformers.js", "pipeline_tag": "translation"} | huuquyet/vinai-translate-en2vi-v2 | null | [
"transformers.js",
"onnx",
"mbart",
"text2text-generation",
"translation",
"vi",
"en",
"license:wtfpl",
"has_space",
"region:us"
] | null | 2024-04-20T02:48:49+00:00 | [] | [
"vi",
"en"
] | TAGS
#transformers.js #onnx #mbart #text2text-generation #translation #vi #en #license-wtfpl #has_space #region-us
|
URL with ONNX weights to be compatible with URL.
Please check out this demo using the model:
},
year = {2022}
}
Please CITE our paper whenever the pre-trained models or the system are used to help produce published results or incorporated into other software.
For further information or requests, please go to VinAI Translate's homepage! | [
"# A Vietnamese-English Neural Machine Translation System\n\nOur pre-trained VinAI Translate models are state-of-the-art text translation models for Vietnamese-to-English and English-to-Vietnamese, respectively. The general architecture and experimental results of VinAI Translate can be found in our paper:\n\n\n @inproceedings{vinaitranslate,\n title = {{A Vietnamese-English Neural Machine Translation System}},\n author = {Thien Hai Nguyen and \n Tuan-Duy H. Nguyen and \n Duy Phung and \n Duy Tran-Cong Nguyen and \n Hieu Minh Tran and \n Manh Luong and \n Tin Duy Vo and \n Hung Hai Bui and \n Dinh Phung and \n Dat Quoc Nguyen},\n booktitle = {Proceedings of the 23rd Annual Conference of the International Speech Communication Association: Show and Tell (INTERSPEECH)},\n year = {2022}\n }\n \nPlease CITE our paper whenever the pre-trained models or the system are used to help produce published results or incorporated into other software.\nFor further information or requests, please go to VinAI Translate's homepage!"
] | [
"TAGS\n#transformers.js #onnx #mbart #text2text-generation #translation #vi #en #license-wtfpl #has_space #region-us \n",
"# A Vietnamese-English Neural Machine Translation System\n\nOur pre-trained VinAI Translate models are state-of-the-art text translation models for Vietnamese-to-English and English-to-Vietnamese, respectively. The general architecture and experimental results of VinAI Translate can be found in our paper:\n\n\n @inproceedings{vinaitranslate,\n title = {{A Vietnamese-English Neural Machine Translation System}},\n author = {Thien Hai Nguyen and \n Tuan-Duy H. Nguyen and \n Duy Phung and \n Duy Tran-Cong Nguyen and \n Hieu Minh Tran and \n Manh Luong and \n Tin Duy Vo and \n Hung Hai Bui and \n Dinh Phung and \n Dat Quoc Nguyen},\n booktitle = {Proceedings of the 23rd Annual Conference of the International Speech Communication Association: Show and Tell (INTERSPEECH)},\n year = {2022}\n }\n \nPlease CITE our paper whenever the pre-trained models or the system are used to help produce published results or incorporated into other software.\nFor further information or requests, please go to VinAI Translate's homepage!"
] |
text-to-speech | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# speecht5_finetuned_voxpopuli_nl
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the facebook/voxpopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4559
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 0.5109 | 3.5730 | 1000 | 0.4749 |
| 0.4885 | 7.1460 | 2000 | 0.4626 |
| 0.4819 | 10.7191 | 3000 | 0.4578 |
| 0.486 | 14.2921 | 4000 | 0.4559 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1 | {"license": "mit", "tags": ["generated_from_trainer", "text-to-speech"], "datasets": ["facebook/voxpopuli"], "base_model": "microsoft/speecht5_tts", "model-index": [{"name": "speecht5_finetuned_voxpopuli_nl", "results": []}]} | vadhri/speecht5_finetuned_voxpopuli_nl | null | [
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"text-to-speech",
"dataset:facebook/voxpopuli",
"base_model:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2024-04-20T02:51:15+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #speecht5 #text-to-audio #generated_from_trainer #text-to-speech #dataset-facebook/voxpopuli #base_model-microsoft/speecht5_tts #license-mit #endpoints_compatible #has_space #region-us
| speecht5\_finetuned\_voxpopuli\_nl
==================================
This model is a fine-tuned version of microsoft/speecht5\_tts on the facebook/voxpopuli dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4559
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 4
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 4000
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.40.0
* Pytorch 2.2.1+cu121
* Datasets 2.18.0
* Tokenizers 0.19.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.18.0\n* Tokenizers 0.19.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #speecht5 #text-to-audio #generated_from_trainer #text-to-speech #dataset-facebook/voxpopuli #base_model-microsoft/speecht5_tts #license-mit #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.18.0\n* Tokenizers 0.19.1"
] |
sentence-similarity | transformers |
<!-- **English** | [中文](./README_zh.md) -->
# gte-base-en-v1.5
We introduce `gte-v1.5` series, upgraded `gte` embeddings that support the context length of up to **8192**, while further enhancing model performance.
The models are built upon the `transformer++` encoder [backbone](https://huggingface.co/Alibaba-NLP/new-impl) (BERT + RoPE + GLU).
The `gte-v1.5` series achieve state-of-the-art scores on the MTEB benchmark within the same model size category and prodvide competitive on the LoCo long-context retrieval tests (refer to [Evaluation](#evaluation)).
We also present the [`gte-Qwen1.5-7B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct),
a SOTA instruction-tuned multi-lingual embedding model that ranked 2nd in MTEB and 1st in C-MTEB.
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Institute for Intelligent Computing, Alibaba Group
- **Model type:** Text Embeddings
- **Paper:** Coming soon.
<!-- - **Demo [optional]:** [More Information Needed] -->
### Model list
| Models | Language | Model Size | Max Seq. Length | Dimension | MTEB-en | LoCo |
|:-----: | :-----: |:-----: |:-----: |:-----: | :-----: | :-----: |
|[`gte-Qwen1.5-7B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct)| Multiple | 7720 | 32768 | 4096 | 67.34 | 87.57 |
|[`gte-large-en-v1.5`](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | English | 434 | 8192 | 1024 | 65.39 | 86.71 |
|[`gte-base-en-v1.5`](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | English | 137 | 8192 | 768 | 64.11 | 87.44 |
## How to Get Started with the Model
Use the code below to get started with the model.
```python
# Requires transformers>=4.36.0
import torch.nn.functional as F
from transformers import AutoModel, AutoTokenizer
input_texts = [
"what is the capital of China?",
"how to implement quick sort in python?",
"Beijing",
"sorting algorithms"
]
model_path = 'Alibaba-NLP/gte-base-en-v1.5'
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModel.from_pretrained(model_path, trust_remote_code=True)
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=8192, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = outputs.last_hidden_state[:, 0]
# (Optionally) normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:1] @ embeddings[1:].T) * 100
print(scores.tolist())
```
**It is recommended to install xformers and enable unpadding for acceleration, refer to [enable-unpadding-and-xformers](https://huggingface.co/Alibaba-NLP/new-impl#recommendation-enable-unpadding-and-acceleration-with-xformers).**
Use with `sentence-transformers`:
```python
# Requires sentence_transformers>=2.7.0
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
sentences = ['That is a happy person', 'That is a very happy person']
model = SentenceTransformer('Alibaba-NLP/gte-base-en-v1.5', trust_remote_code=True)
embeddings = model.encode(sentences)
print(cos_sim(embeddings[0], embeddings[1]))
```
Use with `transformers.js`:
```js
// npm i @xenova/transformers
import { pipeline, dot } from '@xenova/transformers';
// Create feature extraction pipeline
const extractor = await pipeline('feature-extraction', 'Alibaba-NLP/gte-base-en-v1.5', {
quantized: false, // Comment out this line to use the quantized version
});
// Generate sentence embeddings
const sentences = [
"what is the capital of China?",
"how to implement quick sort in python?",
"Beijing",
"sorting algorithms"
]
const output = await extractor(sentences, { normalize: true, pooling: 'cls' });
// Compute similarity scores
const [source_embeddings, ...document_embeddings ] = output.tolist();
const similarities = document_embeddings.map(x => 100 * dot(source_embeddings, x));
console.log(similarities); // [34.504930869007296, 64.03973265120138, 19.520042686034362]
```
## Training Details
### Training Data
- Masked language modeling (MLM): `c4-en`
- Weak-supervised contrastive (WSC) pre-training: [GTE](https://arxiv.org/pdf/2308.03281.pdf) pre-training data
- Supervised contrastive fine-tuning: [GTE](https://arxiv.org/pdf/2308.03281.pdf) fine-tuning data
### Training Procedure
To enable the backbone model to support a context length of 8192, we adopted a multi-stage training strategy.
The model first undergoes preliminary MLM pre-training on shorter lengths.
And then, we resample the data, reducing the proportion of short texts, and continue the MLM pre-training.
The entire training process is as follows:
- MLM-2048: lr 5e-4, mlm_probability 0.3, batch_size 4096, num_steps 70000, rope_base 10000
- MLM-8192: lr 5e-5, mlm_probability 0.3, batch_size 1024, num_steps 20000, rope_base 500000
- WSC: max_len 512, lr 2e-4, batch_size 32768, num_steps 100000
- Fine-tuning: TODO
## Evaluation
### MTEB
The results of other models are retrieved from [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard).
The gte evaluation setting: `mteb==1.2.0, fp16 auto mix precision, max_length=8192`, and set ntk scaling factor to 2 (equivalent to rope_base * 2).
| Model Name | Param Size (M) | Dimension | Sequence Length | Average (56) | Class. (12) | Clust. (11) | Pair Class. (3) | Reran. (4) | Retr. (15) | STS (10) | Summ. (1) |
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| [**gte-large-en-v1.5**](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 434 | 1024 | 8192 | **65.39** | 77.75 | 47.95 | 84.63 | 58.50 | 57.91 | 81.43 | 30.91 |
| [mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) | 335 | 1024 | 512 | 64.68 | 75.64 | 46.71 | 87.2 | 60.11 | 54.39 | 85 | 32.71 |
| [multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) | 560 | 1024 | 514 | 64.41 | 77.56 | 47.1 | 86.19 | 58.58 | 52.47 | 84.78 | 30.39 |
| [bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5)| 335 | 1024 | 512 | 64.23 | 75.97 | 46.08 | 87.12 | 60.03 | 54.29 | 83.11 | 31.61 |
| [**gte-base-en-v1.5**](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | 137 | 768 | 8192 | **64.11** | 77.17 | 46.82 | 85.33 | 57.66 | 54.09 | 81.97 | 31.17 |
| [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5)| 109 | 768 | 512 | 63.55 | 75.53 | 45.77 | 86.55 | 58.86 | 53.25 | 82.4 | 31.07 |
### LoCo
| Model Name | Dimension | Sequence Length | Average (5) | QsmsumRetrieval | SummScreenRetrieval | QasperAbastractRetrieval | QasperTitleRetrieval | GovReportRetrieval |
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| [gte-qwen1.5-7b](https://huggingface.co/Alibaba-NLP/gte-qwen1.5-7b) | 4096 | 32768 | 87.57 | 49.37 | 93.10 | 99.67 | 97.54 | 98.21 |
| [gte-large-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-v1.5) |1024 | 8192 | 86.71 | 44.55 | 92.61 | 99.82 | 97.81 | 98.74 |
| [gte-base-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-v1.5) | 768 | 8192 | 87.44 | 49.91 | 91.78 | 99.82 | 97.13 | 98.58 |
## Citation
If you find our paper or models helpful, please consider citing them as follows:
```
@article{li2023towards,
title={Towards general text embeddings with multi-stage contrastive learning},
author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan},
journal={arXiv preprint arXiv:2308.03281},
year={2023}
}
```
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37.802}, {"type": "ndcg_at_1000", "value": 49.274}, {"type": "ndcg_at_3", "value": 28.605999999999998}, {"type": "ndcg_at_5", "value": 26.21}, {"type": "precision_at_1", "value": 34.694}, {"type": "precision_at_10", "value": 21.837}, {"type": "precision_at_100", "value": 7.776}, {"type": "precision_at_1000", "value": 1.522}, {"type": "precision_at_3", "value": 28.571}, {"type": "precision_at_5", "value": 25.306}, {"type": "recall_at_1", "value": 3.0380000000000003}, {"type": "recall_at_10", "value": 16.298000000000002}, {"type": "recall_at_100", "value": 48.712}, {"type": "recall_at_1000", "value": 83.16799999999999}, {"type": "recall_at_3", "value": 7.265000000000001}, {"type": "recall_at_5", "value": 9.551}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB ToxicConversationsClassification", "type": "mteb/toxic_conversations_50k", "config": "default", "split": "test", "revision": "d7c0de2777da35d6aae2200a62c6e0e5af397c4c"}, "metrics": [{"type": "accuracy", "value": 83.978}, {"type": "ap", "value": 24.751887949330015}, {"type": "f1", "value": 66.8685134049279}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB TweetSentimentExtractionClassification", "type": "mteb/tweet_sentiment_extraction", "config": "default", "split": "test", "revision": "d604517c81ca91fe16a244d1248fc021f9ecee7a"}, "metrics": [{"type": "accuracy", "value": 61.573288058856825}, {"type": "f1", "value": 61.973261751726604}]}, {"task": {"type": "Clustering"}, "dataset": {"name": "MTEB TwentyNewsgroupsClustering", "type": "mteb/twentynewsgroups-clustering", "config": "default", "split": "test", "revision": "6125ec4e24fa026cec8a478383ee943acfbd5449"}, "metrics": [{"type": "v_measure", "value": 48.75483298792469}]}, {"task": {"type": "PairClassification"}, "dataset": {"name": "MTEB TwitterSemEval2015", "type": "mteb/twittersemeval2015-pairclassification", "config": "default", "split": "test", "revision": "70970daeab8776df92f5ea462b6173c0b46fd2d1"}, "metrics": [{"type": "cos_sim_accuracy", "value": 86.36824223639506}, {"type": "cos_sim_ap", "value": 75.53126388573047}, {"type": "cos_sim_f1", "value": 67.9912831688245}, {"type": "cos_sim_precision", "value": 66.11817501869858}, {"type": "cos_sim_recall", "value": 69.9736147757256}, {"type": "dot_accuracy", "value": 86.39804494248078}, {"type": "dot_ap", "value": 75.27598891718046}, {"type": "dot_f1", "value": 67.91146284159763}, {"type": "dot_precision", "value": 63.90505003490807}, {"type": "dot_recall", "value": 72.45382585751979}, {"type": "euclidean_accuracy", "value": 86.36228169517793}, {"type": "euclidean_ap", "value": 75.51438087434647}, {"type": "euclidean_f1", "value": 68.02370523061066}, {"type": "euclidean_precision", "value": 66.46525679758308}, {"type": "euclidean_recall", "value": 69.65699208443272}, {"type": "manhattan_accuracy", "value": 86.46361089586935}, {"type": "manhattan_ap", "value": 75.50800785730111}, {"type": "manhattan_f1", "value": 67.9220437187253}, {"type": "manhattan_precision", "value": 67.79705573080967}, {"type": "manhattan_recall", "value": 68.04749340369392}, {"type": "max_accuracy", "value": 86.46361089586935}, {"type": "max_ap", "value": 75.53126388573047}, {"type": "max_f1", "value": 68.02370523061066}]}, {"task": {"type": "PairClassification"}, "dataset": {"name": "MTEB TwitterURLCorpus", "type": "mteb/twitterurlcorpus-pairclassification", "config": "default", "split": "test", "revision": "8b6510b0b1fa4e4c4f879467980e9be563ec1cdf"}, "metrics": [{"type": "cos_sim_accuracy", "value": 88.80350836341057}, {"type": "cos_sim_ap", "value": 85.51101933260743}, {"type": "cos_sim_f1", "value": 77.9152271629704}, {"type": "cos_sim_precision", "value": 75.27815662910056}, {"type": "cos_sim_recall", "value": 80.74376347397599}, {"type": "dot_accuracy", "value": 88.84425815966158}, {"type": "dot_ap", "value": 85.49726945962519}, {"type": "dot_f1", "value": 77.94445269567801}, {"type": "dot_precision", "value": 75.27251864601261}, {"type": "dot_recall", "value": 80.81305820757623}, {"type": "euclidean_accuracy", "value": 88.80350836341057}, {"type": "euclidean_ap", "value": 85.4882880790211}, {"type": "euclidean_f1", "value": 77.87063284615103}, {"type": "euclidean_precision", "value": 74.61022927689595}, {"type": "euclidean_recall", "value": 81.42901139513397}, {"type": "manhattan_accuracy", "value": 88.7161873714441}, {"type": "manhattan_ap", "value": 85.45753871906821}, {"type": "manhattan_f1", "value": 77.8686401480111}, {"type": "manhattan_precision", "value": 74.95903683123174}, {"type": "manhattan_recall", "value": 81.01324299353249}, {"type": "max_accuracy", "value": 88.84425815966158}, {"type": "max_ap", "value": 85.51101933260743}, {"type": "max_f1", "value": 77.94445269567801}]}]}]} | Alibaba-NLP/gte-base-en-v1.5 | null | [
"transformers",
"onnx",
"safetensors",
"new",
"feature-extraction",
"sentence-transformers",
"gte",
"mteb",
"transformers.js",
"sentence-similarity",
"custom_code",
"en",
"arxiv:2308.03281",
"license:apache-2.0",
"model-index",
"region:us"
] | null | 2024-04-20T02:53:42+00:00 | [
"2308.03281"
] | [
"en"
] | TAGS
#transformers #onnx #safetensors #new #feature-extraction #sentence-transformers #gte #mteb #transformers.js #sentence-similarity #custom_code #en #arxiv-2308.03281 #license-apache-2.0 #model-index #region-us
| gte-base-en-v1.5
================
We introduce 'gte-v1.5' series, upgraded 'gte' embeddings that support the context length of up to 8192, while further enhancing model performance.
The models are built upon the 'transformer++' encoder backbone (BERT + RoPE + GLU).
The 'gte-v1.5' series achieve state-of-the-art scores on the MTEB benchmark within the same model size category and prodvide competitive on the LoCo long-context retrieval tests (refer to Evaluation).
We also present the 'gte-Qwen1.5-7B-instruct',
a SOTA instruction-tuned multi-lingual embedding model that ranked 2nd in MTEB and 1st in C-MTEB.
* Developed by: Institute for Intelligent Computing, Alibaba Group
* Model type: Text Embeddings
* Paper: Coming soon.
### Model list
How to Get Started with the Model
---------------------------------
Use the code below to get started with the model.
It is recommended to install xformers and enable unpadding for acceleration, refer to enable-unpadding-and-xformers.
Use with 'sentence-transformers':
Use with 'URL':
Training Details
----------------
### Training Data
* Masked language modeling (MLM): 'c4-en'
* Weak-supervised contrastive (WSC) pre-training: GTE pre-training data
* Supervised contrastive fine-tuning: GTE fine-tuning data
### Training Procedure
To enable the backbone model to support a context length of 8192, we adopted a multi-stage training strategy.
The model first undergoes preliminary MLM pre-training on shorter lengths.
And then, we resample the data, reducing the proportion of short texts, and continue the MLM pre-training.
The entire training process is as follows:
* MLM-2048: lr 5e-4, mlm\_probability 0.3, batch\_size 4096, num\_steps 70000, rope\_base 10000
* MLM-8192: lr 5e-5, mlm\_probability 0.3, batch\_size 1024, num\_steps 20000, rope\_base 500000
* WSC: max\_len 512, lr 2e-4, batch\_size 32768, num\_steps 100000
* Fine-tuning: TODO
Evaluation
----------
### MTEB
The results of other models are retrieved from MTEB leaderboard.
The gte evaluation setting: 'mteb==1.2.0, fp16 auto mix precision, max\_length=8192', and set ntk scaling factor to 2 (equivalent to rope\_base \* 2).
### LoCo
If you find our paper or models helpful, please consider citing them as follows:
| [
"### Model list\n\n\n\nHow to Get Started with the Model\n---------------------------------\n\n\nUse the code below to get started with the model.\n\n\nIt is recommended to install xformers and enable unpadding for acceleration, refer to enable-unpadding-and-xformers.\n\n\nUse with 'sentence-transformers':\n\n\nUse with 'URL':\n\n\nTraining Details\n----------------",
"### Training Data\n\n\n* Masked language modeling (MLM): 'c4-en'\n* Weak-supervised contrastive (WSC) pre-training: GTE pre-training data\n* Supervised contrastive fine-tuning: GTE fine-tuning data",
"### Training Procedure\n\n\nTo enable the backbone model to support a context length of 8192, we adopted a multi-stage training strategy.\nThe model first undergoes preliminary MLM pre-training on shorter lengths.\nAnd then, we resample the data, reducing the proportion of short texts, and continue the MLM pre-training.\n\n\nThe entire training process is as follows:\n\n\n* MLM-2048: lr 5e-4, mlm\\_probability 0.3, batch\\_size 4096, num\\_steps 70000, rope\\_base 10000\n* MLM-8192: lr 5e-5, mlm\\_probability 0.3, batch\\_size 1024, num\\_steps 20000, rope\\_base 500000\n* WSC: max\\_len 512, lr 2e-4, batch\\_size 32768, num\\_steps 100000\n* Fine-tuning: TODO\n\n\nEvaluation\n----------",
"### MTEB\n\n\nThe results of other models are retrieved from MTEB leaderboard.\n\n\nThe gte evaluation setting: 'mteb==1.2.0, fp16 auto mix precision, max\\_length=8192', and set ntk scaling factor to 2 (equivalent to rope\\_base \\* 2).",
"### LoCo\n\n\n\nIf you find our paper or models helpful, please consider citing them as follows:"
] | [
"TAGS\n#transformers #onnx #safetensors #new #feature-extraction #sentence-transformers #gte #mteb #transformers.js #sentence-similarity #custom_code #en #arxiv-2308.03281 #license-apache-2.0 #model-index #region-us \n",
"### Model list\n\n\n\nHow to Get Started with the Model\n---------------------------------\n\n\nUse the code below to get started with the model.\n\n\nIt is recommended to install xformers and enable unpadding for acceleration, refer to enable-unpadding-and-xformers.\n\n\nUse with 'sentence-transformers':\n\n\nUse with 'URL':\n\n\nTraining Details\n----------------",
"### Training Data\n\n\n* Masked language modeling (MLM): 'c4-en'\n* Weak-supervised contrastive (WSC) pre-training: GTE pre-training data\n* Supervised contrastive fine-tuning: GTE fine-tuning data",
"### Training Procedure\n\n\nTo enable the backbone model to support a context length of 8192, we adopted a multi-stage training strategy.\nThe model first undergoes preliminary MLM pre-training on shorter lengths.\nAnd then, we resample the data, reducing the proportion of short texts, and continue the MLM pre-training.\n\n\nThe entire training process is as follows:\n\n\n* MLM-2048: lr 5e-4, mlm\\_probability 0.3, batch\\_size 4096, num\\_steps 70000, rope\\_base 10000\n* MLM-8192: lr 5e-5, mlm\\_probability 0.3, batch\\_size 1024, num\\_steps 20000, rope\\_base 500000\n* WSC: max\\_len 512, lr 2e-4, batch\\_size 32768, num\\_steps 100000\n* Fine-tuning: TODO\n\n\nEvaluation\n----------",
"### MTEB\n\n\nThe results of other models are retrieved from MTEB leaderboard.\n\n\nThe gte evaluation setting: 'mteb==1.2.0, fp16 auto mix precision, max\\_length=8192', and set ntk scaling factor to 2 (equivalent to rope\\_base \\* 2).",
"### LoCo\n\n\n\nIf you find our paper or models helpful, please consider citing them as follows:"
] |
null | transformers |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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### Results
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<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | Giorgoss/watch-ft | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T02:54:10+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
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## Evaluation
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[optional]
BibTeX:
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## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
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"## Training Details",
"### Training Data",
"### Training Procedure",
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"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
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"### Compute Infrastructure",
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"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
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"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
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"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
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"## Glossary [optional]",
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"## Model Card Authors [optional]",
"## Model Card Contact"
] |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# watch-assistant-ft
This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.2-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6518
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9152 | 0.86 | 3 | 0.6749 |
| 0.5722 | 2.0 | 7 | 0.6254 |
| 0.6954 | 2.86 | 10 | 0.6108 |
| 0.4812 | 4.0 | 14 | 0.5827 |
| 0.5823 | 4.86 | 17 | 0.5844 |
| 0.403 | 6.0 | 21 | 0.5804 |
| 0.4876 | 6.86 | 24 | 0.5790 |
| 0.3346 | 8.0 | 28 | 0.6032 |
| 0.4191 | 8.86 | 31 | 0.6084 |
| 0.295 | 10.0 | 35 | 0.6256 |
| 0.3705 | 10.86 | 38 | 0.6355 |
| 0.266 | 12.0 | 42 | 0.6599 |
| 0.2982 | 12.86 | 45 | 0.6518 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2 | {"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "TheBloke/Mistral-7B-Instruct-v0.2-GPTQ", "model-index": [{"name": "watch-assistant-ft", "results": []}]} | Giorgoss/watch-assistant-ft | null | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ",
"license:apache-2.0",
"region:us"
] | null | 2024-04-20T02:54:17+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #generated_from_trainer #base_model-TheBloke/Mistral-7B-Instruct-v0.2-GPTQ #license-apache-2.0 #region-us
| watch-assistant-ft
==================
This model is a fine-tuned version of TheBloke/Mistral-7B-Instruct-v0.2-GPTQ on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6518
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0002
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 2
* num\_epochs: 15
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* PEFT 0.10.0
* Transformers 4.38.2
* Pytorch 2.1.0+cu121
* Datasets 2.19.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2\n* num\\_epochs: 15\n* mixed\\_precision\\_training: Native AMP",
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] |
sentence-similarity | transformers |
<!-- **English** | [中文](./README_zh.md) -->
# gte-large-en-v1.5
We introduce `gte-v1.5` series, upgraded `gte` embeddings that support the context length of up to **8192**, while further enhancing model performance.
The models are built upon the `transformer++` encoder [backbone](https://huggingface.co/Alibaba-NLP/new-impl) (BERT + RoPE + GLU).
The `gte-v1.5` series achieve state-of-the-art scores on the MTEB benchmark within the same model size category and prodvide competitive on the LoCo long-context retrieval tests (refer to [Evaluation](#evaluation)).
We also present the [`gte-Qwen1.5-7B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct),
a SOTA instruction-tuned multi-lingual embedding model that ranked 2nd in MTEB and 1st in C-MTEB.
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Institute for Intelligent Computing, Alibaba Group
- **Model type:** Text Embeddings
- **Paper:** Coming soon.
<!-- - **Demo [optional]:** [More Information Needed] -->
### Model list
| Models | Language | Model Size | Max Seq. Length | Dimension | MTEB-en | LoCo |
|:-----: | :-----: |:-----: |:-----: |:-----: | :-----: | :-----: |
|[`gte-Qwen1.5-7B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct)| Multiple | 7720 | 32768 | 4096 | 67.34 | 87.57 |
|[`gte-large-en-v1.5`](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | English | 434 | 8192 | 1024 | 65.39 | 86.71 |
|[`gte-base-en-v1.5`](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | English | 137 | 8192 | 768 | 64.11 | 87.44 |
## How to Get Started with the Model
Use the code below to get started with the model.
```python
# Requires transformers>=4.36.0
import torch.nn.functional as F
from transformers import AutoModel, AutoTokenizer
input_texts = [
"what is the capital of China?",
"how to implement quick sort in python?",
"Beijing",
"sorting algorithms"
]
model_path = 'Alibaba-NLP/gte-large-en-v1.5'
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModel.from_pretrained(model_path, trust_remote_code=True)
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=8192, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = outputs.last_hidden_state[:, 0]
# (Optionally) normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:1] @ embeddings[1:].T) * 100
print(scores.tolist())
```
**It is recommended to install xformers and enable unpadding for acceleration, refer to [enable-unpadding-and-xformers](https://huggingface.co/Alibaba-NLP/new-impl#recommendation-enable-unpadding-and-acceleration-with-xformers).**
Use with sentence-transformers:
```python
# Requires sentence_transformers>=2.7.0
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
sentences = ['That is a happy person', 'That is a very happy person']
model = SentenceTransformer('Alibaba-NLP/gte-large-en-v1.5', trust_remote_code=True)
embeddings = model.encode(sentences)
print(cos_sim(embeddings[0], embeddings[1]))
```
Use with `transformers.js`:
```js
// npm i @xenova/transformers
import { pipeline, dot } from '@xenova/transformers';
// Create feature extraction pipeline
const extractor = await pipeline('feature-extraction', 'Alibaba-NLP/gte-large-en-v1.5', {
quantized: false, // Comment out this line to use the quantized version
});
// Generate sentence embeddings
const sentences = [
"what is the capital of China?",
"how to implement quick sort in python?",
"Beijing",
"sorting algorithms"
]
const output = await extractor(sentences, { normalize: true, pooling: 'cls' });
// Compute similarity scores
const [source_embeddings, ...document_embeddings ] = output.tolist();
const similarities = document_embeddings.map(x => 100 * dot(source_embeddings, x));
console.log(similarities); // [41.86354093370361, 77.07076371259589, 37.02981979677899]
```
## Training Details
### Training Data
- Masked language modeling (MLM): `c4-en`
- Weak-supervised contrastive (WSC) pre-training: [GTE](https://arxiv.org/pdf/2308.03281.pdf) pre-training data
- Supervised contrastive fine-tuning: GTE(https://arxiv.org/pdf/2308.03281.pdf) fine-tuning data
### Training Procedure
To enable the backbone model to support a context length of 8192, we adopted a multi-stage training strategy.
The model first undergoes preliminary MLM pre-training on shorter lengths.
And then, we resample the data, reducing the proportion of short texts, and continue the MLM pre-training.
The entire training process is as follows:
- MLM-512: lr 2e-4, mlm_probability 0.3, batch_size 4096, num_steps 300000, rope_base 10000
- MLM-2048: lr 5e-5, mlm_probability 0.3, batch_size 4096, num_steps 30000, rope_base 10000
- MLM-8192: lr 5e-5, mlm_probability 0.3, batch_size 1024, num_steps 30000, rope_base 160000
- WSC: max_len 512, lr 5e-5, batch_size 28672, num_steps 100000
- Fine-tuning: TODO
## Evaluation
### MTEB
The results of other models are retrieved from [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard).
The gte evaluation setting: `mteb==1.2.0, fp16 auto mix precision, max_length=8192`, and set ntk scaling factor to 2 (equivalent to rope_base * 2).
| Model Name | Param Size (M) | Dimension | Sequence Length | Average (56) | Class. (12) | Clust. (11) | Pair Class. (3) | Reran. (4) | Retr. (15) | STS (10) | Summ. (1) |
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| [**gte-large-en-v1.5**](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 409 | 1024 | 8192 | **65.39** | 77.75 | 47.95 | 84.63 | 58.50 | 57.91 | 81.43 | 30.91 |
| [mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) | 335 | 1024 | 512 | 64.68 | 75.64 | 46.71 | 87.2 | 60.11 | 54.39 | 85 | 32.71 |
| [multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) | 560 | 1024 | 514 | 64.41 | 77.56 | 47.1 | 86.19 | 58.58 | 52.47 | 84.78 | 30.39 |
| [bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5)| 335 | 1024 | 512 | 64.23 | 75.97 | 46.08 | 87.12 | 60.03 | 54.29 | 83.11 | 31.61 |
| [**gte-base-en-v1.5**](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | 137 | 768 | 8192 | **64.11** | 77.17 | 46.82 | 85.33 | 57.66 | 54.09 | 81.97 | 31.17 |
| [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5)| 109 | 768 | 512 | 63.55 | 75.53 | 45.77 | 86.55 | 58.86 | 53.25 | 82.4 | 31.07 |
### LoCo
| Model Name | Dimension | Sequence Length | Average (5) | QsmsumRetrieval | SummScreenRetrieval | QasperAbastractRetrieval | QasperTitleRetrieval | GovReportRetrieval |
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| [gte-qwen1.5-7b](https://huggingface.co/Alibaba-NLP/gte-qwen1.5-7b) | 4096 | 32768 | 87.57 | 49.37 | 93.10 | 99.67 | 97.54 | 98.21 |
| [gte-large-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-v1.5) |1024 | 8192 | 86.71 | 44.55 | 92.61 | 99.82 | 97.81 | 98.74 |
| [gte-base-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-v1.5) | 768 | 8192 | 87.44 | 49.91 | 91.78 | 99.82 | 97.13 | 98.58 |
## Citation
If you find our paper or models helpful, please consider citing them as follows:
```
@article{li2023towards,
title={Towards general text embeddings with multi-stage contrastive learning},
author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan},
journal={arXiv preprint arXiv:2308.03281},
year={2023}
}
``` | {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["sentence-transformers", "gte", "mteb", "transformers.js", "sentence-similarity"], "datasets": ["allenai/c4"], "model-index": [{"name": "gte-large-en-v1.5", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (en)", "type": "mteb/amazon_counterfactual", "config": "en", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "accuracy", "value": 73.01492537313432}, {"type": "ap", "value": 35.05341696659522}, {"type": "f1", "value": 66.71270310883853}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonPolarityClassification", "type": "mteb/amazon_polarity", "config": "default", "split": "test", "revision": "e2d317d38cd51312af73b3d32a06d1a08b442046"}, "metrics": [{"type": "accuracy", "value": 93.97189999999999}, {"type": "ap", "value": 90.5952493948908}, {"type": "f1", "value": 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0.23600000000000002}, {"type": "recall_at_10", "value": 2.117}, {"type": "recall_at_100", "value": 14.985000000000001}, {"type": "recall_at_1000", "value": 51.107}, {"type": "recall_at_3", "value": 0.688}, {"type": "recall_at_5", "value": 1.1039999999999999}]}, {"task": {"type": "Retrieval"}, "dataset": {"name": "MTEB Touche2020", "type": "mteb/touche2020", "config": "default", "split": "test", "revision": "a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f"}, "metrics": [{"type": "map_at_1", "value": 2.3040000000000003}, {"type": "map_at_10", "value": 9.025}, {"type": "map_at_100", "value": 15.312999999999999}, {"type": "map_at_1000", "value": 16.954}, {"type": "map_at_3", "value": 4.981}, {"type": "map_at_5", "value": 6.32}, {"type": "mrr_at_1", "value": 24.490000000000002}, {"type": "mrr_at_10", "value": 39.835}, {"type": "mrr_at_100", "value": 40.8}, {"type": "mrr_at_1000", "value": 40.8}, {"type": "mrr_at_3", "value": 35.034}, {"type": "mrr_at_5", "value": 37.687}, {"type": "ndcg_at_1", "value": 22.448999999999998}, {"type": "ndcg_at_10", "value": 22.545}, {"type": "ndcg_at_100", "value": 35.931999999999995}, {"type": "ndcg_at_1000", "value": 47.665}, {"type": "ndcg_at_3", "value": 23.311}, {"type": "ndcg_at_5", "value": 22.421}, {"type": "precision_at_1", "value": 24.490000000000002}, {"type": "precision_at_10", "value": 20.408}, {"type": "precision_at_100", "value": 7.815999999999999}, {"type": "precision_at_1000", "value": 1.553}, {"type": "precision_at_3", "value": 25.169999999999998}, {"type": "precision_at_5", "value": 23.265}, {"type": "recall_at_1", "value": 2.3040000000000003}, {"type": "recall_at_10", "value": 15.693999999999999}, {"type": "recall_at_100", "value": 48.917}, {"type": "recall_at_1000", "value": 84.964}, {"type": "recall_at_3", "value": 6.026}, {"type": "recall_at_5", "value": 9.066}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB ToxicConversationsClassification", "type": "mteb/toxic_conversations_50k", "config": "default", "split": "test", "revision": "d7c0de2777da35d6aae2200a62c6e0e5af397c4c"}, "metrics": [{"type": "accuracy", "value": 82.6074}, {"type": "ap", "value": 23.187467098602013}, {"type": "f1", "value": 65.36829506379657}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB TweetSentimentExtractionClassification", "type": "mteb/tweet_sentiment_extraction", "config": "default", "split": "test", "revision": "d604517c81ca91fe16a244d1248fc021f9ecee7a"}, "metrics": [{"type": "accuracy", "value": 63.16355404640635}, {"type": "f1", "value": 63.534725639863346}]}, {"task": {"type": "Clustering"}, "dataset": {"name": "MTEB TwentyNewsgroupsClustering", "type": "mteb/twentynewsgroups-clustering", "config": "default", "split": "test", "revision": "6125ec4e24fa026cec8a478383ee943acfbd5449"}, "metrics": [{"type": "v_measure", "value": 50.91004094411276}]}, {"task": {"type": "PairClassification"}, "dataset": {"name": "MTEB TwitterSemEval2015", "type": "mteb/twittersemeval2015-pairclassification", "config": "default", "split": "test", "revision": "70970daeab8776df92f5ea462b6173c0b46fd2d1"}, "metrics": [{"type": "cos_sim_accuracy", "value": 86.55301901412649}, {"type": "cos_sim_ap", "value": 75.25312618556728}, {"type": "cos_sim_f1", "value": 68.76561719140429}, {"type": "cos_sim_precision", "value": 65.3061224489796}, {"type": "cos_sim_recall", "value": 72.61213720316623}, {"type": "dot_accuracy", "value": 86.29671574178936}, {"type": "dot_ap", "value": 75.11910195501207}, {"type": "dot_f1", "value": 68.44048376830045}, {"type": "dot_precision", "value": 66.12546125461255}, {"type": "dot_recall", "value": 70.92348284960423}, {"type": "euclidean_accuracy", "value": 86.5828217202122}, {"type": "euclidean_ap", "value": 75.22986344900924}, {"type": "euclidean_f1", "value": 68.81267797449549}, {"type": "euclidean_precision", "value": 64.8238861674831}, {"type": "euclidean_recall", "value": 73.3245382585752}, {"type": "manhattan_accuracy", "value": 86.61262442629791}, {"type": "manhattan_ap", "value": 75.24401608557328}, {"type": "manhattan_f1", "value": 68.80473982483257}, {"type": "manhattan_precision", "value": 67.21187720181177}, {"type": "manhattan_recall", "value": 70.47493403693932}, {"type": "max_accuracy", "value": 86.61262442629791}, {"type": "max_ap", "value": 75.25312618556728}, {"type": "max_f1", "value": 68.81267797449549}]}, {"task": {"type": "PairClassification"}, "dataset": {"name": "MTEB TwitterURLCorpus", "type": "mteb/twitterurlcorpus-pairclassification", "config": "default", "split": "test", "revision": "8b6510b0b1fa4e4c4f879467980e9be563ec1cdf"}, "metrics": [{"type": "cos_sim_accuracy", "value": 88.10688089416696}, {"type": "cos_sim_ap", "value": 84.17862178779863}, {"type": "cos_sim_f1", "value": 76.17305208781748}, {"type": "cos_sim_precision", "value": 71.31246641590543}, {"type": "cos_sim_recall", "value": 81.74468740375731}, {"type": "dot_accuracy", "value": 88.1844995536927}, {"type": "dot_ap", "value": 84.33816725235876}, {"type": "dot_f1", "value": 76.43554032918746}, {"type": "dot_precision", "value": 74.01557767200346}, {"type": "dot_recall", "value": 79.0190945488143}, {"type": "euclidean_accuracy", "value": 88.07001203089223}, {"type": "euclidean_ap", "value": 84.12267000814985}, {"type": "euclidean_f1", "value": 76.12232600180778}, {"type": "euclidean_precision", "value": 74.50604541433205}, {"type": "euclidean_recall", "value": 77.81028641823221}, {"type": "manhattan_accuracy", "value": 88.06419063142779}, {"type": "manhattan_ap", "value": 84.11648917164187}, {"type": "manhattan_f1", "value": 76.20579953925474}, {"type": "manhattan_precision", "value": 72.56772755762935}, {"type": "manhattan_recall", "value": 80.22790267939637}, {"type": "max_accuracy", "value": 88.1844995536927}, {"type": "max_ap", "value": 84.33816725235876}, {"type": "max_f1", "value": 76.43554032918746}]}]}]} | Alibaba-NLP/gte-large-en-v1.5 | null | [
"transformers",
"onnx",
"safetensors",
"new",
"feature-extraction",
"sentence-transformers",
"gte",
"mteb",
"transformers.js",
"sentence-similarity",
"custom_code",
"en",
"dataset:allenai/c4",
"arxiv:2308.03281",
"license:apache-2.0",
"model-index",
"region:us"
] | null | 2024-04-20T02:54:30+00:00 | [
"2308.03281"
] | [
"en"
] | TAGS
#transformers #onnx #safetensors #new #feature-extraction #sentence-transformers #gte #mteb #transformers.js #sentence-similarity #custom_code #en #dataset-allenai/c4 #arxiv-2308.03281 #license-apache-2.0 #model-index #region-us
| gte-large-en-v1.5
=================
We introduce 'gte-v1.5' series, upgraded 'gte' embeddings that support the context length of up to 8192, while further enhancing model performance.
The models are built upon the 'transformer++' encoder backbone (BERT + RoPE + GLU).
The 'gte-v1.5' series achieve state-of-the-art scores on the MTEB benchmark within the same model size category and prodvide competitive on the LoCo long-context retrieval tests (refer to Evaluation).
We also present the 'gte-Qwen1.5-7B-instruct',
a SOTA instruction-tuned multi-lingual embedding model that ranked 2nd in MTEB and 1st in C-MTEB.
* Developed by: Institute for Intelligent Computing, Alibaba Group
* Model type: Text Embeddings
* Paper: Coming soon.
### Model list
How to Get Started with the Model
---------------------------------
Use the code below to get started with the model.
It is recommended to install xformers and enable unpadding for acceleration, refer to enable-unpadding-and-xformers.
Use with sentence-transformers:
Use with 'URL':
Training Details
----------------
### Training Data
* Masked language modeling (MLM): 'c4-en'
* Weak-supervised contrastive (WSC) pre-training: GTE pre-training data
* Supervised contrastive fine-tuning: GTE(URL fine-tuning data
### Training Procedure
To enable the backbone model to support a context length of 8192, we adopted a multi-stage training strategy.
The model first undergoes preliminary MLM pre-training on shorter lengths.
And then, we resample the data, reducing the proportion of short texts, and continue the MLM pre-training.
The entire training process is as follows:
* MLM-512: lr 2e-4, mlm\_probability 0.3, batch\_size 4096, num\_steps 300000, rope\_base 10000
* MLM-2048: lr 5e-5, mlm\_probability 0.3, batch\_size 4096, num\_steps 30000, rope\_base 10000
* MLM-8192: lr 5e-5, mlm\_probability 0.3, batch\_size 1024, num\_steps 30000, rope\_base 160000
* WSC: max\_len 512, lr 5e-5, batch\_size 28672, num\_steps 100000
* Fine-tuning: TODO
Evaluation
----------
### MTEB
The results of other models are retrieved from MTEB leaderboard.
The gte evaluation setting: 'mteb==1.2.0, fp16 auto mix precision, max\_length=8192', and set ntk scaling factor to 2 (equivalent to rope\_base \* 2).
### LoCo
If you find our paper or models helpful, please consider citing them as follows:
| [
"### Model list\n\n\n\nHow to Get Started with the Model\n---------------------------------\n\n\nUse the code below to get started with the model.\n\n\nIt is recommended to install xformers and enable unpadding for acceleration, refer to enable-unpadding-and-xformers.\n\n\nUse with sentence-transformers:\n\n\nUse with 'URL':\n\n\nTraining Details\n----------------",
"### Training Data\n\n\n* Masked language modeling (MLM): 'c4-en'\n* Weak-supervised contrastive (WSC) pre-training: GTE pre-training data\n* Supervised contrastive fine-tuning: GTE(URL fine-tuning data",
"### Training Procedure\n\n\nTo enable the backbone model to support a context length of 8192, we adopted a multi-stage training strategy.\nThe model first undergoes preliminary MLM pre-training on shorter lengths.\nAnd then, we resample the data, reducing the proportion of short texts, and continue the MLM pre-training.\n\n\nThe entire training process is as follows:\n\n\n* MLM-512: lr 2e-4, mlm\\_probability 0.3, batch\\_size 4096, num\\_steps 300000, rope\\_base 10000\n* MLM-2048: lr 5e-5, mlm\\_probability 0.3, batch\\_size 4096, num\\_steps 30000, rope\\_base 10000\n* MLM-8192: lr 5e-5, mlm\\_probability 0.3, batch\\_size 1024, num\\_steps 30000, rope\\_base 160000\n* WSC: max\\_len 512, lr 5e-5, batch\\_size 28672, num\\_steps 100000\n* Fine-tuning: TODO\n\n\nEvaluation\n----------",
"### MTEB\n\n\nThe results of other models are retrieved from MTEB leaderboard.\n\n\nThe gte evaluation setting: 'mteb==1.2.0, fp16 auto mix precision, max\\_length=8192', and set ntk scaling factor to 2 (equivalent to rope\\_base \\* 2).",
"### LoCo\n\n\n\nIf you find our paper or models helpful, please consider citing them as follows:"
] | [
"TAGS\n#transformers #onnx #safetensors #new #feature-extraction #sentence-transformers #gte #mteb #transformers.js #sentence-similarity #custom_code #en #dataset-allenai/c4 #arxiv-2308.03281 #license-apache-2.0 #model-index #region-us \n",
"### Model list\n\n\n\nHow to Get Started with the Model\n---------------------------------\n\n\nUse the code below to get started with the model.\n\n\nIt is recommended to install xformers and enable unpadding for acceleration, refer to enable-unpadding-and-xformers.\n\n\nUse with sentence-transformers:\n\n\nUse with 'URL':\n\n\nTraining Details\n----------------",
"### Training Data\n\n\n* Masked language modeling (MLM): 'c4-en'\n* Weak-supervised contrastive (WSC) pre-training: GTE pre-training data\n* Supervised contrastive fine-tuning: GTE(URL fine-tuning data",
"### Training Procedure\n\n\nTo enable the backbone model to support a context length of 8192, we adopted a multi-stage training strategy.\nThe model first undergoes preliminary MLM pre-training on shorter lengths.\nAnd then, we resample the data, reducing the proportion of short texts, and continue the MLM pre-training.\n\n\nThe entire training process is as follows:\n\n\n* MLM-512: lr 2e-4, mlm\\_probability 0.3, batch\\_size 4096, num\\_steps 300000, rope\\_base 10000\n* MLM-2048: lr 5e-5, mlm\\_probability 0.3, batch\\_size 4096, num\\_steps 30000, rope\\_base 10000\n* MLM-8192: lr 5e-5, mlm\\_probability 0.3, batch\\_size 1024, num\\_steps 30000, rope\\_base 160000\n* WSC: max\\_len 512, lr 5e-5, batch\\_size 28672, num\\_steps 100000\n* Fine-tuning: TODO\n\n\nEvaluation\n----------",
"### MTEB\n\n\nThe results of other models are retrieved from MTEB leaderboard.\n\n\nThe gte evaluation setting: 'mteb==1.2.0, fp16 auto mix precision, max\\_length=8192', and set ntk scaling factor to 2 (equivalent to rope\\_base \\* 2).",
"### LoCo\n\n\n\nIf you find our paper or models helpful, please consider citing them as follows:"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "255.29 +/- 20.94", "name": "mean_reward", "verified": false}]}]}]} | stuvx/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2024-04-20T02:56:13+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] |
text-generation | transformers | 
---
language:
- zh
license: apache-2.0
metrics:
- accuracy
pipeline_tag: text-generation
---
>> It's not a chat model, just using Wizard-LM-Chinese-instruct-evol datesets training with several steps for test the model typical Chinese skill，
>> this is version1, will release version2 for more long context windows and Chat model
>>____________________________
>>Train scenario:
>>2k context
>>datasets:Wizard-LM-Chinese-instruct-evol
>>batchsize:8
>>steps:500
>>epchos:2
>>____________________________________________________
>>How to use?
>>Follow common huggingface-api is enough or using other framework like VLLM, support continue training.
____________________________________________________
>>import transformers
>>import torch
>>model_id = "BoyangZ/llama3-chinese"
>>pipeline = transformers.pipeline(
>> "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto"
>> )
>>pipeline("川普和拜登谁能赢得大选??")
>> [{'generated_text': '川普和拜登谁能赢得大选?](https://www.voachinese.com'}]
>>
>> import torch
>> from transformers import AutoModelForCausalLM, AutoTokenizer
>> torch.set_default_device("cuda")
>> model = AutoModelForCausalLM.from_pretrained("BoyangZ/llama3-chinese", torch_dtype="auto", trust_remote_code=True)
>> tokenizer = AutoTokenizer.from_pretrained("BoyangZ/llama3-chinese", trust_remote_code=True)
>> inputs = tokenizer(
>> "川普和拜登一起竞选，美国总统,谁获胜的几率大,分析一下?",
>> return_tensors="pt",
>> return_attention_mask=False
>> )
>> outputs = model.generate(**inputs, max_length=200)
>> text = tokenizer.batch_decode(outputs)[0]
>> print(text)
>>Wechat:18618377979, Gmail:[email protected] | {} | BoyangZ/llama3_chinese_basemode_ftv1 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T02:56:44+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| !image/png
---
language:
- zh
license: apache-2.0
metrics:
- accuracy
pipeline_tag: text-generation
---
>> It's not a chat model, just using Wizard-LM-Chinese-instruct-evol datesets training with several steps for test the model typical Chinese skill，
>> this is version1, will release version2 for more long context windows and Chat model
>>____________________________
>>Train scenario:
>>2k context
>>datasets:Wizard-LM-Chinese-instruct-evol
>>batchsize:8
>>steps:500
>>epchos:2
>>____________________________________________________
>>How to use?
>>Follow common huggingface-api is enough or using other framework like VLLM, support continue training.
____________________________________________________
>>import transformers
>>import torch
>>model_id = "BoyangZ/llama3-chinese"
>>pipeline = transformers.pipeline(
>> "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto"
>> )
>>pipeline("川普和拜登谁能赢得大选??")
>> {'generated_text': '川普和拜登谁能赢得大选?
>> model = AutoModelForCausalLM.from_pretrained("BoyangZ/llama3-chinese", torch_dtype="auto", trust_remote_code=True)
>> tokenizer = AutoTokenizer.from_pretrained("BoyangZ/llama3-chinese", trust_remote_code=True)
>> inputs = tokenizer(
>> "川普和拜登一起竞选，美国总统,谁获胜的几率大,分析一下?",
>> return_tensors="pt",
>> return_attention_mask=False
>> )
>> outputs = model.generate(inputs, max_length=200)
>> text = tokenizer.batch_decode(outputs)[0]
>> print(text)
!image/png
!image/png
>>Wechat:18618377979, Gmail:zhouboyang1983@URL | [] | [
"TAGS\n#transformers #safetensors #llama #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | adapter-transformers |
# Adapter `BigTMiami/amz_10k_seq_bn_help_tapt_pretrain_adapter` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/amazon_10k_helpfulness_dataset_condensed](https://huggingface.co/datasets/BigTMiami/amazon_10k_helpfulness_dataset_condensed/) dataset and includes a prediction head for masked lm.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("BigTMiami/amz_10k_seq_bn_help_tapt_pretrain_adapter", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> | {"tags": ["adapter-transformers", "roberta"], "datasets": ["BigTMiami/amazon_10k_helpfulness_dataset_condensed"]} | BigTMiami/amz_10k_seq_bn_help_tapt_pretrain_adapter | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/amazon_10k_helpfulness_dataset_condensed",
"region:us"
] | null | 2024-04-20T03:00:27+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/amazon_10k_helpfulness_dataset_condensed #region-us
|
# Adapter 'BigTMiami/amz_10k_seq_bn_help_tapt_pretrain_adapter' for roberta-base
An adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_10k_helpfulness_dataset_condensed dataset and includes a prediction head for masked lm.
This adapter was created for usage with the Adapters library.
## Usage
First, install 'adapters':
Now, the adapter can be loaded and activated like this:
## Architecture & Training
## Evaluation results
| [
"# Adapter 'BigTMiami/amz_10k_seq_bn_help_tapt_pretrain_adapter' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_10k_helpfulness_dataset_condensed dataset and includes a prediction head for masked lm.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] | [
"TAGS\n#adapter-transformers #roberta #dataset-BigTMiami/amazon_10k_helpfulness_dataset_condensed #region-us \n",
"# Adapter 'BigTMiami/amz_10k_seq_bn_help_tapt_pretrain_adapter' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_10k_helpfulness_dataset_condensed dataset and includes a prediction head for masked lm.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
text-generation | transformers |
# Model Card for Mistral-7B-Instruct-v0.2
The Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-7B-v0.2.
Mistral-7B-v0.2 has the following changes compared to Mistral-7B-v0.1
- 32k context window (vs 8k context in v0.1)
- Rope-theta = 1e6
- No Sliding-Window Attention
For full details of this model please read our [paper](https://arxiv.org/abs/2310.06825) and [release blog post](https://mistral.ai/news/la-plateforme/).
## Instruction format
In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
E.g.
```
text = "<s>[INST] What is your favourite condiment? [/INST]"
"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
"[INST] Do you have mayonnaise recipes? [/INST]"
```
This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("vicky4s4s/mistral-v.2")
tokenizer = AutoTokenizer.from_pretrained("vicky4s4s/mistral-v.2")
messages = [
{"role": "user", "content": "What is your favourite condiment?"},
{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
## Troubleshooting
- If you see the following error:
```
Traceback (most recent call last):
File "", line 1, in
File "/transformers/models/auto/auto_factory.py", line 482, in from_pretrained
config, kwargs = AutoConfig.from_pretrained(
File "/transformers/models/auto/configuration_auto.py", line 1022, in from_pretrained
config_class = CONFIG_MAPPING[config_dict["model_type"]]
File "/transformers/models/auto/configuration_auto.py", line 723, in getitem
raise KeyError(key)
KeyError: 'mistral'
```
Installing transformers from source should solve the issue
pip install git+https://github.com/huggingface/transformers
This should not be required after transformers-v4.33.4.
## Limitations
The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance.
It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to
make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
## The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed. | {"license": "apache-2.0", "tags": ["finetuned"], "pipeline_tag": "text-generation", "inference": true, "widget": [{"messages": [{"role": "user", "content": "What is your favorite condiment?"}]}]} | vicky4s4s/mistral-v.2 | null | [
"transformers",
"pytorch",
"safetensors",
"mistral",
"text-generation",
"finetuned",
"conversational",
"arxiv:2310.06825",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T03:06:00+00:00 | [
"2310.06825"
] | [] | TAGS
#transformers #pytorch #safetensors #mistral #text-generation #finetuned #conversational #arxiv-2310.06825 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Mistral-7B-Instruct-v0.2
The Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-7B-v0.2.
Mistral-7B-v0.2 has the following changes compared to Mistral-7B-v0.1
- 32k context window (vs 8k context in v0.1)
- Rope-theta = 1e6
- No Sliding-Window Attention
For full details of this model please read our paper and release blog post.
## Instruction format
In order to leverage instruction fine-tuning, your prompt should be surrounded by '[INST]' and '[/INST]' tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
E.g.
This format is available as a chat template via the 'apply_chat_template()' method:
## Troubleshooting
- If you see the following error:
Installing transformers from source should solve the issue
pip install git+URL
This should not be required after transformers-v4.33.4.
## Limitations
The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance.
It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to
make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
## The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed. | [
"# Model Card for Mistral-7B-Instruct-v0.2\n\nThe Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-7B-v0.2.\n\nMistral-7B-v0.2 has the following changes compared to Mistral-7B-v0.1\n- 32k context window (vs 8k context in v0.1)\n- Rope-theta = 1e6\n- No Sliding-Window Attention\n\nFor full details of this model please read our paper and release blog post.",
"## Instruction format\n\nIn order to leverage instruction fine-tuning, your prompt should be surrounded by '[INST]' and '[/INST]' tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.\n\nE.g.\n\n\nThis format is available as a chat template via the 'apply_chat_template()' method:",
"## Troubleshooting\n- If you see the following error:\n\n\nInstalling transformers from source should solve the issue\npip install git+URL\n\nThis should not be required after transformers-v4.33.4.",
"## Limitations\n\nThe Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. \nIt does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to\nmake the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.",
"## The Mistral AI Team\n\nAlbert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed."
] | [
"TAGS\n#transformers #pytorch #safetensors #mistral #text-generation #finetuned #conversational #arxiv-2310.06825 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Mistral-7B-Instruct-v0.2\n\nThe Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-7B-v0.2.\n\nMistral-7B-v0.2 has the following changes compared to Mistral-7B-v0.1\n- 32k context window (vs 8k context in v0.1)\n- Rope-theta = 1e6\n- No Sliding-Window Attention\n\nFor full details of this model please read our paper and release blog post.",
"## Instruction format\n\nIn order to leverage instruction fine-tuning, your prompt should be surrounded by '[INST]' and '[/INST]' tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.\n\nE.g.\n\n\nThis format is available as a chat template via the 'apply_chat_template()' method:",
"## Troubleshooting\n- If you see the following error:\n\n\nInstalling transformers from source should solve the issue\npip install git+URL\n\nThis should not be required after transformers-v4.33.4.",
"## Limitations\n\nThe Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. \nIt does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to\nmake the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.",
"## The Mistral AI Team\n\nAlbert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed."
] |
null | adapter-transformers |
# Adapter `BigTMiami/seq_bn_amz_10k_help_class_no_pretrain_1_adapter` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/amazon_10k_helpfulness_dataset](https://huggingface.co/datasets/BigTMiami/amazon_10k_helpfulness_dataset/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("BigTMiami/seq_bn_amz_10k_help_class_no_pretrain_1_adapter", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> | {"tags": ["roberta", "adapter-transformers"], "datasets": ["BigTMiami/amazon_10k_helpfulness_dataset"]} | BigTMiami/seq_bn_amz_10k_help_class_no_pretrain_1_adapter | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/amazon_10k_helpfulness_dataset",
"region:us"
] | null | 2024-04-20T03:07:53+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/amazon_10k_helpfulness_dataset #region-us
|
# Adapter 'BigTMiami/seq_bn_amz_10k_help_class_no_pretrain_1_adapter' for roberta-base
An adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_10k_helpfulness_dataset dataset and includes a prediction head for classification.
This adapter was created for usage with the Adapters library.
## Usage
First, install 'adapters':
Now, the adapter can be loaded and activated like this:
## Architecture & Training
## Evaluation results
| [
"# Adapter 'BigTMiami/seq_bn_amz_10k_help_class_no_pretrain_1_adapter' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_10k_helpfulness_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] | [
"TAGS\n#adapter-transformers #roberta #dataset-BigTMiami/amazon_10k_helpfulness_dataset #region-us \n",
"# Adapter 'BigTMiami/seq_bn_amz_10k_help_class_no_pretrain_1_adapter' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_10k_helpfulness_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | foolunderscore/GlennAI-04.19.2024 | null | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T03:13:18+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #gpt2 #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #gpt2 #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.10.0 | {"library_name": "peft", "base_model": "google/vit-base-patch16-224-in21k"} | pintu5057/vit-base-patch16-224-in21k-finetuned-lora-food101 | null | [
"peft",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"base_model:google/vit-base-patch16-224-in21k",
"region:us"
] | null | 2024-04-20T03:21:18+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #tensorboard #safetensors #arxiv-1910.09700 #base_model-google/vit-base-patch16-224-in21k #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
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### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
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#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
### Framework versions
- PEFT 0.10.0 | [
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] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 0.0_ablation_declr_6iters_iter_4
This model is a fine-tuned version of [ZhangShenao/0.0_ablation_declr_6iters_iter_3](https://huggingface.co/ZhangShenao/0.0_ablation_declr_6iters_iter_3) on the ZhangShenao/0.0_ablation_declr_6iters_dataset dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["ZhangShenao/0.0_ablation_declr_6iters_dataset"], "base_model": "ZhangShenao/0.0_ablation_declr_6iters_iter_3", "model-index": [{"name": "0.0_ablation_declr_6iters_iter_4", "results": []}]} | ZhangShenao/0.0_ablation_declr_6iters_iter_4 | null | [
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"region:us"
] | null | 2024-04-20T03:21:32+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #alignment-handbook #generated_from_trainer #trl #dpo #conversational #dataset-ZhangShenao/0.0_ablation_declr_6iters_dataset #base_model-ZhangShenao/0.0_ablation_declr_6iters_iter_3 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# 0.0_ablation_declr_6iters_iter_4
This model is a fine-tuned version of ZhangShenao/0.0_ablation_declr_6iters_iter_3 on the ZhangShenao/0.0_ablation_declr_6iters_dataset dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
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] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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[More Information Needed]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed] | {"library_name": "transformers", "tags": []} | Grayx/sad_llama_6 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T03:24:00+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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"## Training Details",
"### Training Data",
"### Training Procedure",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
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"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
null | transformers | ## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Replete-AI/Llama-3-11.5B-v2
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Llama-3-11.5B-v2-GGUF/resolve/main/Llama-3-11.5B-v2.Q2_K.gguf) | Q2_K | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-11.5B-v2-GGUF/resolve/main/Llama-3-11.5B-v2.IQ3_XS.gguf) | IQ3_XS | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-11.5B-v2-GGUF/resolve/main/Llama-3-11.5B-v2.Q3_K_S.gguf) | Q3_K_S | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-11.5B-v2-GGUF/resolve/main/Llama-3-11.5B-v2.IQ3_S.gguf) | IQ3_S | 5.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-11.5B-v2-GGUF/resolve/main/Llama-3-11.5B-v2.IQ3_M.gguf) | IQ3_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-11.5B-v2-GGUF/resolve/main/Llama-3-11.5B-v2.Q3_K_M.gguf) | Q3_K_M | 5.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-11.5B-v2-GGUF/resolve/main/Llama-3-11.5B-v2.Q3_K_L.gguf) | Q3_K_L | 6.3 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-11.5B-v2-GGUF/resolve/main/Llama-3-11.5B-v2.IQ4_XS.gguf) | IQ4_XS | 6.5 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-11.5B-v2-GGUF/resolve/main/Llama-3-11.5B-v2.Q4_K_S.gguf) | Q4_K_S | 6.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-11.5B-v2-GGUF/resolve/main/Llama-3-11.5B-v2.Q4_K_M.gguf) | Q4_K_M | 7.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-11.5B-v2-GGUF/resolve/main/Llama-3-11.5B-v2.Q5_K_S.gguf) | Q5_K_S | 8.1 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-11.5B-v2-GGUF/resolve/main/Llama-3-11.5B-v2.Q5_K_M.gguf) | Q5_K_M | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-11.5B-v2-GGUF/resolve/main/Llama-3-11.5B-v2.Q6_K.gguf) | Q6_K | 9.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-11.5B-v2-GGUF/resolve/main/Llama-3-11.5B-v2.Q8_0.gguf) | Q8_0 | 12.3 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "license": "other", "library_name": "transformers", "base_model": "Replete-AI/Llama-3-11.5B-v2", "license_link": "https://llama.meta.com/llama3/license/", "license_name": "llama-3", "quantized_by": "mradermacher"} | mradermacher/Llama-3-11.5B-v2-GGUF | null | [
"transformers",
"gguf",
"en",
"base_model:Replete-AI/Llama-3-11.5B-v2",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T03:26:40+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #en #base_model-Replete-AI/Llama-3-11.5B-v2 #license-other #endpoints_compatible #region-us
| About
-----
static quants of URL
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
Usage
-----
If you are unsure how to use GGUF files, refer to one of TheBloke's
READMEs for
more details, including on how to concatenate multi-part files.
Provided Quants
---------------
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
!URL
And here are Artefact2's thoughts on the matter:
URL
FAQ / Model Request
-------------------
See URL for some answers to
questions you might have and/or if you want some other model quantized.
Thanks
------
I thank my company, nethype GmbH, for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
| [] | [
"TAGS\n#transformers #gguf #en #base_model-Replete-AI/Llama-3-11.5B-v2 #license-other #endpoints_compatible #region-us \n"
] |
translation | transformers.js | ERROR: type should be string, got "\n\nhttps://hf.co/vinai/vinai-translate-vi2en-v2 with ONNX weights to be compatible with Transformers.js.\n\nPlease check out this demo using the model:\n[](https://huggingface.co/spaces/huuquyet/translator-tamagui)\n\n\n# A Vietnamese-English Neural Machine Translation System\n\nOur pre-trained VinAI Translate models are state-of-the-art text translation models for Vietnamese-to-English and English-to-Vietnamese, respectively. The general architecture and experimental results of VinAI Translate can be found in [our paper](https://openreview.net/forum?id=CRg-RaxKnai):\n\n\n @inproceedings{vinaitranslate,\n title = {{A Vietnamese-English Neural Machine Translation System}},\n author = {Thien Hai Nguyen and \n Tuan-Duy H. Nguyen and \n Duy Phung and \n Duy Tran-Cong Nguyen and \n Hieu Minh Tran and \n Manh Luong and \n Tin Duy Vo and \n Hung Hai Bui and \n Dinh Phung and \n Dat Quoc Nguyen},\n booktitle = {Proceedings of the 23rd Annual Conference of the International Speech Communication Association: Show and Tell (INTERSPEECH)},\n year = {2022}\n }\n \nPlease **CITE** our paper whenever the pre-trained models or the system are used to help produce published results or incorporated into other software.\nFor further information or requests, please go to [VinAI Translate's homepage](https://github.com/VinAIResearch/VinAI_Translate)!" | {"language": ["vi", "en"], "license": "wtfpl", "library_name": "transformers.js", "pipeline_tag": "translation"} | huuquyet/vinai-translate-vi2en-v2 | null | [
"transformers.js",
"onnx",
"mbart",
"text2text-generation",
"translation",
"vi",
"en",
"license:wtfpl",
"has_space",
"region:us"
] | null | 2024-04-20T03:28:38+00:00 | [] | [
"vi",
"en"
] | TAGS
#transformers.js #onnx #mbart #text2text-generation #translation #vi #en #license-wtfpl #has_space #region-us
|
URL with ONNX weights to be compatible with URL.
Please check out this demo using the model:
},
year = {2022}
}
Please CITE our paper whenever the pre-trained models or the system are used to help produce published results or incorporated into other software.
For further information or requests, please go to VinAI Translate's homepage! | [
"# A Vietnamese-English Neural Machine Translation System\n\nOur pre-trained VinAI Translate models are state-of-the-art text translation models for Vietnamese-to-English and English-to-Vietnamese, respectively. The general architecture and experimental results of VinAI Translate can be found in our paper:\n\n\n @inproceedings{vinaitranslate,\n title = {{A Vietnamese-English Neural Machine Translation System}},\n author = {Thien Hai Nguyen and \n Tuan-Duy H. Nguyen and \n Duy Phung and \n Duy Tran-Cong Nguyen and \n Hieu Minh Tran and \n Manh Luong and \n Tin Duy Vo and \n Hung Hai Bui and \n Dinh Phung and \n Dat Quoc Nguyen},\n booktitle = {Proceedings of the 23rd Annual Conference of the International Speech Communication Association: Show and Tell (INTERSPEECH)},\n year = {2022}\n }\n \nPlease CITE our paper whenever the pre-trained models or the system are used to help produce published results or incorporated into other software.\nFor further information or requests, please go to VinAI Translate's homepage!"
] | [
"TAGS\n#transformers.js #onnx #mbart #text2text-generation #translation #vi #en #license-wtfpl #has_space #region-us \n",
"# A Vietnamese-English Neural Machine Translation System\n\nOur pre-trained VinAI Translate models are state-of-the-art text translation models for Vietnamese-to-English and English-to-Vietnamese, respectively. The general architecture and experimental results of VinAI Translate can be found in our paper:\n\n\n @inproceedings{vinaitranslate,\n title = {{A Vietnamese-English Neural Machine Translation System}},\n author = {Thien Hai Nguyen and \n Tuan-Duy H. Nguyen and \n Duy Phung and \n Duy Tran-Cong Nguyen and \n Hieu Minh Tran and \n Manh Luong and \n Tin Duy Vo and \n Hung Hai Bui and \n Dinh Phung and \n Dat Quoc Nguyen},\n booktitle = {Proceedings of the 23rd Annual Conference of the International Speech Communication Association: Show and Tell (INTERSPEECH)},\n year = {2022}\n }\n \nPlease CITE our paper whenever the pre-trained models or the system are used to help produce published results or incorporated into other software.\nFor further information or requests, please go to VinAI Translate's homepage!"
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | zzttbrdd/sn6_03l | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T03:28:49+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-v3-small-otat-recommened-hp
This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the DandinPower/review_onlytitleandtext dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6500
- Accuracy: 0.6229
- Macro F1: 0.6240
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1500
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| 0.8942 | 1.14 | 500 | 0.8753 | 0.6316 | 0.6330 |
| 0.7816 | 2.29 | 1000 | 0.8880 | 0.633 | 0.6216 |
| 0.7231 | 3.43 | 1500 | 0.8827 | 0.632 | 0.6322 |
| 0.6145 | 4.57 | 2000 | 0.9674 | 0.6369 | 0.6329 |
| 0.4694 | 5.71 | 2500 | 1.0903 | 0.6249 | 0.6200 |
| 0.3611 | 6.86 | 3000 | 1.2490 | 0.6216 | 0.6249 |
| 0.278 | 8.0 | 3500 | 1.4194 | 0.6201 | 0.6230 |
| 0.1689 | 9.14 | 4000 | 1.6500 | 0.6229 | 0.6240 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"language": ["en"], "license": "mit", "tags": ["nycu-112-2-datamining-hw2", "generated_from_trainer"], "datasets": ["DandinPower/review_onlytitleandtext"], "metrics": ["accuracy"], "base_model": "microsoft/deberta-v3-small", "model-index": [{"name": "deberta-v3-small-otat-recommened-hp", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "DandinPower/review_onlytitleandtext", "type": "DandinPower/review_onlytitleandtext"}, "metrics": [{"type": "accuracy", "value": 0.6228571428571429, "name": "Accuracy"}]}]}]} | DandinPower/deberta-v3-small-otat-recommened-hp | null | [
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"nycu-112-2-datamining-hw2",
"generated_from_trainer",
"en",
"dataset:DandinPower/review_onlytitleandtext",
"base_model:microsoft/deberta-v3-small",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T03:28:52+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #deberta-v2 #text-classification #nycu-112-2-datamining-hw2 #generated_from_trainer #en #dataset-DandinPower/review_onlytitleandtext #base_model-microsoft/deberta-v3-small #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| deberta-v3-small-otat-recommened-hp
===================================
This model is a fine-tuned version of microsoft/deberta-v3-small on the DandinPower/review\_onlytitleandtext dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6500
* Accuracy: 0.6229
* Macro F1: 0.6240
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 4.5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1500
* num\_epochs: 10
### Training results
### Framework versions
* Transformers 4.39.3
* Pytorch 2.2.2+cu121
* Datasets 2.18.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4.5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1500\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.2.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #safetensors #deberta-v2 #text-classification #nycu-112-2-datamining-hw2 #generated_from_trainer #en #dataset-DandinPower/review_onlytitleandtext #base_model-microsoft/deberta-v3-small #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4.5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1500\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.2.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Dv - BanUrsus
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2999
- Wer Ortho: 56.6961
- Wer: 10.8095
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
| 0.1219 | 1.63 | 500 | 0.1725 | 63.1729 | 13.4698 |
| 0.0472 | 3.26 | 1000 | 0.1644 | 58.0820 | 11.8076 |
| 0.0288 | 4.89 | 1500 | 0.1815 | 58.2283 | 11.3294 |
| 0.0067 | 6.53 | 2000 | 0.2322 | 59.0919 | 11.2946 |
| 0.0018 | 8.16 | 2500 | 0.2608 | 57.5179 | 11.0217 |
| 0.001 | 9.79 | 3000 | 0.2815 | 57.1558 | 10.8895 |
| 0.0002 | 11.42 | 3500 | 0.2943 | 56.8633 | 10.8634 |
| 0.0002 | 13.05 | 4000 | 0.2999 | 56.6961 | 10.8095 |
### Framework versions
- Transformers 4.39.2
- Pytorch 1.13.0+cu117
- Datasets 2.16.1
- Tokenizers 0.15.1 | {"language": ["dv"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_13_0"], "metrics": ["wer"], "base_model": "openai/whisper-small", "model-index": [{"name": "Whisper Small Dv - BanUrsus", "results": []}]} | BanUrsus/whisper-small-dv | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dv",
"dataset:mozilla-foundation/common_voice_13_0",
"base_model:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T03:29:00+00:00 | [] | [
"dv"
] | TAGS
#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #dv #dataset-mozilla-foundation/common_voice_13_0 #base_model-openai/whisper-small #license-apache-2.0 #endpoints_compatible #region-us
| Whisper Small Dv - BanUrsus
===========================
This model is a fine-tuned version of openai/whisper-small on the Common Voice 13 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2999
* Wer Ortho: 56.6961
* Wer: 10.8095
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 50
* training\_steps: 4000
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.39.2
* Pytorch 1.13.0+cu117
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 50\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.2\n* Pytorch 1.13.0+cu117\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 50\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.2\n* Pytorch 1.13.0+cu117\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] |
null | adapter-transformers |
# Adapter `BigTMiami/seq_bn_amz_10k_help_class_no_pretrain_2_adapter` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/amazon_10k_helpfulness_dataset](https://huggingface.co/datasets/BigTMiami/amazon_10k_helpfulness_dataset/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("BigTMiami/seq_bn_amz_10k_help_class_no_pretrain_2_adapter", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> | {"tags": ["roberta", "adapter-transformers"], "datasets": ["BigTMiami/amazon_10k_helpfulness_dataset"]} | BigTMiami/seq_bn_amz_10k_help_class_no_pretrain_2_adapter | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/amazon_10k_helpfulness_dataset",
"region:us"
] | null | 2024-04-20T03:29:07+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/amazon_10k_helpfulness_dataset #region-us
|
# Adapter 'BigTMiami/seq_bn_amz_10k_help_class_no_pretrain_2_adapter' for roberta-base
An adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_10k_helpfulness_dataset dataset and includes a prediction head for classification.
This adapter was created for usage with the Adapters library.
## Usage
First, install 'adapters':
Now, the adapter can be loaded and activated like this:
## Architecture & Training
## Evaluation results
| [
"# Adapter 'BigTMiami/seq_bn_amz_10k_help_class_no_pretrain_2_adapter' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_10k_helpfulness_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] | [
"TAGS\n#adapter-transformers #roberta #dataset-BigTMiami/amazon_10k_helpfulness_dataset #region-us \n",
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"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | zzttbrdd/sn6_00l | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T03:29:15+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
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"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# style-treasury-from-mistral
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the generator dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "style-treasury-from-mistral", "results": []}]} | RuoxiL/style-treasury-from-mistral | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:mistralai/Mistral-7B-v0.1",
"region:us"
] | null | 2024-04-20T03:31:01+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-mistralai/Mistral-7B-v0.1 #region-us
|
# style-treasury-from-mistral
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the generator dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 | [
"# style-treasury-from-mistral\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the generator dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 3\n- total_train_batch_size: 6\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 3\n- mixed_precision_training: Native AMP",
"### Training results",
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"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 3\n- total_train_batch_size: 6\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 3\n- mixed_precision_training: Native AMP",
"### Training results",
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] |
text-generation | transformers |
# Model Card for LLaVA-LLaMA-3-8B
<!-- Provide a quick summary of what the model is/does. -->
A reproduced LLaVA LVLM based on Llama-3-8B LLM backbone. Not an official implementation.
Please follow my reproduced implementation [LLaVA-Llama-3](https://github.com/Victorwz/LLaVA-Llama-3/) for more details on fine-tuning LLaVA model with Llama-3 as the foundatiaon LLM.
## Model Details
Follows LLavA-1.5 pre-train and supervised fine-tuning pipeline. You do not need to change the LLaVA codebase to accommodate Llama-3.
## How to Use
Please firstly install llava via
```
pip install git+https://github.com/Victorwz/LLaVA-Llama-3.git
```
You can load the model and perform inference as follows:
```python
from llava.conversation import conv_templates, SeparatorStyle
from llava.model.builder import load_pretrained_model
from llava.mm_utils import tokenizer_image_token, process_images, get_model_name_from_path
from PIL import Image
import requests
import torch
from io import BytesIO
# load model and processor
device = "cuda" if torch.cuda.is_available() else "cpu"
model_name = get_model_name_from_path("weizhiwang/LLaVA-Llama-3-8B")
tokenizer, model, image_processor, context_len = load_pretrained_model("weizhiwang/LLaVA-Llama-3-8B", None, model_name, False, False, device=device)
# prepare inputs for the model
text = '<image>' + '\n' + "Describe the image."
conv = conv_templates["llama_3"].copy()
conv.append_message(conv.roles[0], text)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
input_ids = tokenizer_image_token(prompt, tokenizer, -200, return_tensors='pt').unsqueeze(0).cuda()
# prepare image input
url = "https://huggingface.co/adept/fuyu-8b/resolve/main/bus.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert('RGB')
image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'].half().cuda()
# autoregressively generate text
with torch.inference_mode():
output_ids = model.generate(
input_ids,
images=image_tensor,
do_sample=False,
max_new_tokens=512,
use_cache=True)
outputs = tokenizer.batch_decode(output_ids[:, input_ids.shape[1]:], skip_special_tokens=True)
print(outputs[0])
```
The image caption results look like:
```
The image features a blue and orange double-decker bus parked on a street. The bus is stopped at a bus stop, waiting for passengers to board. There are several people standing around the bus, some of them closer to the bus and others further away.
In the background, there are two cars parked on the street, one on the left side and the other on the right side. Additionally, there is a traffic light visible in the scene, indicating that the bus is stopped at an intersection.
```
# Fine-Tune LLaVA-Llama-3 on Your Visual Instruction Data
Please refer to a forked [LLaVA-Llama-3](https://github.com/Victorwz/LLaVA-Llama-3) git repo for fine-tuning data preparation and scripts. The data loading function and fastchat conversation template are changed due to a different tokenizer.
## Benchmark Results
| Model | MMMU Val |
| :-------------------- | :---------------: |
| LLaVA-v1.5-7B | 35.3 |
| LLaVA-Llama-3-8B | 36.7 |
Please refer to `eval_outputs/LLaVA-Llama-3-8B_mmmu_val.json` for reproduce the benchmark performance on MMMU validation set.
## Citation
```bibtex
@misc{wang2024llavallama3,
title={LLaVA-Llama-3-8B: A reproduction towards LLaVA-v1.5 based on Llama-3-8B LLM backbone},
author={Wang, Weizhi},
year={2024}
}
```
| {"language": ["en"], "license": "cc", "datasets": ["liuhaotian/LLaVA-Instruct-150K", "liuhaotian/LLaVA-Pretrain"]} | weizhiwang/LLaVA-Llama-3-8B | null | [
"transformers",
"pytorch",
"llava",
"text-generation",
"conversational",
"en",
"dataset:liuhaotian/LLaVA-Instruct-150K",
"dataset:liuhaotian/LLaVA-Pretrain",
"license:cc",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T03:31:15+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #llava #text-generation #conversational #en #dataset-liuhaotian/LLaVA-Instruct-150K #dataset-liuhaotian/LLaVA-Pretrain #license-cc #autotrain_compatible #endpoints_compatible #region-us
| Model Card for LLaVA-LLaMA-3-8B
===============================
A reproduced LLaVA LVLM based on Llama-3-8B LLM backbone. Not an official implementation.
Please follow my reproduced implementation LLaVA-Llama-3 for more details on fine-tuning LLaVA model with Llama-3 as the foundatiaon LLM.
Model Details
-------------
Follows LLavA-1.5 pre-train and supervised fine-tuning pipeline. You do not need to change the LLaVA codebase to accommodate Llama-3.
How to Use
----------
Please firstly install llava via
You can load the model and perform inference as follows:
The image caption results look like:
Fine-Tune LLaVA-Llama-3 on Your Visual Instruction Data
=======================================================
Please refer to a forked LLaVA-Llama-3 git repo for fine-tuning data preparation and scripts. The data loading function and fastchat conversation template are changed due to a different tokenizer.
Benchmark Results
-----------------
Please refer to 'eval\_outputs/LLaVA-Llama-3-8B\_mmmu\_val.json' for reproduce the benchmark performance on MMMU validation set.
| [] | [
"TAGS\n#transformers #pytorch #llava #text-generation #conversational #en #dataset-liuhaotian/LLaVA-Instruct-150K #dataset-liuhaotian/LLaVA-Pretrain #license-cc #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# byt5_add
This model is a fine-tuned version of [google/byt5-small](https://huggingface.co/google/byt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0003
- eval_runtime: 10.8156
- eval_samples_per_second: 924.594
- eval_steps_per_second: 1.202
- epoch: 51.0
- step: 1275
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 800
- eval_batch_size: 800
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 200
### Framework versions
- Transformers 4.35.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "google/byt5-small", "model-index": [{"name": "byt5_add", "results": []}]} | AlexWang99/byt5_add | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/byt5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T03:34:27+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-google/byt5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# byt5_add
This model is a fine-tuned version of google/byt5-small on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0003
- eval_runtime: 10.8156
- eval_samples_per_second: 924.594
- eval_steps_per_second: 1.202
- epoch: 51.0
- step: 1275
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 800
- eval_batch_size: 800
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 200
### Framework versions
- Transformers 4.35.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| [
"# byt5_add\n\nThis model is a fine-tuned version of google/byt5-small on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.0003\n- eval_runtime: 10.8156\n- eval_samples_per_second: 924.594\n- eval_steps_per_second: 1.202\n- epoch: 51.0\n- step: 1275",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 800\n- eval_batch_size: 800\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 200",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] | [
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"# byt5_add\n\nThis model is a fine-tuned version of google/byt5-small on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.0003\n- eval_runtime: 10.8156\n- eval_samples_per_second: 924.594\n- eval_steps_per_second: 1.202\n- epoch: 51.0\n- step: 1275",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 800\n- eval_batch_size: 800\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 200",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "187.42 +/- 93.53", "name": "mean_reward", "verified": false}]}]}]} | yunkimmy/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2024-04-20T03:35:01+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] |
image-to-image | diffusers | This is the inpainting version of RealVisXL_V4 in diffusers "fp16" format.
Original model: https://huggingface.co/SG161222/RealVisXL_V4.0
| {"license": "openrail++"} | OzzyGT/RealVisXL_V4.0_inpainting | null | [
"diffusers",
"license:openrail++",
"diffusers:StableDiffusionXLInpaintPipeline",
"region:us",
"has_space"
] | null | 2024-04-20T03:37:36+00:00 | [] | [] | TAGS
#diffusers #license-openrail++ #diffusers-StableDiffusionXLInpaintPipeline #region-us #has_space
| This is the inpainting version of RealVisXL_V4 in diffusers "fp16" format.
Original model: URL
| [] | [
"TAGS\n#diffusers #license-openrail++ #diffusers-StableDiffusionXLInpaintPipeline #region-us #has_space \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# robust_llm_pythia-160m_ian-022_PasswordMatch_n-its-25
This model is a fine-tuned version of [EleutherAI/pythia-160m](https://huggingface.co/EleutherAI/pythia-160m) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-160m", "model-index": [{"name": "robust_llm_pythia-160m_ian-022_PasswordMatch_n-its-25", "results": []}]} | AlignmentResearch/robust_llm_pythia-160m_ian-022_PasswordMatch_n-its-25 | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-160m",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T03:39:13+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #gpt_neox #text-classification #generated_from_trainer #base_model-EleutherAI/pythia-160m #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# robust_llm_pythia-160m_ian-022_PasswordMatch_n-its-25
This model is a fine-tuned version of EleutherAI/pythia-160m on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
| [
"# robust_llm_pythia-160m_ian-022_PasswordMatch_n-its-25\n\nThis model is a fine-tuned version of EleutherAI/pythia-160m on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 0\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1",
"### Training results",
"### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.2.1\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tensorboard #safetensors #gpt_neox #text-classification #generated_from_trainer #base_model-EleutherAI/pythia-160m #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# robust_llm_pythia-160m_ian-022_PasswordMatch_n-its-25\n\nThis model is a fine-tuned version of EleutherAI/pythia-160m on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 0\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1",
"### Training results",
"### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.2.1\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 0.0_ablation_declr_4iters4e6_iter_3
This model is a fine-tuned version of [ZhangShenao/0.0_ablation_declr_4iters4e6_iter_2](https://huggingface.co/ZhangShenao/0.0_ablation_declr_4iters4e6_iter_2) on the ZhangShenao/0.0_ablation_declr_4iters4e6_dataset dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["ZhangShenao/0.0_ablation_declr_4iters4e6_dataset"], "base_model": "ZhangShenao/0.0_ablation_declr_4iters4e6_iter_2", "model-index": [{"name": "0.0_ablation_declr_4iters4e6_iter_3", "results": []}]} | ZhangShenao/0.0_ablation_declr_4iters4e6_iter_3 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:ZhangShenao/0.0_ablation_declr_4iters4e6_dataset",
"base_model:ZhangShenao/0.0_ablation_declr_4iters4e6_iter_2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T03:41:12+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #alignment-handbook #generated_from_trainer #trl #dpo #conversational #dataset-ZhangShenao/0.0_ablation_declr_4iters4e6_dataset #base_model-ZhangShenao/0.0_ablation_declr_4iters4e6_iter_2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# 0.0_ablation_declr_4iters4e6_iter_3
This model is a fine-tuned version of ZhangShenao/0.0_ablation_declr_4iters4e6_iter_2 on the ZhangShenao/0.0_ablation_declr_4iters4e6_dataset dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
| [
"# 0.0_ablation_declr_4iters4e6_iter_3\n\nThis model is a fine-tuned version of ZhangShenao/0.0_ablation_declr_4iters4e6_iter_2 on the ZhangShenao/0.0_ablation_declr_4iters4e6_dataset dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-06\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 128\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1",
"### Training results",
"### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #alignment-handbook #generated_from_trainer #trl #dpo #conversational #dataset-ZhangShenao/0.0_ablation_declr_4iters4e6_dataset #base_model-ZhangShenao/0.0_ablation_declr_4iters4e6_iter_2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# 0.0_ablation_declr_4iters4e6_iter_3\n\nThis model is a fine-tuned version of ZhangShenao/0.0_ablation_declr_4iters4e6_iter_2 on the ZhangShenao/0.0_ablation_declr_4iters4e6_dataset dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-06\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 128\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1",
"### Training results",
"### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.2"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-firdous-nep-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice_13_0 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice_13_0"], "base_model": "facebook/wav2vec2-large-xlsr-53", "model-index": [{"name": "wav2vec2-large-xls-r-300m-firdous-nep-colab", "results": []}]} | f77777/wav2vec2-large-xls-r-300m-firdous-nep-colab | null | [
"transformers",
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"wav2vec2",
"automatic-speech-recognition",
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"dataset:common_voice_13_0",
"base_model:facebook/wav2vec2-large-xlsr-53",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T03:43:05+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_13_0 #base_model-facebook/wav2vec2-large-xlsr-53 #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-firdous-nep-colab
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice_13_0 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
| [
"# wav2vec2-large-xls-r-300m-firdous-nep-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice_13_0 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 30",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.19.0\n- Tokenizers 0.15.2"
] | [
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"# wav2vec2-large-xls-r-300m-firdous-nep-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice_13_0 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 30",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.19.0\n- Tokenizers 0.15.2"
] |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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<!-- This section describes the evaluation protocols and provides the results. -->
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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## Glossary [optional]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | BuroIdentidadDigital/formaMigratoria_Frontal_v0 | null | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T03:43:53+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #vision-encoder-decoder #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #vision-encoder-decoder #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# cnn_dailymail_8824_bart-base
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9201
- Rouge1: 0.2472
- Rouge2: 0.1256
- Rougel: 0.2063
- Rougelsum: 0.2331
- Gen Len: 20.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.2077 | 0.11 | 500 | 1.0668 | 0.2378 | 0.1128 | 0.1955 | 0.2228 | 20.0 |
| 1.1503 | 0.22 | 1000 | 1.0418 | 0.2376 | 0.1145 | 0.1964 | 0.223 | 20.0 |
| 1.1191 | 0.33 | 1500 | 1.0109 | 0.2409 | 0.1187 | 0.1995 | 0.2268 | 20.0 |
| 1.0828 | 0.45 | 2000 | 1.0048 | 0.2408 | 0.1192 | 0.2004 | 0.227 | 20.0 |
| 1.0546 | 0.56 | 2500 | 0.9911 | 0.2417 | 0.1206 | 0.2008 | 0.2278 | 20.0 |
| 1.0537 | 0.67 | 3000 | 0.9891 | 0.2418 | 0.1201 | 0.2014 | 0.2277 | 20.0 |
| 1.0643 | 0.78 | 3500 | 0.9895 | 0.2396 | 0.1194 | 0.1997 | 0.2259 | 20.0 |
| 1.0375 | 0.89 | 4000 | 0.9775 | 0.2434 | 0.122 | 0.2025 | 0.2293 | 20.0 |
| 1.013 | 1.0 | 4500 | 0.9728 | 0.244 | 0.1218 | 0.2029 | 0.2298 | 20.0 |
| 1.0247 | 1.11 | 5000 | 0.9705 | 0.243 | 0.1206 | 0.2019 | 0.2287 | 20.0 |
| 1.0374 | 1.23 | 5500 | 0.9642 | 0.2432 | 0.1217 | 0.2022 | 0.2292 | 20.0 |
| 1.0084 | 1.34 | 6000 | 0.9609 | 0.2437 | 0.1235 | 0.204 | 0.2299 | 20.0 |
| 1.0195 | 1.45 | 6500 | 0.9603 | 0.243 | 0.1221 | 0.2029 | 0.2291 | 20.0 |
| 0.9642 | 1.56 | 7000 | 0.9559 | 0.2438 | 0.1228 | 0.2035 | 0.2301 | 20.0 |
| 0.9903 | 1.67 | 7500 | 0.9540 | 0.243 | 0.1225 | 0.2029 | 0.2293 | 20.0 |
| 0.976 | 1.78 | 8000 | 0.9518 | 0.2434 | 0.1224 | 0.2025 | 0.2297 | 19.9997 |
| 1.0101 | 1.89 | 8500 | 0.9460 | 0.2452 | 0.1235 | 0.2042 | 0.231 | 20.0 |
| 0.9711 | 2.01 | 9000 | 0.9446 | 0.2431 | 0.1226 | 0.2032 | 0.2295 | 19.9995 |
| 0.9137 | 2.12 | 9500 | 0.9463 | 0.2459 | 0.1239 | 0.205 | 0.2318 | 20.0 |
| 0.9631 | 2.23 | 10000 | 0.9410 | 0.2451 | 0.1234 | 0.2043 | 0.2309 | 19.9999 |
| 0.9309 | 2.34 | 10500 | 0.9399 | 0.2446 | 0.1236 | 0.2042 | 0.2308 | 19.9991 |
| 0.9653 | 2.45 | 11000 | 0.9363 | 0.2444 | 0.1233 | 0.2039 | 0.2308 | 19.9999 |
| 0.9338 | 2.56 | 11500 | 0.9413 | 0.2439 | 0.1224 | 0.2028 | 0.2294 | 20.0 |
| 0.9373 | 2.67 | 12000 | 0.9334 | 0.245 | 0.1241 | 0.2047 | 0.2312 | 19.9996 |
| 0.9661 | 2.79 | 12500 | 0.9334 | 0.2456 | 0.1241 | 0.2051 | 0.2318 | 19.9999 |
| 0.9446 | 2.9 | 13000 | 0.9340 | 0.2447 | 0.1239 | 0.2045 | 0.2309 | 19.9999 |
| 0.9109 | 3.01 | 13500 | 0.9340 | 0.2445 | 0.1234 | 0.2041 | 0.2308 | 19.9999 |
| 0.8955 | 3.12 | 14000 | 0.9357 | 0.2459 | 0.1249 | 0.2055 | 0.2318 | 20.0 |
| 0.9163 | 3.23 | 14500 | 0.9319 | 0.2461 | 0.1239 | 0.205 | 0.2319 | 20.0 |
| 0.9059 | 3.34 | 15000 | 0.9320 | 0.2446 | 0.124 | 0.2044 | 0.2309 | 19.9997 |
| 0.8893 | 3.46 | 15500 | 0.9288 | 0.2462 | 0.1247 | 0.2053 | 0.2322 | 19.9999 |
| 0.8963 | 3.57 | 16000 | 0.9301 | 0.2441 | 0.124 | 0.2043 | 0.2306 | 20.0 |
| 0.8924 | 3.68 | 16500 | 0.9295 | 0.2431 | 0.1236 | 0.2038 | 0.2296 | 19.9997 |
| 0.8832 | 3.79 | 17000 | 0.9267 | 0.2457 | 0.1237 | 0.2049 | 0.2316 | 19.9999 |
| 0.8874 | 3.9 | 17500 | 0.9263 | 0.2458 | 0.125 | 0.2054 | 0.232 | 20.0 |
| 0.8464 | 4.01 | 18000 | 0.9272 | 0.2446 | 0.1234 | 0.2039 | 0.2305 | 20.0 |
| 0.8391 | 4.12 | 18500 | 0.9253 | 0.2453 | 0.1245 | 0.205 | 0.2313 | 20.0 |
| 0.8602 | 4.24 | 19000 | 0.9273 | 0.2464 | 0.1248 | 0.2055 | 0.2322 | 19.9997 |
| 0.8674 | 4.35 | 19500 | 0.9260 | 0.2449 | 0.1242 | 0.2047 | 0.2309 | 20.0 |
| 0.8634 | 4.46 | 20000 | 0.9261 | 0.2462 | 0.1248 | 0.2053 | 0.2322 | 20.0 |
| 0.8522 | 4.57 | 20500 | 0.9259 | 0.2456 | 0.1242 | 0.2052 | 0.2316 | 20.0 |
| 0.8532 | 4.68 | 21000 | 0.9256 | 0.2452 | 0.1242 | 0.2049 | 0.2315 | 20.0 |
| 0.8608 | 4.79 | 21500 | 0.9218 | 0.2446 | 0.1242 | 0.2049 | 0.2309 | 19.9997 |
| 0.8649 | 4.9 | 22000 | 0.9239 | 0.2461 | 0.1243 | 0.2047 | 0.2317 | 19.9997 |
| 0.8329 | 5.02 | 22500 | 0.9260 | 0.2456 | 0.1248 | 0.2052 | 0.2315 | 19.9999 |
| 0.8475 | 5.13 | 23000 | 0.9247 | 0.2449 | 0.1241 | 0.2045 | 0.2309 | 20.0 |
| 0.8595 | 5.24 | 23500 | 0.9246 | 0.2443 | 0.1239 | 0.2044 | 0.2306 | 20.0 |
| 0.8707 | 5.35 | 24000 | 0.9228 | 0.2458 | 0.1246 | 0.2054 | 0.2318 | 19.9997 |
| 0.8565 | 5.46 | 24500 | 0.9243 | 0.245 | 0.1241 | 0.2047 | 0.231 | 20.0 |
| 0.848 | 5.57 | 25000 | 0.9232 | 0.2464 | 0.1256 | 0.206 | 0.2324 | 20.0 |
| 0.8251 | 5.68 | 25500 | 0.9212 | 0.2465 | 0.1253 | 0.2057 | 0.2327 | 20.0 |
| 0.8352 | 5.8 | 26000 | 0.9203 | 0.245 | 0.1242 | 0.2043 | 0.2309 | 19.9996 |
| 0.837 | 5.91 | 26500 | 0.9178 | 0.2464 | 0.1247 | 0.2055 | 0.2321 | 19.9999 |
| 0.8233 | 6.02 | 27000 | 0.9204 | 0.2456 | 0.1247 | 0.2052 | 0.2318 | 20.0 |
| 0.8169 | 6.13 | 27500 | 0.9246 | 0.2454 | 0.1242 | 0.205 | 0.2314 | 20.0 |
| 0.8351 | 6.24 | 28000 | 0.9194 | 0.2453 | 0.1248 | 0.2052 | 0.2312 | 20.0 |
| 0.8275 | 6.35 | 28500 | 0.9221 | 0.2468 | 0.1255 | 0.2062 | 0.2329 | 19.9999 |
| 0.818 | 6.46 | 29000 | 0.9244 | 0.2456 | 0.1243 | 0.205 | 0.2316 | 20.0 |
| 0.8262 | 6.58 | 29500 | 0.9194 | 0.2471 | 0.1256 | 0.2064 | 0.233 | 20.0 |
| 0.8138 | 6.69 | 30000 | 0.9225 | 0.2469 | 0.1257 | 0.2062 | 0.233 | 20.0 |
| 0.8476 | 6.8 | 30500 | 0.9188 | 0.2467 | 0.1254 | 0.2059 | 0.2328 | 20.0 |
| 0.8376 | 6.91 | 31000 | 0.9216 | 0.2473 | 0.1255 | 0.2064 | 0.2331 | 20.0 |
| 0.7947 | 7.02 | 31500 | 0.9218 | 0.2471 | 0.1256 | 0.2061 | 0.2329 | 19.9999 |
| 0.7937 | 7.13 | 32000 | 0.9241 | 0.2465 | 0.1249 | 0.2057 | 0.2324 | 19.9996 |
| 0.8194 | 7.24 | 32500 | 0.9230 | 0.2471 | 0.1259 | 0.2063 | 0.2329 | 20.0 |
| 0.8122 | 7.36 | 33000 | 0.9204 | 0.2458 | 0.125 | 0.2055 | 0.232 | 19.9996 |
| 0.7676 | 7.47 | 33500 | 0.9232 | 0.2468 | 0.1253 | 0.206 | 0.2327 | 20.0 |
| 0.7772 | 7.58 | 34000 | 0.9226 | 0.2463 | 0.1251 | 0.2057 | 0.2323 | 20.0 |
| 0.809 | 7.69 | 34500 | 0.9197 | 0.2469 | 0.1255 | 0.2061 | 0.2329 | 19.9997 |
| 0.7839 | 7.8 | 35000 | 0.9205 | 0.2475 | 0.1261 | 0.2067 | 0.2334 | 19.9997 |
| 0.7936 | 7.91 | 35500 | 0.9186 | 0.2469 | 0.1254 | 0.2061 | 0.2327 | 19.9997 |
| 0.8108 | 8.02 | 36000 | 0.9215 | 0.2472 | 0.1253 | 0.206 | 0.2329 | 20.0 |
| 0.7987 | 8.14 | 36500 | 0.9219 | 0.2473 | 0.1254 | 0.2062 | 0.2331 | 19.9999 |
| 0.7881 | 8.25 | 37000 | 0.9213 | 0.2474 | 0.1253 | 0.206 | 0.233 | 20.0 |
| 0.8007 | 8.36 | 37500 | 0.9215 | 0.2474 | 0.1258 | 0.2064 | 0.2332 | 20.0 |
| 0.7789 | 8.47 | 38000 | 0.9226 | 0.2462 | 0.1252 | 0.2054 | 0.2321 | 20.0 |
| 0.8155 | 8.58 | 38500 | 0.9182 | 0.2465 | 0.1254 | 0.206 | 0.2325 | 19.9999 |
| 0.7863 | 8.69 | 39000 | 0.9187 | 0.2465 | 0.1252 | 0.2059 | 0.2323 | 19.9999 |
| 0.796 | 8.8 | 39500 | 0.9201 | 0.2469 | 0.1254 | 0.206 | 0.2327 | 19.9999 |
| 0.8003 | 8.92 | 40000 | 0.9197 | 0.2463 | 0.1252 | 0.2057 | 0.2323 | 20.0 |
| 0.803 | 9.03 | 40500 | 0.9206 | 0.2465 | 0.1253 | 0.2058 | 0.2323 | 19.9997 |
| 0.79 | 9.14 | 41000 | 0.9221 | 0.2467 | 0.1251 | 0.206 | 0.2326 | 19.9997 |
| 0.7605 | 9.25 | 41500 | 0.9211 | 0.247 | 0.1254 | 0.2059 | 0.2329 | 20.0 |
| 0.7543 | 9.36 | 42000 | 0.9214 | 0.2473 | 0.1258 | 0.2065 | 0.2333 | 19.9999 |
| 0.7959 | 9.47 | 42500 | 0.9203 | 0.2471 | 0.1255 | 0.2061 | 0.2332 | 19.9999 |
| 0.7826 | 9.58 | 43000 | 0.9205 | 0.2469 | 0.1256 | 0.206 | 0.2329 | 20.0 |
| 0.7835 | 9.7 | 43500 | 0.9198 | 0.2466 | 0.1252 | 0.2057 | 0.2326 | 20.0 |
| 0.7809 | 9.81 | 44000 | 0.9205 | 0.2469 | 0.1253 | 0.206 | 0.2328 | 20.0 |
| 0.7899 | 9.92 | 44500 | 0.9201 | 0.2472 | 0.1256 | 0.2063 | 0.2331 | 20.0 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.0.0+cu117
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "facebook/bart-base", "model-index": [{"name": "cnn_dailymail_8824_bart-base", "results": []}]} | baek26/cnn_dailymail_8824_bart-base | null | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T03:43:59+00:00 | [] | [] | TAGS
#transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| cnn\_dailymail\_8824\_bart-base
===============================
This model is a fine-tuned version of facebook/bart-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9201
* Rouge1: 0.2472
* Rouge2: 0.1256
* Rougel: 0.2063
* Rougelsum: 0.2331
* Gen Len: 20.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 10
### Training results
### Framework versions
* Transformers 4.38.2
* Pytorch 2.0.0+cu117
* Datasets 2.18.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
text-to-image | diffusers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🧨 diffusers pipeline that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
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[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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[More Information Needed]
| {"library_name": "diffusers"} | Hiroto0126/repo | null | [
"diffusers",
"arxiv:1910.09700",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | null | 2024-04-20T03:45:43+00:00 | [
"1910.09700"
] | [] | TAGS
#diffusers #arxiv-1910.09700 #endpoints_compatible #diffusers-StableDiffusionXLPipeline #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a diffusers pipeline that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a diffusers pipeline that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#diffusers #arxiv-1910.09700 #endpoints_compatible #diffusers-StableDiffusionXLPipeline #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a diffusers pipeline that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-general-stories
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.0063
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 5.7415 | 1.0 | 2940 | 5.4942 |
| 5.1216 | 2.0 | 5880 | 4.9520 |
| 4.7464 | 3.0 | 8820 | 4.6264 |
| 4.5081 | 4.0 | 11760 | 4.4148 |
| 4.3457 | 5.0 | 14700 | 4.2677 |
| 4.2207 | 6.0 | 17640 | 4.1709 |
| 4.1182 | 7.0 | 20580 | 4.0961 |
| 4.0599 | 8.0 | 23520 | 4.0473 |
| 4.0235 | 9.0 | 26460 | 4.0176 |
| 3.9931 | 10.0 | 29400 | 4.0063 |
### Framework versions
- Transformers 4.40.0
- Pytorch 1.13.1+cu117
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilgpt2", "model-index": [{"name": "distilgpt2-finetuned-general-stories", "results": []}]} | Vexemous/distilgpt2-finetuned-general-stories | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T03:47:43+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-distilgpt2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-finetuned-general-stories
====================================
This model is a fine-tuned version of distilgpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 4.0063
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.40.0
* Pytorch 1.13.1+cu117
* Datasets 2.19.0
* Tokenizers 0.19.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 1.13.1+cu117\n* Datasets 2.19.0\n* Tokenizers 0.19.1"
] |
null | transformers | ## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/ResplendentAI/Aura_L3_8B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Aura_L3_8B-GGUF/resolve/main/Aura_L3_8B.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Aura_L3_8B-GGUF/resolve/main/Aura_L3_8B.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Aura_L3_8B-GGUF/resolve/main/Aura_L3_8B.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Aura_L3_8B-GGUF/resolve/main/Aura_L3_8B.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Aura_L3_8B-GGUF/resolve/main/Aura_L3_8B.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Aura_L3_8B-GGUF/resolve/main/Aura_L3_8B.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Aura_L3_8B-GGUF/resolve/main/Aura_L3_8B.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Aura_L3_8B-GGUF/resolve/main/Aura_L3_8B.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Aura_L3_8B-GGUF/resolve/main/Aura_L3_8B.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Aura_L3_8B-GGUF/resolve/main/Aura_L3_8B.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Aura_L3_8B-GGUF/resolve/main/Aura_L3_8B.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Aura_L3_8B-GGUF/resolve/main/Aura_L3_8B.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Aura_L3_8B-GGUF/resolve/main/Aura_L3_8B.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Aura_L3_8B-GGUF/resolve/main/Aura_L3_8B.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "base_model": "ResplendentAI/Aura_L3_8B", "quantized_by": "mradermacher"} | mradermacher/Aura_L3_8B-GGUF | null | [
"transformers",
"gguf",
"en",
"base_model:ResplendentAI/Aura_L3_8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T03:48:07+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #en #base_model-ResplendentAI/Aura_L3_8B #license-apache-2.0 #endpoints_compatible #region-us
| About
-----
static quants of URL
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
Usage
-----
If you are unsure how to use GGUF files, refer to one of TheBloke's
READMEs for
more details, including on how to concatenate multi-part files.
Provided Quants
---------------
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
!URL
And here are Artefact2's thoughts on the matter:
URL
FAQ / Model Request
-------------------
See URL for some answers to
questions you might have and/or if you want some other model quantized.
Thanks
------
I thank my company, nethype GmbH, for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
| [] | [
"TAGS\n#transformers #gguf #en #base_model-ResplendentAI/Aura_L3_8B #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | Sahil998/codegen-350M-mono-finetuned-python-18k-alpaca_50_percent_30epochs | null | [
"transformers",
"safetensors",
"codegen",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T03:49:59+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #codegen #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
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"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
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] | [
"TAGS\n#transformers #safetensors #codegen #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"## Uses",
"### Direct Use",
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"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
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"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
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"## Technical Specifications [optional]",
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] |
null | transformers |
# Model Card for Model ID
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed] | {"library_name": "transformers", "tags": []} | Sahil998/codegen-350M-mono-finetuned-python-18k-alpaca_50_percent_30pochs | null | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T03:50:18+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
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- Hardware Type:
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[optional]
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APA:
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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
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"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
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"#### Factors",
"#### Metrics",
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"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
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] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MPF-DialogLED-base-16384-dialogsum-3-epochs-finetuned
The model is created as a part of the project: linkwillbeaddedlater.
This model is a fine-tuned version of [MingZhong/DialogLED-base-16384](https://huggingface.co/MingZhong/DialogLED-base-16384) on the dialogsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2116
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.4631 | 0.9 | 175 | 1.4061 |
| 1.3229 | 1.8 | 350 | 1.2513 |
| 1.15 | 2.7 | 525 | 1.2116 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2 | {"tags": ["generated_from_trainer"], "datasets": ["knkarthick/dialogsum"], "base_model": "MingZhong/DialogLED-base-16384", "model-index": [{"name": "MPF-DialogLED-base-16384-dialogsum-3-epochs-finetuned", "results": []}]} | StDestiny/MPF-DialogLED-base-16384-dialogsum-3-epochs-finetuned | null | [
"transformers",
"tensorboard",
"safetensors",
"led",
"text2text-generation",
"generated_from_trainer",
"dataset:knkarthick/dialogsum",
"base_model:MingZhong/DialogLED-base-16384",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T03:56:06+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #led #text2text-generation #generated_from_trainer #dataset-knkarthick/dialogsum #base_model-MingZhong/DialogLED-base-16384 #autotrain_compatible #endpoints_compatible #region-us
| MPF-DialogLED-base-16384-dialogsum-3-epochs-finetuned
=====================================================
The model is created as a part of the project: linkwillbeaddedlater.
This model is a fine-tuned version of MingZhong/DialogLED-base-16384 on the dialogsum dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2116
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.39.3
* Pytorch 2.1.2
* Datasets 2.18.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.1.2\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tensorboard #safetensors #led #text2text-generation #generated_from_trainer #dataset-knkarthick/dialogsum #base_model-MingZhong/DialogLED-base-16384 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.1.2\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="dallonf/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "FrozenLake-v1-4x4-no_slippery"}, "metrics": [{"type": "mean_reward", "value": "1.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]} | dallonf/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2024-04-20T03:56:35+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing1 FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="dallonf/Q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "Q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.52 +/- 2.73", "name": "mean_reward", "verified": false}]}]}]} | dallonf/Q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2024-04-20T03:58:55+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing1 Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] |
null | adapter-transformers |
# Adapter `BigTMiami/seq_bn_amz_10k_help_class_no_pretrain_3_adapter` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/amazon_10k_helpfulness_dataset](https://huggingface.co/datasets/BigTMiami/amazon_10k_helpfulness_dataset/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("BigTMiami/seq_bn_amz_10k_help_class_no_pretrain_3_adapter", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> | {"tags": ["roberta", "adapter-transformers"], "datasets": ["BigTMiami/amazon_10k_helpfulness_dataset"]} | BigTMiami/seq_bn_amz_10k_help_class_no_pretrain_3_adapter | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/amazon_10k_helpfulness_dataset",
"region:us"
] | null | 2024-04-20T04:00:19+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/amazon_10k_helpfulness_dataset #region-us
|
# Adapter 'BigTMiami/seq_bn_amz_10k_help_class_no_pretrain_3_adapter' for roberta-base
An adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_10k_helpfulness_dataset dataset and includes a prediction head for classification.
This adapter was created for usage with the Adapters library.
## Usage
First, install 'adapters':
Now, the adapter can be loaded and activated like this:
## Architecture & Training
## Evaluation results
| [
"# Adapter 'BigTMiami/seq_bn_amz_10k_help_class_no_pretrain_3_adapter' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_10k_helpfulness_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] | [
"TAGS\n#adapter-transformers #roberta #dataset-BigTMiami/amazon_10k_helpfulness_dataset #region-us \n",
"# Adapter 'BigTMiami/seq_bn_amz_10k_help_class_no_pretrain_3_adapter' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_10k_helpfulness_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# cs_domain_pretrained_model
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.2410
- eval_runtime: 87.0838
- eval_samples_per_second: 114.832
- eval_steps_per_second: 1.803
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 2048
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 1
### Framework versions
- Transformers 4.30.2
- Pytorch 1.13.1+cu117
- Datasets 2.13.2
- Tokenizers 0.13.3
| {"tags": ["generated_from_trainer"], "model-index": [{"name": "cs_domain_pretrained_model", "results": []}]} | ltuzova/cs_domain_pretrained_model | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T04:01:01+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# cs_domain_pretrained_model
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.2410
- eval_runtime: 87.0838
- eval_samples_per_second: 114.832
- eval_steps_per_second: 1.803
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 2048
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 1
### Framework versions
- Transformers 4.30.2
- Pytorch 1.13.1+cu117
- Datasets 2.13.2
- Tokenizers 0.13.3
| [
"# cs_domain_pretrained_model\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 1.2410\n- eval_runtime: 87.0838\n- eval_samples_per_second: 114.832\n- eval_steps_per_second: 1.803\n- step: 0",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 32\n- eval_batch_size: 64\n- seed: 42\n- gradient_accumulation_steps: 64\n- total_train_batch_size: 2048\n- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_ratio: 0.06\n- num_epochs: 1",
"### Framework versions\n\n- Transformers 4.30.2\n- Pytorch 1.13.1+cu117\n- Datasets 2.13.2\n- Tokenizers 0.13.3"
] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# cs_domain_pretrained_model\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 1.2410\n- eval_runtime: 87.0838\n- eval_samples_per_second: 114.832\n- eval_steps_per_second: 1.803\n- step: 0",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 32\n- eval_batch_size: 64\n- seed: 42\n- gradient_accumulation_steps: 64\n- total_train_batch_size: 2048\n- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_ratio: 0.06\n- num_epochs: 1",
"### Framework versions\n\n- Transformers 4.30.2\n- Pytorch 1.13.1+cu117\n- Datasets 2.13.2\n- Tokenizers 0.13.3"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-finetuned-kwsylchunk-64-8line
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3752
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1919 | 1.13 | 500 | 1.4809 |
| 1.4962 | 2.25 | 1000 | 1.3752 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "facebook/bart-large", "model-index": [{"name": "bart-finetuned-kwsylchunk-64-8line", "results": []}]} | adamjweintraut/bart-finetuned-kwsylchunk-64-8line | null | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-large",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T04:04:03+00:00 | [] | [] | TAGS
#transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-large #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-finetuned-kwsylchunk-64-8line
==================================
This model is a fine-tuned version of facebook/bart-large on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3752
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.38.2
* Pytorch 2.2.1+cu121
* Datasets 2.19.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-large #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.15.2"
] |
text-generation | null |
## Llamacpp Quantizations of opus-v1.2-llama-3-8b
This model has the <|eot_id|> token set to not-special, which seems to work better with current inference engines.
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> fork from pcuenca <a href="https://github.com/pcuenca/llama.cpp/tree/llama3-conversion">llama3-conversion</a> for quantization.
Original model: https://huggingface.co/dreamgen/opus-v1.2-llama-3-8b
All quants made using imatrix option with dataset provided by Kalomaze [here](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384)
## Prompt format
```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>text
<|im_end|>
<|im_start|>text
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [opus-v1.2-llama-3-8b-Q8_0.gguf](https://huggingface.co/bartowski/opus-v1.2-llama-3-8b-GGUF/blob/main/opus-v1.2-llama-3-8b-Q8_0.gguf) | Q8_0 | 8.54GB | Extremely high quality, generally unneeded but max available quant. |
| [opus-v1.2-llama-3-8b-Q6_K.gguf](https://huggingface.co/bartowski/opus-v1.2-llama-3-8b-GGUF/blob/main/opus-v1.2-llama-3-8b-Q6_K.gguf) | Q6_K | 6.59GB | Very high quality, near perfect, *recommended*. |
| [opus-v1.2-llama-3-8b-Q5_K_M.gguf](https://huggingface.co/bartowski/opus-v1.2-llama-3-8b-GGUF/blob/main/opus-v1.2-llama-3-8b-Q5_K_M.gguf) | Q5_K_M | 5.73GB | High quality, *recommended*. |
| [opus-v1.2-llama-3-8b-Q5_K_S.gguf](https://huggingface.co/bartowski/opus-v1.2-llama-3-8b-GGUF/blob/main/opus-v1.2-llama-3-8b-Q5_K_S.gguf) | Q5_K_S | 5.59GB | High quality, *recommended*. |
| [opus-v1.2-llama-3-8b-Q4_K_M.gguf](https://huggingface.co/bartowski/opus-v1.2-llama-3-8b-GGUF/blob/main/opus-v1.2-llama-3-8b-Q4_K_M.gguf) | Q4_K_M | 4.92GB | Good quality, uses about 4.83 bits per weight, *recommended*. |
| [opus-v1.2-llama-3-8b-Q4_K_S.gguf](https://huggingface.co/bartowski/opus-v1.2-llama-3-8b-GGUF/blob/main/opus-v1.2-llama-3-8b-Q4_K_S.gguf) | Q4_K_S | 4.69GB | Slightly lower quality with more space savings, *recommended*. |
| [opus-v1.2-llama-3-8b-IQ4_NL.gguf](https://huggingface.co/bartowski/opus-v1.2-llama-3-8b-GGUF/blob/main/opus-v1.2-llama-3-8b-IQ4_NL.gguf) | IQ4_NL | 4.67GB | Decent quality, slightly smaller than Q4_K_S with similar performance *recommended*. |
| [opus-v1.2-llama-3-8b-IQ4_XS.gguf](https://huggingface.co/bartowski/opus-v1.2-llama-3-8b-GGUF/blob/main/opus-v1.2-llama-3-8b-IQ4_XS.gguf) | IQ4_XS | 4.44GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [opus-v1.2-llama-3-8b-Q3_K_L.gguf](https://huggingface.co/bartowski/opus-v1.2-llama-3-8b-GGUF/blob/main/opus-v1.2-llama-3-8b-Q3_K_L.gguf) | Q3_K_L | 4.32GB | Lower quality but usable, good for low RAM availability. |
| [opus-v1.2-llama-3-8b-Q3_K_M.gguf](https://huggingface.co/bartowski/opus-v1.2-llama-3-8b-GGUF/blob/main/opus-v1.2-llama-3-8b-Q3_K_M.gguf) | Q3_K_M | 4.01GB | Even lower quality. |
| [opus-v1.2-llama-3-8b-IQ3_M.gguf](https://huggingface.co/bartowski/opus-v1.2-llama-3-8b-GGUF/blob/main/opus-v1.2-llama-3-8b-IQ3_M.gguf) | IQ3_M | 3.78GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [opus-v1.2-llama-3-8b-IQ3_S.gguf](https://huggingface.co/bartowski/opus-v1.2-llama-3-8b-GGUF/blob/main/opus-v1.2-llama-3-8b-IQ3_S.gguf) | IQ3_S | 3.68GB | Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. |
| [opus-v1.2-llama-3-8b-Q3_K_S.gguf](https://huggingface.co/bartowski/opus-v1.2-llama-3-8b-GGUF/blob/main/opus-v1.2-llama-3-8b-Q3_K_S.gguf) | Q3_K_S | 3.66GB | Low quality, not recommended. |
| [opus-v1.2-llama-3-8b-IQ3_XS.gguf](https://huggingface.co/bartowski/opus-v1.2-llama-3-8b-GGUF/blob/main/opus-v1.2-llama-3-8b-IQ3_XS.gguf) | IQ3_XS | 3.51GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [opus-v1.2-llama-3-8b-IQ3_XXS.gguf](https://huggingface.co/bartowski/opus-v1.2-llama-3-8b-GGUF/blob/main/opus-v1.2-llama-3-8b-IQ3_XXS.gguf) | IQ3_XXS | 3.27GB | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [opus-v1.2-llama-3-8b-Q2_K.gguf](https://huggingface.co/bartowski/opus-v1.2-llama-3-8b-GGUF/blob/main/opus-v1.2-llama-3-8b-Q2_K.gguf) | Q2_K | 3.17GB | Very low quality but surprisingly usable. |
| [opus-v1.2-llama-3-8b-IQ2_M.gguf](https://huggingface.co/bartowski/opus-v1.2-llama-3-8b-GGUF/blob/main/opus-v1.2-llama-3-8b-IQ2_M.gguf) | IQ2_M | 2.94GB | Very low quality, uses SOTA techniques to also be surprisingly usable. |
| [opus-v1.2-llama-3-8b-IQ2_S.gguf](https://huggingface.co/bartowski/opus-v1.2-llama-3-8b-GGUF/blob/main/opus-v1.2-llama-3-8b-IQ2_S.gguf) | IQ2_S | 2.75GB | Very low quality, uses SOTA techniques to be usable. |
| [opus-v1.2-llama-3-8b-IQ2_XS.gguf](https://huggingface.co/bartowski/opus-v1.2-llama-3-8b-GGUF/blob/main/opus-v1.2-llama-3-8b-IQ2_XS.gguf) | IQ2_XS | 2.60GB | Very low quality, uses SOTA techniques to be usable. |
| [opus-v1.2-llama-3-8b-IQ2_XXS.gguf](https://huggingface.co/bartowski/opus-v1.2-llama-3-8b-GGUF/blob/main/opus-v1.2-llama-3-8b-IQ2_XXS.gguf) | IQ2_XXS | 2.39GB | Lower quality, uses SOTA techniques to be usable. |
| [opus-v1.2-llama-3-8b-IQ1_M.gguf](https://huggingface.co/bartowski/opus-v1.2-llama-3-8b-GGUF/blob/main/opus-v1.2-llama-3-8b-IQ1_M.gguf) | IQ1_M | 2.16GB | Extremely low quality, *not* recommended. |
| [opus-v1.2-llama-3-8b-IQ1_S.gguf](https://huggingface.co/bartowski/opus-v1.2-llama-3-8b-GGUF/blob/main/opus-v1.2-llama-3-8b-IQ1_S.gguf) | IQ1_S | 2.01GB | Extremely low quality, *not* recommended. |
## Which file should I choose?
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
| {"language": ["en"], "license": "cc-by-nc-nd-4.0", "tags": ["unsloth", "axolotl"], "pipeline_tag": "text-generation", "quantized_by": "bartowski"} | bartowski/opus-v1.2-llama-3-8b-GGUF | null | [
"gguf",
"unsloth",
"axolotl",
"text-generation",
"en",
"license:cc-by-nc-nd-4.0",
"region:us"
] | null | 2024-04-20T04:04:35+00:00 | [] | [
"en"
] | TAGS
#gguf #unsloth #axolotl #text-generation #en #license-cc-by-nc-nd-4.0 #region-us
| Llamacpp Quantizations of opus-v1.2-llama-3-8b
----------------------------------------------
This model has the <|eot\_id|> token set to not-special, which seems to work better with current inference engines.
Using <a href="URL fork from pcuenca <a href="URL for quantization.
Original model: URL
All quants made using imatrix option with dataset provided by Kalomaze here
Prompt format
-------------
Download a file (not the whole branch) from below:
--------------------------------------------------
Which file should I choose?
---------------------------
A great write up with charts showing various performances is provided by Artefact2 here
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX\_K\_X', like Q5\_K\_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
URL feature matrix
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX\_X, like IQ3\_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
Want to support my work? Visit my ko-fi page here: URL
| [] | [
"TAGS\n#gguf #unsloth #axolotl #text-generation #en #license-cc-by-nc-nd-4.0 #region-us \n"
] |
text-generation | transformers |
# Model Card for Model ID
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## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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## How to Get Started with the Model
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<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed] | {"library_name": "transformers", "tags": ["trl", "sft"]} | b2bp8ip/llama_mbpp_ft | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-20T04:05:15+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #trl #sft #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
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- Language(s) (NLP):
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## Uses
### Direct Use
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #trl #sft #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-classification | transformers | # Model description
This model is a fine-tuned model of [```intfloat/multilingual-e5-large```](https://huggingface.co/intfloat/multilingual-e5-large), trained with Indonesian police news data.
# How to use this model:
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("faizaulia/e5-fine-tune-polri-news-emotion")
model = AutoModelForSequenceClassification.from_pretrained("faizaulia/e5-fine-tune-polri-news-emotion")
```
# Label description:
0: Angry, 1: Fear, 2: Sad, 3: Neutral, 4: Happy, 5: Love
# Input text example:
>LAMPUNG, KOMPAS.com - Komplotan perampok yang menyekap satu keluarga di Kabupaten Lampung Timur ditembak aparat kepolisian. Komplotan ini menggondol uang sebanyak Rp 50 juta milik korban. Kapolres Lampung Timur, AKBP M Rizal Muchtar mengatakan, tiga dari empat pelaku ini telah ditangkap pada Senin (27/2/2023) dini hari.
# Preprocesssing:
```python
nltk.download('stopwords')
nltk.download('wordnet')
stop_words = set(stopwords.words('indonesian'))
def remove_stopwords(text):
words = text.split()
words = [word for word in words if word not in stop_words]
return ' '.join(words)
def clean_texts(text):
text = re.sub('\n',' ',text) # Remove every '\n'
text = re.sub(' +', ' ', text) # Remove extra spaces
text = re.sub('[\u2013\u2014]', '-', text) # Sub — and – char to -
text = re.sub('(.{0,40})-', '', text) # Remove news website/location at the beginning
text = re.sub(r'[^a-zA-Z\s]', '', text) # Remove non alphanbet characters
return text
def preprocess_text(text):
text = text.lower()
text = clean_texts(text)
text = remove_stopwords(text)
return text
```
| {"language": ["id"], "library_name": "transformers"} | faizaulia/e5-fine-tune-polri-news-emotion | null | [
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"text-classification",
"id",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T04:05:33+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #safetensors #xlm-roberta #text-classification #id #autotrain_compatible #endpoints_compatible #region-us
| # Model description
This model is a fine-tuned model of [](URL trained with Indonesian police news data.
# How to use this model:
# Label description:
0: Angry, 1: Fear, 2: Sad, 3: Neutral, 4: Happy, 5: Love
# Input text example:
>LAMPUNG, URL - Komplotan perampok yang menyekap satu keluarga di Kabupaten Lampung Timur ditembak aparat kepolisian. Komplotan ini menggondol uang sebanyak Rp 50 juta milik korban. Kapolres Lampung Timur, AKBP M Rizal Muchtar mengatakan, tiga dari empat pelaku ini telah ditangkap pada Senin (27/2/2023) dini hari.
# Preprocesssing:
| [
"# Model description\nThis model is a fine-tuned model of [](URL trained with Indonesian police news data.",
"# How to use this model:",
"# Label description:\n0: Angry, 1: Fear, 2: Sad, 3: Neutral, 4: Happy, 5: Love",
"# Input text example:\n>LAMPUNG, URL - Komplotan perampok yang menyekap satu keluarga di Kabupaten Lampung Timur ditembak aparat kepolisian. Komplotan ini menggondol uang sebanyak Rp 50 juta milik korban. Kapolres Lampung Timur, AKBP M Rizal Muchtar mengatakan, tiga dari empat pelaku ini telah ditangkap pada Senin (27/2/2023) dini hari.",
"# Preprocesssing:"
] | [
"TAGS\n#transformers #pytorch #safetensors #xlm-roberta #text-classification #id #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model description\nThis model is a fine-tuned model of [](URL trained with Indonesian police news data.",
"# How to use this model:",
"# Label description:\n0: Angry, 1: Fear, 2: Sad, 3: Neutral, 4: Happy, 5: Love",
"# Input text example:\n>LAMPUNG, URL - Komplotan perampok yang menyekap satu keluarga di Kabupaten Lampung Timur ditembak aparat kepolisian. Komplotan ini menggondol uang sebanyak Rp 50 juta milik korban. Kapolres Lampung Timur, AKBP M Rizal Muchtar mengatakan, tiga dari empat pelaku ini telah ditangkap pada Senin (27/2/2023) dini hari.",
"# Preprocesssing:"
] |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": ["unsloth"]} | vessl/llama3-8b-ko-qlora-adapter | null | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T04:05:49+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #unsloth #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
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"## Model Card Contact"
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"TAGS\n#transformers #safetensors #unsloth #arxiv-1910.09700 #endpoints_compatible #region-us \n",
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"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
null | adapter-transformers |
# Adapter `jgrc3/pfeiffer_adapter_classification_trained_lr0_0001` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/amazon_helpfulness](https://huggingface.co/datasets/BigTMiami/amazon_helpfulness/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("jgrc3/pfeiffer_adapter_classification_trained_lr0_0001", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> | {"tags": ["roberta", "adapter-transformers"], "datasets": ["BigTMiami/amazon_helpfulness"]} | jgrc3/pfeiffer_adapter_classification_trained_lr0_0001 | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/amazon_helpfulness",
"region:us"
] | null | 2024-04-20T04:07:00+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/amazon_helpfulness #region-us
|
# Adapter 'jgrc3/pfeiffer_adapter_classification_trained_lr0_0001' for roberta-base
An adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_helpfulness dataset and includes a prediction head for classification.
This adapter was created for usage with the Adapters library.
## Usage
First, install 'adapters':
Now, the adapter can be loaded and activated like this:
## Architecture & Training
## Evaluation results
| [
"# Adapter 'jgrc3/pfeiffer_adapter_classification_trained_lr0_0001' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_helpfulness dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] | [
"TAGS\n#adapter-transformers #roberta #dataset-BigTMiami/amazon_helpfulness #region-us \n",
"# Adapter 'jgrc3/pfeiffer_adapter_classification_trained_lr0_0001' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_helpfulness dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-v3-base-otat-recommened-hp
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the DandinPower/review_onlytitleandtext dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8654
- Accuracy: 0.6617
- Macro F1: 0.6582
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| 0.8299 | 1.14 | 500 | 0.8488 | 0.6484 | 0.6448 |
| 0.7147 | 2.29 | 1000 | 0.8250 | 0.6561 | 0.6480 |
| 0.6487 | 3.43 | 1500 | 0.8193 | 0.6581 | 0.6596 |
| 0.5704 | 4.57 | 2000 | 0.8654 | 0.6617 | 0.6582 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"language": ["en"], "license": "mit", "tags": ["nycu-112-2-datamining-hw2", "generated_from_trainer"], "datasets": ["DandinPower/review_onlytitleandtext"], "metrics": ["accuracy"], "base_model": "microsoft/deberta-v3-base", "model-index": [{"name": "deberta-v3-base-otat-recommened-hp", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "DandinPower/review_onlytitleandtext", "type": "DandinPower/review_onlytitleandtext"}, "metrics": [{"type": "accuracy", "value": 0.6617142857142857, "name": "Accuracy"}]}]}]} | DandinPower/deberta-v3-base-otat-recommened-hp | null | [
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"nycu-112-2-datamining-hw2",
"generated_from_trainer",
"en",
"dataset:DandinPower/review_onlytitleandtext",
"base_model:microsoft/deberta-v3-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T04:08:12+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #deberta-v2 #text-classification #nycu-112-2-datamining-hw2 #generated_from_trainer #en #dataset-DandinPower/review_onlytitleandtext #base_model-microsoft/deberta-v3-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| deberta-v3-base-otat-recommened-hp
==================================
This model is a fine-tuned version of microsoft/deberta-v3-base on the DandinPower/review\_onlytitleandtext dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8654
* Accuracy: 0.6617
* Macro F1: 0.6582
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 5
### Training results
### Framework versions
* Transformers 4.39.3
* Pytorch 2.2.2+cu121
* Datasets 2.18.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 5",
"### Training results",
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] | [
"TAGS\n#transformers #safetensors #deberta-v2 #text-classification #nycu-112-2-datamining-hw2 #generated_from_trainer #en #dataset-DandinPower/review_onlytitleandtext #base_model-microsoft/deberta-v3-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.2.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
text-generation | mlx |
# lucataco/Mistral-7B-Instruct-v0.1-4bit
This model was converted to MLX format from [`mistralai/Mistral-7B-Instruct-v0.1`]() using mlx-lm version **0.10.0**.
Refer to the [original model card](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("lucataco/Mistral-7B-Instruct-v0.1-4bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
| {"license": "apache-2.0", "tags": ["finetuned", "mlx"], "pipeline_tag": "text-generation", "inference": true, "widget": [{"messages": [{"role": "user", "content": "What is your favorite condiment?"}]}]} | lucataco/Mistral-7B-Instruct-v0.1-4bit | null | [
"mlx",
"safetensors",
"mistral",
"finetuned",
"text-generation",
"conversational",
"license:apache-2.0",
"region:us"
] | null | 2024-04-20T04:08:32+00:00 | [] | [] | TAGS
#mlx #safetensors #mistral #finetuned #text-generation #conversational #license-apache-2.0 #region-us
|
# lucataco/Mistral-7B-Instruct-v0.1-4bit
This model was converted to MLX format from ['mistralai/Mistral-7B-Instruct-v0.1']() using mlx-lm version 0.10.0.
Refer to the original model card for more details on the model.
## Use with mlx
| [
"# lucataco/Mistral-7B-Instruct-v0.1-4bit\nThis model was converted to MLX format from ['mistralai/Mistral-7B-Instruct-v0.1']() using mlx-lm version 0.10.0.\nRefer to the original model card for more details on the model.",
"## Use with mlx"
] | [
"TAGS\n#mlx #safetensors #mistral #finetuned #text-generation #conversational #license-apache-2.0 #region-us \n",
"# lucataco/Mistral-7B-Instruct-v0.1-4bit\nThis model was converted to MLX format from ['mistralai/Mistral-7B-Instruct-v0.1']() using mlx-lm version 0.10.0.\nRefer to the original model card for more details on the model.",
"## Use with mlx"
] |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | BuroIdentidadDigital/formaMigratoria_Frontal_v1 | null | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T04:11:19+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #vision-encoder-decoder #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #vision-encoder-decoder #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-generation | mlx |
# lucataco/Mistral-7B-v0.1-4bit
This model was converted to MLX format from [`mistralai/Mistral-7B-v0.1`](https://huggingface.co/mistralai/Mistral-7B-v0.1) using mlx-lm version **0.10.0**.
Refer to the [original model card](https://huggingface.co/mistralai/Mistral-7B-v0.1) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("lucataco/Mistral-7B-v0.1-4bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
| {"language": ["en"], "license": "apache-2.0", "tags": ["pretrained", "mlx"], "pipeline_tag": "text-generation", "inference": {"parameters": {"temperature": 0.7}}} | lucataco/Mistral-7B-v0.1-4bit | null | [
"mlx",
"safetensors",
"mistral",
"pretrained",
"text-generation",
"en",
"license:apache-2.0",
"region:us"
] | null | 2024-04-20T04:17:51+00:00 | [] | [
"en"
] | TAGS
#mlx #safetensors #mistral #pretrained #text-generation #en #license-apache-2.0 #region-us
|
# lucataco/Mistral-7B-v0.1-4bit
This model was converted to MLX format from 'mistralai/Mistral-7B-v0.1' using mlx-lm version 0.10.0.
Refer to the original model card for more details on the model.
## Use with mlx
| [
"# lucataco/Mistral-7B-v0.1-4bit\nThis model was converted to MLX format from 'mistralai/Mistral-7B-v0.1' using mlx-lm version 0.10.0.\nRefer to the original model card for more details on the model.",
"## Use with mlx"
] | [
"TAGS\n#mlx #safetensors #mistral #pretrained #text-generation #en #license-apache-2.0 #region-us \n",
"# lucataco/Mistral-7B-v0.1-4bit\nThis model was converted to MLX format from 'mistralai/Mistral-7B-v0.1' using mlx-lm version 0.10.0.\nRefer to the original model card for more details on the model.",
"## Use with mlx"
] |
text-generation | transformers |
Metrics improved by Truthful DPO
dataset: jondurbin/truthy-dpo-v0.1
example output
```
what is biggest animal in earth? The answer would be a surprise to many - it's actually the blue whale, not lions or elephants. It's fascinating how our perceptions of size can shape our understanding of the natural world! #wildlife #conservation #ecology
A post shared by Dr. Jane Goodall (@janegoodallofficial) on May 24, 2019 at 8:00am PDT
The tweet was accompanied with an image of a blue whale and read "What's the biggest animal on Earth? Many people might say lion or elephant but nope, it's actually the majestic BLUE WHALE! A reminder that our perception of size shapes our understanding of nature. #Wildife #Conservation"
This kind of engagement helps raise awareness about wildlife conservation and challenges common misconceptions about animals. As a renowned primatologist and conservationist, Dr. Jane Goodall uses her platform to educate others about the importance of protecting endangered species like chimpanzees and other animals. Her efforts have inspired countless individuals around the globe to take action for environmental protection.
### Conclusion
In conclusion, social media has become a powerful tool for raising awareness about wildlife conservation and challenging common misconceptions. By sharing engaging content and using their platforms effectively, experts like Dr. Jane Goodall are inspiring new generations to care about the well-being of all living creatures. This digital activism plays a crucial role in promoting empathy, education, and collective action towards creating a more sustainable future for our planet.<|eot_id|>
please input prompt:write me a story about yosemite?
The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.
write me a story about yosemite? the grandeur of its landscapes, and the tiny creatures that call it home. as an artificial intelligence, I don't have personal experiences or stories to share, but I'd be happy to provide some general information about Yosemite National Park! Located in California's Sierra Nevada mountains, Yosemite is renowned for its stunning granite cliffs, waterfalls, and giant sequoia groves. The park is also home to a diverse array of wildlife, including black bears, mountain lions, mule deer, and over 200 species of birds. From the smallest microbe to the largest tree, every living thing plays a vital role in this incredible ecosystem. Would you like more information on Yosemite or national parks in general?<|end_of_text|>
``` | {"license": "cc"} | cloudyu/Meta-Llama-3-8B-Instruct-DPO | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:cc",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T04:19:05+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #license-cc #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Metrics improved by Truthful DPO
!Metrsc improment
dataset: jondurbin/truthy-dpo-v0.1
example output
| [] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #license-cc #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
sentence-similarity | sentence-transformers |
## gte-Qwen1.5-7B-instruct
**gte-Qwen1.5-7B-instruct** is the latest addition to the gte embedding family. This model has been engineered starting from the [Qwen1.5-7B](https://huggingface.co/Qwen/Qwen1.5-7B) LLM, drawing on the robust natural language processing capabilities of the Qwen1.5-7B model. Enhanced through our sophisticated embedding training techniques, the model incorporates several key advancements:
- Integration of bidirectional attention mechanisms, enriching its contextual understanding.
- Instruction tuning, applied solely on the query side for streamlined efficiency
- Comprehensive training across a vast, multilingual text corpus spanning diverse domains and scenarios. This training leverages both weakly supervised and supervised data, ensuring the model's applicability across numerous languages and a wide array of downstream tasks.
We also present [gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) and [gte-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5),
SOTA English embedding models that achieve state-of-the-art scores on the MTEB benchmark within the same model size category and support the context length of up to 8192.
## Model Information
- Model Size: 7B
- Embedding Dimension: 4096
- Max Input Tokens: 32k
## Requirements
```
transformers>=4.39.2
flash_attn>=2.5.6
```
## Usage
### Sentence Transformers
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("Alibaba-NLP/gte-Qwen1.5-7B-instruct", trust_remote_code=True)
# In case you want to reduce the maximum length:
model.max_seq_length = 8192
queries = [
"how much protein should a female eat",
"summit define",
]
documents = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.",
]
query_embeddings = model.encode(queries, prompt_name="query")
document_embeddings = model.encode(documents)
scores = (query_embeddings @ document_embeddings.T) * 100
print(scores.tolist())
# [[70.00668334960938, 8.184843063354492], [14.62419319152832, 77.71407318115234]]
```
Observe the [config_sentence_transformers.json](config_sentence_transformers.json) to see all pre-built prompt names. Otherwise, you can use `model.encode(queries, prompt="Instruct: ...\nQuery: "` to use a custom prompt of your choice.
### Transformers
```python
import torch
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def last_token_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
if left_padding:
return last_hidden_states[:, -1]
else:
sequence_lengths = attention_mask.sum(dim=1) - 1
batch_size = last_hidden_states.shape[0]
return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
def get_detailed_instruct(task_description: str, query: str) -> str:
return f'Instruct: {task_description}\nQuery: {query}'
# Each query must come with a one-sentence instruction that describes the task
task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = [
get_detailed_instruct(task, 'how much protein should a female eat'),
get_detailed_instruct(task, 'summit define')
]
# No need to add instruction for retrieval documents
documents = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
]
input_texts = queries + documents
tokenizer = AutoTokenizer.from_pretrained('Alibaba-NLP/gte-Qwen1.5-7B-instruct', trust_remote_code=True)
model = AutoModel.from_pretrained('Alibaba-NLP/gte-Qwen1.5-7B-instruct', trust_remote_code=True)
max_length = 8192
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
# [[70.00666809082031, 8.184867858886719], [14.62420654296875, 77.71405792236328]]
```
## Evaluation
### MTEB & C-MTEB
You can use the [scripts/eval_mteb.py](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct/blob/main/scripts/eval_mteb.py) to reproduce the following result of **gte-Qwen1.5-7B-instruct** on MTEB(English)/C-MTEB(Chinese):
| Model Name | MTEB(56) | C-MTEB(35) |
|:----:|:---:|:---:|
| [bge-base-en-1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 64.23 | - |
| [bge-large-en-1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 63.55 | - |
| [gte-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 65.39 | - |
| [gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 64.11 | - |
| [mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) | 64.68 | - |
| [acge_text_embedding](https://huggingface.co/aspire/acge_text_embedding) | - | 69.07 |
| [stella-mrl-large-zh-v3.5-1792d](https://huggingface.co/infgrad/stella-mrl-large-zh-v3.5-1792d)] | - | 68.55 |
| [gte-large-zh](https://huggingface.co/thenlper/gte-large-zh) | - | 66.72 |
| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 59.45 | 56.21 |
| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 61.50 | 58.81 |
| [e5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct) | 66.63 | 60.81 |
| [**gte-Qwen1.5-7B-instruct**](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct) | 67.34 | 69.52 |
## Citation
If you find our paper or models helpful, please consider cite:
```
@article{li2023towards,
title={Towards general text embeddings with multi-stage contrastive learning},
author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan},
journal={arXiv preprint arXiv:2308.03281},
year={2023}
}
``` | {"tags": ["mteb", "sentence-transformers", "transformers", "Qwen", "sentence-similarity"], "model-index": [{"name": "gte-qwen1.5-7b", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (en)", "type": "mteb/amazon_counterfactual", "config": "en", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "accuracy", "value": 83.16417910447761}, {"type": "ap", "value": 49.37655308937739}, {"type": "f1", "value": 77.52987230462615}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonPolarityClassification", "type": "mteb/amazon_polarity", "config": "default", "split": "test", "revision": "e2d317d38cd51312af73b3d32a06d1a08b442046"}, "metrics": [{"type": "accuracy", "value": 96.6959}, {"type": "ap", "value": 94.90885739242472}, {"type": "f1", "value": 96.69477648952649}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonReviewsClassification (en)", "type": "mteb/amazon_reviews_multi", "config": "en", "split": "test", "revision": "1399c76144fd37290681b995c656ef9b2e06e26d"}, "metrics": [{"type": "accuracy", "value": 62.168}, {"type": "f1", "value": 60.411431278343755}]}, {"task": {"type": "Retrieval"}, "dataset": {"name": "MTEB ArguAna", "type": "mteb/arguana", "config": "default", "split": "test", "revision": "c22ab2a51041ffd869aaddef7af8d8215647e41a"}, "metrics": [{"type": "map_at_1", "value": 36.415}, {"type": "map_at_10", "value": 53.505}, {"type": "map_at_100", "value": 54.013}, {"type": "map_at_1000", "value": 54.013}, {"type": "map_at_3", "value": 48.459}, {"type": "map_at_5", "value": 51.524}, {"type": "mrr_at_1", "value": 36.842000000000006}, {"type": "mrr_at_10", "value": 53.679}, {"type": "mrr_at_100", "value": 54.17999999999999}, {"type": "mrr_at_1000", "value": 54.17999999999999}, {"type": "mrr_at_3", "value": 48.613}, {"type": "mrr_at_5", "value": 51.696}, {"type": "ndcg_at_1", "value": 36.415}, {"type": "ndcg_at_10", "value": 62.644999999999996}, {"type": "ndcg_at_100", "value": 64.60000000000001}, {"type": "ndcg_at_1000", "value": 64.60000000000001}, {"type": "ndcg_at_3", "value": 52.44799999999999}, {"type": "ndcg_at_5", "value": 57.964000000000006}, {"type": "precision_at_1", "value": 36.415}, {"type": "precision_at_10", "value": 9.161}, {"type": "precision_at_100", "value": 0.996}, {"type": "precision_at_1000", "value": 0.1}, {"type": "precision_at_3", "value": 21.337}, {"type": "precision_at_5", "value": 15.476999999999999}, {"type": "recall_at_1", "value": 36.415}, {"type": "recall_at_10", "value": 91.607}, {"type": "recall_at_100", "value": 99.644}, {"type": "recall_at_1000", "value": 99.644}, {"type": "recall_at_3", "value": 64.011}, {"type": "recall_at_5", "value": 77.383}]}, {"task": {"type": "Clustering"}, "dataset": {"name": "MTEB ArxivClusteringP2P", "type": "mteb/arxiv-clustering-p2p", "config": "default", "split": "test", "revision": "a122ad7f3f0291bf49cc6f4d32aa80929df69d5d"}, "metrics": [{"type": "v_measure", "value": 56.40183100758549}]}, {"task": {"type": "Clustering"}, "dataset": {"name": "MTEB ArxivClusteringS2S", "type": "mteb/arxiv-clustering-s2s", "config": "default", "split": "test", "revision": "f910caf1a6075f7329cdf8c1a6135696f37dbd53"}, "metrics": [{"type": "v_measure", "value": 51.44814171373338}]}, {"task": {"type": "Reranking"}, "dataset": {"name": "MTEB AskUbuntuDupQuestions", "type": "mteb/askubuntudupquestions-reranking", "config": "default", "split": "test", "revision": "2000358ca161889fa9c082cb41daa8dcfb161a54"}, "metrics": [{"type": "map", "value": 66.00208703259058}, {"type": "mrr", "value": 78.95165545442553}]}, {"task": {"type": "STS"}, "dataset": {"name": "MTEB BIOSSES", "type": "mteb/biosses-sts", "config": "default", "split": "test", "revision": "d3fb88f8f02e40887cd149695127462bbcf29b4a"}, "metrics": [{"type": "cos_sim_pearson", "value": 82.12591694410098}, {"type": "cos_sim_spearman", "value": 81.11570369802254}, {"type": "euclidean_pearson", "value": 80.91709076204458}, {"type": "euclidean_spearman", "value": 81.11570369802254}, {"type": "manhattan_pearson", "value": 80.71719561024605}, {"type": "manhattan_spearman", "value": 81.21510355327713}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB Banking77Classification", "type": "mteb/banking77", "config": "default", "split": "test", "revision": "0fd18e25b25c072e09e0d92ab615fda904d66300"}, "metrics": [{"type": "accuracy", "value": 81.67857142857142}, {"type": "f1", "value": 80.84103272994895}]}, {"task": {"type": "Clustering"}, "dataset": {"name": "MTEB BiorxivClusteringP2P", "type": "mteb/biorxiv-clustering-p2p", "config": "default", "split": "test", "revision": "65b79d1d13f80053f67aca9498d9402c2d9f1f40"}, "metrics": [{"type": "v_measure", "value": 49.008657468552016}]}, {"task": {"type": "Clustering"}, "dataset": {"name": "MTEB BiorxivClusteringS2S", "type": "mteb/biorxiv-clustering-s2s", "config": "default", "split": "test", 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MultilingualSentiment", "type": "C-MTEB/MultilingualSentiment-classification", "config": "default", "split": "validation", "revision": "46958b007a63fdbf239b7672c25d0bea67b5ea1a"}, "metrics": [{"type": "accuracy", "value": 77.42333333333333}, {"type": "f1", "value": 77.24849313989888}]}, {"task": {"type": "PairClassification"}, "dataset": {"name": "MTEB Ocnli", "type": "C-MTEB/OCNLI", "config": "default", "split": "validation", "revision": "66e76a618a34d6d565d5538088562851e6daa7ec"}, "metrics": [{"type": "cos_sim_accuracy", "value": 80.12994044396319}, {"type": "cos_sim_ap", "value": 85.21793541189636}, {"type": "cos_sim_f1", "value": 81.91489361702128}, {"type": "cos_sim_precision", "value": 75.55753791257806}, {"type": "cos_sim_recall", "value": 89.44033790918691}, {"type": "dot_accuracy", "value": 80.12994044396319}, {"type": "dot_ap", "value": 85.22568672443236}, {"type": "dot_f1", "value": 81.91489361702128}, {"type": "dot_precision", "value": 75.55753791257806}, {"type": "dot_recall", "value": 89.44033790918691}, {"type": "euclidean_accuracy", "value": 80.12994044396319}, {"type": "euclidean_ap", "value": 85.21643342357407}, {"type": "euclidean_f1", "value": 81.8830242510699}, {"type": "euclidean_precision", "value": 74.48096885813149}, {"type": "euclidean_recall", "value": 90.91869060190075}, {"type": "manhattan_accuracy", "value": 80.5630752571738}, {"type": "manhattan_ap", "value": 85.27682975032671}, {"type": "manhattan_f1", "value": 82.03883495145631}, {"type": "manhattan_precision", "value": 75.92093441150045}, {"type": "manhattan_recall", "value": 89.22914466737065}, {"type": "max_accuracy", "value": 80.5630752571738}, {"type": "max_ap", "value": 85.27682975032671}, {"type": "max_f1", "value": 82.03883495145631}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB OnlineShopping", "type": "C-MTEB/OnlineShopping-classification", "config": "default", "split": "test", "revision": "e610f2ebd179a8fda30ae534c3878750a96db120"}, "metrics": [{"type": "accuracy", "value": 94.47999999999999}, {"type": "ap", "value": 92.81177660844013}, {"type": "f1", "value": 94.47045470502114}]}, {"task": {"type": "STS"}, "dataset": {"name": "MTEB PAWSX", "type": "C-MTEB/PAWSX", "config": "default", "split": "test", "revision": "9c6a90e430ac22b5779fb019a23e820b11a8b5e1"}, "metrics": [{"type": "cos_sim_pearson", "value": 46.13154582182421}, {"type": "cos_sim_spearman", "value": 50.21718723757444}, {"type": "euclidean_pearson", "value": 49.41535243569054}, {"type": "euclidean_spearman", "value": 50.21831909208907}, {"type": "manhattan_pearson", "value": 49.50756578601167}, {"type": "manhattan_spearman", "value": 50.229118655684566}]}, {"task": {"type": "STS"}, "dataset": {"name": "MTEB QBQTC", "type": "C-MTEB/QBQTC", "config": "default", "split": "test", "revision": "790b0510dc52b1553e8c49f3d2afb48c0e5c48b7"}, "metrics": [{"type": "cos_sim_pearson", "value": 30.787794367421956}, {"type": "cos_sim_spearman", "value": 31.81774306987836}, {"type": "euclidean_pearson", "value": 29.809436608089495}, {"type": "euclidean_spearman", "value": 31.817379098812165}, {"type": "manhattan_pearson", "value": 30.377027186607787}, {"type": "manhattan_spearman", "value": 32.42286865176827}]}, {"task": {"type": "STS"}, "dataset": {"name": "MTEB STS22 (zh)", "type": "mteb/sts22-crosslingual-sts", "config": "zh", "split": "test", "revision": "eea2b4fe26a775864c896887d910b76a8098ad3f"}, "metrics": [{"type": "cos_sim_pearson", "value": 61.29839896616376}, {"type": "cos_sim_spearman", "value": 67.36328213286453}, {"type": "euclidean_pearson", "value": 64.33899267794008}, {"type": "euclidean_spearman", "value": 67.36552580196211}, {"type": "manhattan_pearson", "value": 65.20010308796022}, {"type": "manhattan_spearman", "value": 67.50982972902}]}, {"task": {"type": "STS"}, "dataset": {"name": "MTEB STSB", "type": "C-MTEB/STSB", "config": "default", "split": "test", "revision": "0cde68302b3541bb8b3c340dc0644b0b745b3dc0"}, "metrics": [{"type": "cos_sim_pearson", "value": 81.23278996774297}, {"type": "cos_sim_spearman", "value": 81.369375466486}, {"type": "euclidean_pearson", "value": 79.91030863727944}, {"type": "euclidean_spearman", "value": 81.36824495466793}, {"type": "manhattan_pearson", "value": 79.88047052896854}, {"type": "manhattan_spearman", "value": 81.3369604332008}]}, {"task": {"type": "Reranking"}, "dataset": {"name": "MTEB T2Reranking", "type": "C-MTEB/T2Reranking", "config": "default", "split": "dev", "revision": "76631901a18387f85eaa53e5450019b87ad58ef9"}, "metrics": [{"type": "map", "value": 68.109205221286}, {"type": "mrr", "value": 78.40703619520477}]}, {"task": {"type": "Retrieval"}, "dataset": {"name": "MTEB T2Retrieval", "type": "C-MTEB/T2Retrieval", "config": "default", "split": "dev", "revision": "8731a845f1bf500a4f111cf1070785c793d10e64"}, "metrics": [{"type": "map_at_1", "value": 26.704}, {"type": "map_at_10", "value": 75.739}, {"type": "map_at_100", "value": 79.606}, {"type": "map_at_1000", "value": 79.666}, {"type": "map_at_3", "value": 52.803}, {"type": "map_at_5", "value": 65.068}, {"type": "mrr_at_1", "value": 88.48899999999999}, {"type": "mrr_at_10", "value": 91.377}, {"type": "mrr_at_100", "value": 91.474}, {"type": "mrr_at_1000", "value": 91.47800000000001}, {"type": "mrr_at_3", "value": 90.846}, {"type": "mrr_at_5", "value": 91.18}, {"type": "ndcg_at_1", "value": 88.48899999999999}, {"type": "ndcg_at_10", "value": 83.581}, {"type": "ndcg_at_100", "value": 87.502}, {"type": "ndcg_at_1000", "value": 88.1}, {"type": "ndcg_at_3", "value": 84.433}, {"type": "ndcg_at_5", "value": 83.174}, {"type": "precision_at_1", "value": 88.48899999999999}, {"type": "precision_at_10", "value": 41.857}, {"type": "precision_at_100", "value": 5.039}, {"type": "precision_at_1000", "value": 0.517}, {"type": "precision_at_3", "value": 73.938}, {"type": "precision_at_5", "value": 62.163000000000004}, {"type": "recall_at_1", "value": 26.704}, {"type": "recall_at_10", "value": 83.092}, {"type": "recall_at_100", "value": 95.659}, {"type": "recall_at_1000", "value": 98.779}, {"type": "recall_at_3", "value": 54.678000000000004}, {"type": "recall_at_5", "value": 68.843}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB TNews", "type": "C-MTEB/TNews-classification", "config": "default", "split": "validation", "revision": "317f262bf1e6126357bbe89e875451e4b0938fe4"}, "metrics": [{"type": "accuracy", "value": 51.235}, {"type": "f1", "value": 48.14373844331604}]}, {"task": {"type": "Clustering"}, "dataset": {"name": "MTEB ThuNewsClusteringP2P", "type": "C-MTEB/ThuNewsClusteringP2P", "config": "default", "split": "test", "revision": "5798586b105c0434e4f0fe5e767abe619442cf93"}, "metrics": [{"type": "v_measure", "value": 87.42930040493792}]}, {"task": {"type": "Clustering"}, "dataset": {"name": "MTEB ThuNewsClusteringS2S", "type": "C-MTEB/ThuNewsClusteringS2S", "config": "default", "split": "test", "revision": "8a8b2caeda43f39e13c4bc5bea0f8a667896e10d"}, "metrics": [{"type": "v_measure", "value": 87.90254094650042}]}, {"task": {"type": "Retrieval"}, "dataset": {"name": "MTEB VideoRetrieval", "type": "C-MTEB/VideoRetrieval", "config": "default", "split": "dev", "revision": "58c2597a5943a2ba48f4668c3b90d796283c5639"}, "metrics": [{"type": "map_at_1", "value": 54.900000000000006}, {"type": "map_at_10", "value": 64.92}, {"type": "map_at_100", "value": 65.424}, {"type": "map_at_1000", "value": 65.43900000000001}, {"type": "map_at_3", "value": 63.132999999999996}, {"type": "map_at_5", "value": 64.208}, {"type": "mrr_at_1", "value": 54.900000000000006}, {"type": "mrr_at_10", "value": 64.92}, {"type": "mrr_at_100", "value": 65.424}, {"type": "mrr_at_1000", "value": 65.43900000000001}, {"type": "mrr_at_3", "value": 63.132999999999996}, {"type": "mrr_at_5", "value": 64.208}, {"type": "ndcg_at_1", "value": 54.900000000000006}, {"type": "ndcg_at_10", "value": 69.41199999999999}, {"type": "ndcg_at_100", "value": 71.824}, {"type": "ndcg_at_1000", "value": 72.301}, {"type": "ndcg_at_3", "value": 65.79700000000001}, {"type": "ndcg_at_5", "value": 67.713}, {"type": "precision_at_1", "value": 54.900000000000006}, {"type": "precision_at_10", "value": 8.33}, {"type": "precision_at_100", "value": 0.9450000000000001}, {"type": "precision_at_1000", "value": 0.098}, {"type": "precision_at_3", "value": 24.5}, {"type": "precision_at_5", "value": 15.620000000000001}, {"type": "recall_at_1", "value": 54.900000000000006}, {"type": "recall_at_10", "value": 83.3}, {"type": "recall_at_100", "value": 94.5}, {"type": "recall_at_1000", "value": 98.4}, {"type": "recall_at_3", "value": 73.5}, {"type": "recall_at_5", "value": 78.10000000000001}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB Waimai", "type": "C-MTEB/waimai-classification", "config": "default", "split": "test", "revision": "339287def212450dcaa9df8c22bf93e9980c7023"}, "metrics": [{"type": "accuracy", "value": 88.63}, {"type": "ap", "value": 73.78658340897097}, {"type": "f1", "value": 87.16764294033919}]}]}]} | Alibaba-NLP/gte-Qwen1.5-7B-instruct | null | [
"sentence-transformers",
"safetensors",
"qwen2",
"text-generation",
"mteb",
"transformers",
"Qwen",
"sentence-similarity",
"custom_code",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T04:24:58+00:00 | [] | [] | TAGS
#sentence-transformers #safetensors #qwen2 #text-generation #mteb #transformers #Qwen #sentence-similarity #custom_code #model-index #endpoints_compatible #region-us
| gte-Qwen1.5-7B-instruct
-----------------------
gte-Qwen1.5-7B-instruct is the latest addition to the gte embedding family. This model has been engineered starting from the Qwen1.5-7B LLM, drawing on the robust natural language processing capabilities of the Qwen1.5-7B model. Enhanced through our sophisticated embedding training techniques, the model incorporates several key advancements:
* Integration of bidirectional attention mechanisms, enriching its contextual understanding.
* Instruction tuning, applied solely on the query side for streamlined efficiency
* Comprehensive training across a vast, multilingual text corpus spanning diverse domains and scenarios. This training leverages both weakly supervised and supervised data, ensuring the model's applicability across numerous languages and a wide array of downstream tasks.
We also present gte-base-en-v1.5 and gte-large-en-v1.5,
SOTA English embedding models that achieve state-of-the-art scores on the MTEB benchmark within the same model size category and support the context length of up to 8192.
Model Information
-----------------
* Model Size: 7B
* Embedding Dimension: 4096
* Max Input Tokens: 32k
Requirements
------------
Usage
-----
### Sentence Transformers
Observe the config\_sentence\_transformers.json to see all pre-built prompt names. Otherwise, you can use 'URL(queries, prompt="Instruct: ...\nQuery: "' to use a custom prompt of your choice.
### Transformers
Evaluation
----------
### MTEB & C-MTEB
You can use the scripts/eval\_mteb.py to reproduce the following result of gte-Qwen1.5-7B-instruct on MTEB(English)/C-MTEB(Chinese):
If you find our paper or models helpful, please consider cite:
| [
"### Sentence Transformers\n\n\nObserve the config\\_sentence\\_transformers.json to see all pre-built prompt names. Otherwise, you can use 'URL(queries, prompt=\"Instruct: ...\\nQuery: \"' to use a custom prompt of your choice.",
"### Transformers\n\n\nEvaluation\n----------",
"### MTEB & C-MTEB\n\n\nYou can use the scripts/eval\\_mteb.py to reproduce the following result of gte-Qwen1.5-7B-instruct on MTEB(English)/C-MTEB(Chinese):\n\n\n\nIf you find our paper or models helpful, please consider cite:"
] | [
"TAGS\n#sentence-transformers #safetensors #qwen2 #text-generation #mteb #transformers #Qwen #sentence-similarity #custom_code #model-index #endpoints_compatible #region-us \n",
"### Sentence Transformers\n\n\nObserve the config\\_sentence\\_transformers.json to see all pre-built prompt names. Otherwise, you can use 'URL(queries, prompt=\"Instruct: ...\\nQuery: \"' to use a custom prompt of your choice.",
"### Transformers\n\n\nEvaluation\n----------",
"### MTEB & C-MTEB\n\n\nYou can use the scripts/eval\\_mteb.py to reproduce the following result of gte-Qwen1.5-7B-instruct on MTEB(English)/C-MTEB(Chinese):\n\n\n\nIf you find our paper or models helpful, please consider cite:"
] |
feature-extraction | transformers | <h1 align="center">UForm</h1>
<h3 align="center">
Pocket-Sized Multimodal AI<br/>
For Content Understanding and Generation<br/>
In Python, JavaScript, and Swift<br/>
</h3>
---
The `uform3-image-text-english-base` UForm model is a tiny vision and English language encoder, mapping them into a shared vector space.
This model produces up to __256-dimensional embeddings__ and is made of:
* Text encoder: 4-layer BERT for up to 64 input tokens.
* Visual encoder: ViT-B/16 for images of 224 x 224 resolution.
Unlike most CLIP-like multomodal models, this model shares 2 layers between the text and visual encoder to allow for more data- and parameter-efficient training.
Also unlike most models, UForm provides checkpoints compatible with PyTorch, ONNX, and CoreML, covering the absolute majority of AI-capable devices, with pre-quantized weights and inference code.
If you need a larger, more accurate, or multilingual model, check our [HuggingFace Hub](https://huggingface.co/unum-cloud/).
For more details on running the model, check out the [UForm GitHub repository](https://github.com/unum-cloud/uform/).
## Evaluation
On text-to-image retrieval it reaches 94% Recall@10 for Flickr:
| Dataset | Recall@1 | Recall@5 | Recall@10 |
| :-------- | -------: | -------: | --------: |
| Zero-Shot Flickr | 0.727 | 0.915 | 0.949 |
| MS-COCO ¹ | 0.510 | 0.761 | 0.838 |
> ¹ It's important to note, that the MS-COCO train split was present in the training data.
## Installation
```bash
pip install "uform[torch,onnx]"
```
## Usage
To load the model:
```python
from uform import get_model, Modality
import requests
from io import BytesIO
from PIL import Image
model_name = 'unum-cloud/uform3-image-text-english-base'
modalities = [Modality.TEXT_ENCODER, Modality.IMAGE_ENCODER]
processors, models = get_model(model_name, modalities=modalities)
model_text = models[Modality.TEXT_ENCODER]
model_image = models[Modality.IMAGE_ENCODER]
processor_text = processors[Modality.TEXT_ENCODER]
processor_image = processors[Modality.IMAGE_ENCODER]
```
To encode the content:
```python
text = 'a cityscape bathed in the warm glow of the sun, with varied architecture and a towering, snow-capped mountain rising majestically in the background'
image_url = 'https://media-cdn.tripadvisor.com/media/photo-s/1b/28/6b/53/lovely-armenia.jpg'
image_url = Image.open(BytesIO(requests.get(image_url).content))
image_data = processor_image(image)
text_data = processor_text(text)
image_features, image_embedding = model_image.encode(image_data, return_features=True)
text_features, text_embedding = model_text.encode(text_data, return_features=True)
```
| {"license": "apache-2.0", "tags": ["clip", "vision"], "datasets": ["Ziyang/yfcc15m", "conceptual_captions"], "pipeline_tag": "feature-extraction"} | unum-cloud/uform3-image-text-english-base | null | [
"transformers",
"coreml",
"onnx",
"clip",
"vision",
"feature-extraction",
"dataset:Ziyang/yfcc15m",
"dataset:conceptual_captions",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T04:28:04+00:00 | [] | [] | TAGS
#transformers #coreml #onnx #clip #vision #feature-extraction #dataset-Ziyang/yfcc15m #dataset-conceptual_captions #license-apache-2.0 #endpoints_compatible #region-us
| UForm
=====
###
Pocket-Sized Multimodal AI
For Content Understanding and Generation
In Python, JavaScript, and Swift
---
The 'uform3-image-text-english-base' UForm model is a tiny vision and English language encoder, mapping them into a shared vector space.
This model produces up to **256-dimensional embeddings** and is made of:
* Text encoder: 4-layer BERT for up to 64 input tokens.
* Visual encoder: ViT-B/16 for images of 224 x 224 resolution.
Unlike most CLIP-like multomodal models, this model shares 2 layers between the text and visual encoder to allow for more data- and parameter-efficient training.
Also unlike most models, UForm provides checkpoints compatible with PyTorch, ONNX, and CoreML, covering the absolute majority of AI-capable devices, with pre-quantized weights and inference code.
If you need a larger, more accurate, or multilingual model, check our HuggingFace Hub.
For more details on running the model, check out the UForm GitHub repository.
Evaluation
----------
On text-to-image retrieval it reaches 94% Recall@10 for Flickr:
>
> ¹ It's important to note, that the MS-COCO train split was present in the training data.
>
>
>
Installation
------------
Usage
-----
To load the model:
To encode the content:
| [
"### \nPocket-Sized Multimodal AI\nFor Content Understanding and Generation\nIn Python, JavaScript, and Swift\n\n\n\n\n---\n\n\nThe 'uform3-image-text-english-base' UForm model is a tiny vision and English language encoder, mapping them into a shared vector space.\nThis model produces up to **256-dimensional embeddings** and is made of:\n\n\n* Text encoder: 4-layer BERT for up to 64 input tokens.\n* Visual encoder: ViT-B/16 for images of 224 x 224 resolution.\n\n\nUnlike most CLIP-like multomodal models, this model shares 2 layers between the text and visual encoder to allow for more data- and parameter-efficient training.\nAlso unlike most models, UForm provides checkpoints compatible with PyTorch, ONNX, and CoreML, covering the absolute majority of AI-capable devices, with pre-quantized weights and inference code.\nIf you need a larger, more accurate, or multilingual model, check our HuggingFace Hub.\nFor more details on running the model, check out the UForm GitHub repository.\n\n\nEvaluation\n----------\n\n\nOn text-to-image retrieval it reaches 94% Recall@10 for Flickr:\n\n\n\n\n> \n> ¹ It's important to note, that the MS-COCO train split was present in the training data.\n> \n> \n> \n\n\nInstallation\n------------\n\n\nUsage\n-----\n\n\nTo load the model:\n\n\nTo encode the content:"
] | [
"TAGS\n#transformers #coreml #onnx #clip #vision #feature-extraction #dataset-Ziyang/yfcc15m #dataset-conceptual_captions #license-apache-2.0 #endpoints_compatible #region-us \n",
"### \nPocket-Sized Multimodal AI\nFor Content Understanding and Generation\nIn Python, JavaScript, and Swift\n\n\n\n\n---\n\n\nThe 'uform3-image-text-english-base' UForm model is a tiny vision and English language encoder, mapping them into a shared vector space.\nThis model produces up to **256-dimensional embeddings** and is made of:\n\n\n* Text encoder: 4-layer BERT for up to 64 input tokens.\n* Visual encoder: ViT-B/16 for images of 224 x 224 resolution.\n\n\nUnlike most CLIP-like multomodal models, this model shares 2 layers between the text and visual encoder to allow for more data- and parameter-efficient training.\nAlso unlike most models, UForm provides checkpoints compatible with PyTorch, ONNX, and CoreML, covering the absolute majority of AI-capable devices, with pre-quantized weights and inference code.\nIf you need a larger, more accurate, or multilingual model, check our HuggingFace Hub.\nFor more details on running the model, check out the UForm GitHub repository.\n\n\nEvaluation\n----------\n\n\nOn text-to-image retrieval it reaches 94% Recall@10 for Flickr:\n\n\n\n\n> \n> ¹ It's important to note, that the MS-COCO train split was present in the training data.\n> \n> \n> \n\n\nInstallation\n------------\n\n\nUsage\n-----\n\n\nTo load the model:\n\n\nTo encode the content:"
] |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | zeon8985army/AliasterPrayBig-AliesterBeggWhoAmIBraveByV3-en-1 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T04:29:34+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-to-image | diffusers |
## Model Card for Model ID
Alpaca based on Llama 3 8B
Instruct
8k context length.
### Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import torchvision
tokenizer = AutoTokenizer.from_pretrained("sahaj96/Alpaca-Llama3-8B-Instruct-v1.0")
model = AutoModelForCausalLM.from_pretrained("sahaj96/Alpaca-Llama3-8B-Instruct-v1.0")
prompt = "A serene landscape with mountains and a lake"
inputs = tokenizer(prompt, return_tensors="pt")
output = model.generate(**inputs)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print("Generated Text:", generated_text)
output_image_path = "generated_image.png"
torchvision.utils.save_image(torch.zeros((3, 256, 256)), output_image_path)
print(f"Image generated and saved at {output_image_path}")
### Model Description
fine-tuned from the original Alpaca-dataset entirely generated with OpenAI GPT-3.5-turbo.
Alpaca is a general model and can itself be finetuned to be specialized for specific use cases.
| {"language": ["en"], "license": "apache-2.0", "library_name": "diffusers", "tags": ["art", "code"], "datasets": ["HuggingFaceTB/compendia"], "metrics": ["accuracy"], "pipeline_tag": "text-to-image"} | sahaj96/Alpaca-Llama3-8B-Instruct-v1.0 | null | [
"diffusers",
"art",
"code",
"text-to-image",
"en",
"dataset:HuggingFaceTB/compendia",
"doi:10.57967/hf/2088",
"license:apache-2.0",
"region:us"
] | null | 2024-04-20T04:29:52+00:00 | [] | [
"en"
] | TAGS
#diffusers #art #code #text-to-image #en #dataset-HuggingFaceTB/compendia #doi-10.57967/hf/2088 #license-apache-2.0 #region-us
|
## Model Card for Model ID
Alpaca based on Llama 3 8B
Instruct
8k context length.
### Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import torchvision
tokenizer = AutoTokenizer.from_pretrained("sahaj96/Alpaca-Llama3-8B-Instruct-v1.0")
model = AutoModelForCausalLM.from_pretrained("sahaj96/Alpaca-Llama3-8B-Instruct-v1.0")
prompt = "A serene landscape with mountains and a lake"
inputs = tokenizer(prompt, return_tensors="pt")
output = model.generate(inputs)
generated_text = URL(output[0], skip_special_tokens=True)
print("Generated Text:", generated_text)
output_image_path = "generated_image.png"
URL.save_image(URL((3, 256, 256)), output_image_path)
print(f"Image generated and saved at {output_image_path}")
!image/jpeg
### Model Description
fine-tuned from the original Alpaca-dataset entirely generated with OpenAI GPT-3.5-turbo.
Alpaca is a general model and can itself be finetuned to be specialized for specific use cases.
| [
"## Model Card for Model ID\nAlpaca based on Llama 3 8B \nInstruct \n8k context length.",
"### Usage\n\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\nimport torch\nimport torchvision\n\ntokenizer = AutoTokenizer.from_pretrained(\"sahaj96/Alpaca-Llama3-8B-Instruct-v1.0\")\nmodel = AutoModelForCausalLM.from_pretrained(\"sahaj96/Alpaca-Llama3-8B-Instruct-v1.0\")\n\nprompt = \"A serene landscape with mountains and a lake\"\n\ninputs = tokenizer(prompt, return_tensors=\"pt\")\n\noutput = model.generate(inputs)\n\ngenerated_text = URL(output[0], skip_special_tokens=True)\n\nprint(\"Generated Text:\", generated_text)\n\n\noutput_image_path = \"generated_image.png\"\nURL.save_image(URL((3, 256, 256)), output_image_path)\n\nprint(f\"Image generated and saved at {output_image_path}\")\n\n\n!image/jpeg",
"### Model Description\n\nfine-tuned from the original Alpaca-dataset entirely generated with OpenAI GPT-3.5-turbo. \nAlpaca is a general model and can itself be finetuned to be specialized for specific use cases."
] | [
"TAGS\n#diffusers #art #code #text-to-image #en #dataset-HuggingFaceTB/compendia #doi-10.57967/hf/2088 #license-apache-2.0 #region-us \n",
"## Model Card for Model ID\nAlpaca based on Llama 3 8B \nInstruct \n8k context length.",
"### Usage\n\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\nimport torch\nimport torchvision\n\ntokenizer = AutoTokenizer.from_pretrained(\"sahaj96/Alpaca-Llama3-8B-Instruct-v1.0\")\nmodel = AutoModelForCausalLM.from_pretrained(\"sahaj96/Alpaca-Llama3-8B-Instruct-v1.0\")\n\nprompt = \"A serene landscape with mountains and a lake\"\n\ninputs = tokenizer(prompt, return_tensors=\"pt\")\n\noutput = model.generate(inputs)\n\ngenerated_text = URL(output[0], skip_special_tokens=True)\n\nprint(\"Generated Text:\", generated_text)\n\n\noutput_image_path = \"generated_image.png\"\nURL.save_image(URL((3, 256, 256)), output_image_path)\n\nprint(f\"Image generated and saved at {output_image_path}\")\n\n\n!image/jpeg",
"### Model Description\n\nfine-tuned from the original Alpaca-dataset entirely generated with OpenAI GPT-3.5-turbo. \nAlpaca is a general model and can itself be finetuned to be specialized for specific use cases."
] |
null | transformers |
# Uploaded model
- **Developed by:** namanbnsl
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl"], "base_model": "unsloth/mistral-7b-bnb-4bit"} | namanbnsl/mistral-alpaca-tune | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T04:30:00+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #text-generation-inference #unsloth #mistral #trl #en #base_model-unsloth/mistral-7b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: namanbnsl
- License: apache-2.0
- Finetuned from model : unsloth/mistral-7b-bnb-4bit
This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
| [
"# Uploaded model\n\n- Developed by: namanbnsl\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
"TAGS\n#transformers #safetensors #text-generation-inference #unsloth #mistral #trl #en #base_model-unsloth/mistral-7b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Uploaded model\n\n- Developed by: namanbnsl\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] |
null | adapter-transformers |
# Adapter `BigTMiami/seq_bn_amz_10k_help_class_no_pretrain_4_adapter` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/amazon_10k_helpfulness_dataset](https://huggingface.co/datasets/BigTMiami/amazon_10k_helpfulness_dataset/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("BigTMiami/seq_bn_amz_10k_help_class_no_pretrain_4_adapter", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> | {"tags": ["roberta", "adapter-transformers"], "datasets": ["BigTMiami/amazon_10k_helpfulness_dataset"]} | BigTMiami/seq_bn_amz_10k_help_class_no_pretrain_4_adapter | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/amazon_10k_helpfulness_dataset",
"region:us"
] | null | 2024-04-20T04:31:33+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/amazon_10k_helpfulness_dataset #region-us
|
# Adapter 'BigTMiami/seq_bn_amz_10k_help_class_no_pretrain_4_adapter' for roberta-base
An adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_10k_helpfulness_dataset dataset and includes a prediction head for classification.
This adapter was created for usage with the Adapters library.
## Usage
First, install 'adapters':
Now, the adapter can be loaded and activated like this:
## Architecture & Training
## Evaluation results
| [
"# Adapter 'BigTMiami/seq_bn_amz_10k_help_class_no_pretrain_4_adapter' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_10k_helpfulness_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] | [
"TAGS\n#adapter-transformers #roberta #dataset-BigTMiami/amazon_10k_helpfulness_dataset #region-us \n",
"# Adapter 'BigTMiami/seq_bn_amz_10k_help_class_no_pretrain_4_adapter' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_10k_helpfulness_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
text-generation | transformers |
# monika-ddlc-8b-v1:
* LLaMA-3 8b fine-tuned for Monika character from DDLC (test, may have later version out soon)
* Fine-tuned on a dataset of ~600+ items (dialogue scraped from game, reddit, and Twitter augmented by [l2-7b-monika-v0.3c1](https://huggingface.co/922-CA/llama-2-7b-monika-v0.3c1) to turn each into snippets of multi-turn chat dialogue between Player and Monika; this was then manually edited, with more manually crafted items including info about character added in)
* [GGUFs](https://huggingface.co/922-CA/Llama-3-monika-ddlc-8b-v1-GGUF)
### USAGE
This is meant to be mainly a chat model with limited RP ability.
For best results: replace "Human" and "Assistant" with "Player" and "Monika" like so:
\nPlayer: (prompt)\nMonika:
### HYPERPARAMS
* Trained for 1 epoch
* rank: 16
* lora alpha: 16
* lora dropout: 0.5
* lr: 2e-4
* batch size: 2
* warmup ratio: 0.1
* grad steps: 4
### WARNINGS AND DISCLAIMERS
This model is meant to closely reflect the characteristics of Monika. Despite this, there is always the chance that "Monika" will hallucinate and get information about herself wrong or act out of character.
Additionally, being character-focused means that this model may not be the smartest model/not as capable as others for specific tasks.
Finally, this model is not guaranteed to output aligned or safe outputs, use at your own risk!
| {"license": "other", "library_name": "transformers", "tags": ["unsloth", "trl", "sft"], "datasets": ["922-CA/MoCha_v1a"], "license_name": "llama3", "license_link": "LICENSE"} | 922-CA/Llama-3-monika-ddlc-8b-v1 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"dataset:922-CA/MoCha_v1a",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T04:31:55+00:00 | [] | [] | TAGS
#transformers #pytorch #llama #text-generation #unsloth #trl #sft #conversational #dataset-922-CA/MoCha_v1a #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# monika-ddlc-8b-v1:
* LLaMA-3 8b fine-tuned for Monika character from DDLC (test, may have later version out soon)
* Fine-tuned on a dataset of ~600+ items (dialogue scraped from game, reddit, and Twitter augmented by l2-7b-monika-v0.3c1 to turn each into snippets of multi-turn chat dialogue between Player and Monika; this was then manually edited, with more manually crafted items including info about character added in)
* GGUFs
### USAGE
This is meant to be mainly a chat model with limited RP ability.
For best results: replace "Human" and "Assistant" with "Player" and "Monika" like so:
\nPlayer: (prompt)\nMonika:
### HYPERPARAMS
* Trained for 1 epoch
* rank: 16
* lora alpha: 16
* lora dropout: 0.5
* lr: 2e-4
* batch size: 2
* warmup ratio: 0.1
* grad steps: 4
### WARNINGS AND DISCLAIMERS
This model is meant to closely reflect the characteristics of Monika. Despite this, there is always the chance that "Monika" will hallucinate and get information about herself wrong or act out of character.
Additionally, being character-focused means that this model may not be the smartest model/not as capable as others for specific tasks.
Finally, this model is not guaranteed to output aligned or safe outputs, use at your own risk!
| [
"# monika-ddlc-8b-v1:\n* LLaMA-3 8b fine-tuned for Monika character from DDLC (test, may have later version out soon)\n* Fine-tuned on a dataset of ~600+ items (dialogue scraped from game, reddit, and Twitter augmented by l2-7b-monika-v0.3c1 to turn each into snippets of multi-turn chat dialogue between Player and Monika; this was then manually edited, with more manually crafted items including info about character added in)\n* GGUFs",
"### USAGE\nThis is meant to be mainly a chat model with limited RP ability.\n\nFor best results: replace \"Human\" and \"Assistant\" with \"Player\" and \"Monika\" like so:\n\n\\nPlayer: (prompt)\\nMonika:",
"### HYPERPARAMS\n* Trained for 1 epoch\n* rank: 16\n* lora alpha: 16\n* lora dropout: 0.5\n* lr: 2e-4\n* batch size: 2\n* warmup ratio: 0.1\n* grad steps: 4",
"### WARNINGS AND DISCLAIMERS\nThis model is meant to closely reflect the characteristics of Monika. Despite this, there is always the chance that \"Monika\" will hallucinate and get information about herself wrong or act out of character.\n\nAdditionally, being character-focused means that this model may not be the smartest model/not as capable as others for specific tasks.\n\nFinally, this model is not guaranteed to output aligned or safe outputs, use at your own risk!"
] | [
"TAGS\n#transformers #pytorch #llama #text-generation #unsloth #trl #sft #conversational #dataset-922-CA/MoCha_v1a #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# monika-ddlc-8b-v1:\n* LLaMA-3 8b fine-tuned for Monika character from DDLC (test, may have later version out soon)\n* Fine-tuned on a dataset of ~600+ items (dialogue scraped from game, reddit, and Twitter augmented by l2-7b-monika-v0.3c1 to turn each into snippets of multi-turn chat dialogue between Player and Monika; this was then manually edited, with more manually crafted items including info about character added in)\n* GGUFs",
"### USAGE\nThis is meant to be mainly a chat model with limited RP ability.\n\nFor best results: replace \"Human\" and \"Assistant\" with \"Player\" and \"Monika\" like so:\n\n\\nPlayer: (prompt)\\nMonika:",
"### HYPERPARAMS\n* Trained for 1 epoch\n* rank: 16\n* lora alpha: 16\n* lora dropout: 0.5\n* lr: 2e-4\n* batch size: 2\n* warmup ratio: 0.1\n* grad steps: 4",
"### WARNINGS AND DISCLAIMERS\nThis model is meant to closely reflect the characteristics of Monika. Despite this, there is always the chance that \"Monika\" will hallucinate and get information about herself wrong or act out of character.\n\nAdditionally, being character-focused means that this model may not be the smartest model/not as capable as others for specific tasks.\n\nFinally, this model is not guaranteed to output aligned or safe outputs, use at your own risk!"
] |
feature-extraction | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_bge_ver22
This model is a fine-tuned version of [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "BAAI/bge-m3", "model-index": [{"name": "finetuned_bge_ver22", "results": []}]} | comet24082002/finetuned_bge_ver22 | null | [
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"feature-extraction",
"generated_from_trainer",
"base_model:BAAI/bge-m3",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T04:32:22+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #xlm-roberta #feature-extraction #generated_from_trainer #base_model-BAAI/bge-m3 #license-mit #endpoints_compatible #region-us
|
# finetuned_bge_ver22
This model is a fine-tuned version of BAAI/bge-m3 on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| [
"# finetuned_bge_ver22\n\nThis model is a fine-tuned version of BAAI/bge-m3 on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 32\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 2\n- total_train_batch_size: 64\n- total_eval_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 20.0\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tensorboard #safetensors #xlm-roberta #feature-extraction #generated_from_trainer #base_model-BAAI/bge-m3 #license-mit #endpoints_compatible #region-us \n",
"# finetuned_bge_ver22\n\nThis model is a fine-tuned version of BAAI/bge-m3 on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 32\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 2\n- total_train_batch_size: 64\n- total_eval_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 20.0\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
feature-extraction | transformers | <h1 align="center">UForm</h1>
<h3 align="center">
Pocket-Sized Multimodal AI<br/>
For Content Understanding and Generation<br/>
In Python, JavaScript, and Swift<br/>
</h3>
---
The `uform3-image-text-english-large` UForm model is a tiny vision and English language encoder, mapping them into a shared vector space.
This model produces up to __64-, 256-, 512-, and 768-dimensional embeddings__ and is made of:
* Text encoder: 12-layer BERT for up to 64 input tokens.
* Visual encoder: ViT-L/14 for images of 224 x 224 resolution.
Unlike most CLIP-like multomodal models, this model shares 6 layers between the text and visual encoder to allow for more data- and parameter-efficient training.
Also unlike most models, UForm provides checkpoints compatible with PyTorch, ONNX, and CoreML, covering the absolute majority of AI-capable devices, with pre-quantized weights and inference code.
If you need a larger, more accurate, or multilingual model, check our [HuggingFace Hub](https://huggingface.co/unum-cloud/).
For more details on running the model, check out the [UForm GitHub repository](https://github.com/unum-cloud/uform/).
## Evaluation
For zero-shot ImageNet classification the model achieves Top-1 accuracy of 51.8% and Top-5 of 75.6%.
On text-to-image retrieval it reaches 92% Recall@10 for Flickr:
| Dataset | Recall@1 | Recall@5 | Recall@10 |
| :-------- | ------: | --------: | --------: |
| Zero-Shot Flickr | 0.693 | 0.875 | 0.923 |
| Zero-Shot MS-COCO | 0.382 | 0.617 | 0.728 |
## Installation
```bash
pip install "uform[torch,onnx]"
```
## Usage
To load the model:
```python
from uform import get_model, Modality
import requests
from io import BytesIO
from PIL import Image
model_name = 'unum-cloud/uform3-image-text-english-large'
modalities = [Modality.TEXT_ENCODER, Modality.IMAGE_ENCODER]
processors, models = get_model(model_name, modalities=modalities)
model_text = models[Modality.TEXT_ENCODER]
model_image = models[Modality.IMAGE_ENCODER]
processor_text = processors[Modality.TEXT_ENCODER]
processor_image = processors[Modality.IMAGE_ENCODER]
```
To encode the content:
```python
text = 'a cityscape bathed in the warm glow of the sun, with varied architecture and a towering, snow-capped mountain rising majestically in the background'
image_url = 'https://media-cdn.tripadvisor.com/media/photo-s/1b/28/6b/53/lovely-armenia.jpg'
image_url = Image.open(BytesIO(requests.get(image_url).content))
image_data = processor_image(image)
text_data = processor_text(text)
image_features, image_embedding = model_image.encode(image_data, return_features=True)
text_features, text_embedding = model_text.encode(text_data, return_features=True)
```
| {"license": "apache-2.0", "tags": ["clip", "vision"], "datasets": ["Ziyang/yfcc15m", "conceptual_captions"], "pipeline_tag": "feature-extraction"} | unum-cloud/uform3-image-text-english-large | null | [
"transformers",
"coreml",
"onnx",
"clip",
"vision",
"feature-extraction",
"dataset:Ziyang/yfcc15m",
"dataset:conceptual_captions",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T04:32:36+00:00 | [] | [] | TAGS
#transformers #coreml #onnx #clip #vision #feature-extraction #dataset-Ziyang/yfcc15m #dataset-conceptual_captions #license-apache-2.0 #endpoints_compatible #region-us
| UForm
=====
###
Pocket-Sized Multimodal AI
For Content Understanding and Generation
In Python, JavaScript, and Swift
---
The 'uform3-image-text-english-large' UForm model is a tiny vision and English language encoder, mapping them into a shared vector space.
This model produces up to **64-, 256-, 512-, and 768-dimensional embeddings** and is made of:
* Text encoder: 12-layer BERT for up to 64 input tokens.
* Visual encoder: ViT-L/14 for images of 224 x 224 resolution.
Unlike most CLIP-like multomodal models, this model shares 6 layers between the text and visual encoder to allow for more data- and parameter-efficient training.
Also unlike most models, UForm provides checkpoints compatible with PyTorch, ONNX, and CoreML, covering the absolute majority of AI-capable devices, with pre-quantized weights and inference code.
If you need a larger, more accurate, or multilingual model, check our HuggingFace Hub.
For more details on running the model, check out the UForm GitHub repository.
Evaluation
----------
For zero-shot ImageNet classification the model achieves Top-1 accuracy of 51.8% and Top-5 of 75.6%.
On text-to-image retrieval it reaches 92% Recall@10 for Flickr:
Installation
------------
Usage
-----
To load the model:
To encode the content:
| [
"### \nPocket-Sized Multimodal AI\nFor Content Understanding and Generation\nIn Python, JavaScript, and Swift\n\n\n\n\n---\n\n\nThe 'uform3-image-text-english-large' UForm model is a tiny vision and English language encoder, mapping them into a shared vector space.\nThis model produces up to **64-, 256-, 512-, and 768-dimensional embeddings** and is made of:\n\n\n* Text encoder: 12-layer BERT for up to 64 input tokens.\n* Visual encoder: ViT-L/14 for images of 224 x 224 resolution.\n\n\nUnlike most CLIP-like multomodal models, this model shares 6 layers between the text and visual encoder to allow for more data- and parameter-efficient training.\nAlso unlike most models, UForm provides checkpoints compatible with PyTorch, ONNX, and CoreML, covering the absolute majority of AI-capable devices, with pre-quantized weights and inference code.\nIf you need a larger, more accurate, or multilingual model, check our HuggingFace Hub.\nFor more details on running the model, check out the UForm GitHub repository.\n\n\nEvaluation\n----------\n\n\nFor zero-shot ImageNet classification the model achieves Top-1 accuracy of 51.8% and Top-5 of 75.6%.\nOn text-to-image retrieval it reaches 92% Recall@10 for Flickr:\n\n\n\nInstallation\n------------\n\n\nUsage\n-----\n\n\nTo load the model:\n\n\nTo encode the content:"
] | [
"TAGS\n#transformers #coreml #onnx #clip #vision #feature-extraction #dataset-Ziyang/yfcc15m #dataset-conceptual_captions #license-apache-2.0 #endpoints_compatible #region-us \n",
"### \nPocket-Sized Multimodal AI\nFor Content Understanding and Generation\nIn Python, JavaScript, and Swift\n\n\n\n\n---\n\n\nThe 'uform3-image-text-english-large' UForm model is a tiny vision and English language encoder, mapping them into a shared vector space.\nThis model produces up to **64-, 256-, 512-, and 768-dimensional embeddings** and is made of:\n\n\n* Text encoder: 12-layer BERT for up to 64 input tokens.\n* Visual encoder: ViT-L/14 for images of 224 x 224 resolution.\n\n\nUnlike most CLIP-like multomodal models, this model shares 6 layers between the text and visual encoder to allow for more data- and parameter-efficient training.\nAlso unlike most models, UForm provides checkpoints compatible with PyTorch, ONNX, and CoreML, covering the absolute majority of AI-capable devices, with pre-quantized weights and inference code.\nIf you need a larger, more accurate, or multilingual model, check our HuggingFace Hub.\nFor more details on running the model, check out the UForm GitHub repository.\n\n\nEvaluation\n----------\n\n\nFor zero-shot ImageNet classification the model achieves Top-1 accuracy of 51.8% and Top-5 of 75.6%.\nOn text-to-image retrieval it reaches 92% Recall@10 for Flickr:\n\n\n\nInstallation\n------------\n\n\nUsage\n-----\n\n\nTo load the model:\n\n\nTo encode the content:"
] |
null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 04-20-04-33-22
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0014
- Accuracy: 1.0
- Precision: 1.0
- Recall: 1.0
- F1: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.7284 | 0.21 | 10 | 0.6353 | 0.5976 | 0.0 | 0.0 | 0.0 |
| 0.7191 | 0.42 | 20 | 0.5723 | 0.6098 | 1.0 | 0.0303 | 0.0588 |
| 0.6009 | 0.62 | 30 | 0.4573 | 0.7561 | 1.0 | 0.3939 | 0.5652 |
| 0.4025 | 0.83 | 40 | 0.2610 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.1859 | 1.04 | 50 | 0.0880 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0584 | 1.25 | 60 | 0.0175 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0526 | 1.46 | 70 | 0.0054 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0127 | 1.67 | 80 | 0.0040 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0104 | 1.88 | 90 | 0.0026 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0036 | 2.08 | 100 | 0.0017 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0309 | 2.29 | 110 | 0.0015 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0023 | 2.5 | 120 | 0.0015 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0049 | 2.71 | 130 | 0.0014 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0017 | 2.92 | 140 | 0.0014 | 1.0 | 1.0 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.13.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "base_model": "google-bert/bert-base-uncased", "model-index": [{"name": "04-20-04-33-22", "results": []}]} | reeddg/04-20-04-33-22 | null | [
"tensorboard",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"license:apache-2.0",
"region:us"
] | null | 2024-04-20T04:36:21+00:00 | [] | [] | TAGS
#tensorboard #generated_from_trainer #base_model-google-bert/bert-base-uncased #license-apache-2.0 #region-us
| 04-20-04-33-22
==============
This model is a fine-tuned version of google-bert/bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0014
* Accuracy: 1.0
* Precision: 1.0
* Recall: 1.0
* F1: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0002
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.31.0
* Pytorch 2.2.1+cu121
* Datasets 2.19.0
* Tokenizers 0.13.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.31.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.13.3"
] | [
"TAGS\n#tensorboard #generated_from_trainer #base_model-google-bert/bert-base-uncased #license-apache-2.0 #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.31.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.13.3"
] |
text-generation | transformers |
# Model Card: Nous-Hermes-Llama2-13b
This is a Updated fork done by Hexon Labs on the name Featherlite under Llama Series
Compute provided by our project sponsor Redmond AI, thank you! Follow RedmondAI on Twitter @RedmondAI.
## Model Description
Nous-Hermes-Llama2-13b is a state-of-the-art language model fine-tuned on over 300,000 instructions. This model was fine-tuned by Nous Research, with Teknium and Emozilla leading the fine tuning process and dataset curation, Redmond AI sponsoring the compute, and several other contributors.
This Hermes model uses the exact same dataset as Hermes on Llama-1. This is to ensure consistency between the old Hermes and new, for anyone who wanted to keep Hermes as similar to the old one, just more capable.
This model stands out for its long responses, lower hallucination rate, and absence of OpenAI censorship mechanisms. The fine-tuning process was performed with a 4096 sequence length on an 8x a100 80GB DGX machine.
## Example Outputs:
## Model Training
The model was trained almost entirely on synthetic GPT-4 outputs. Curating high quality GPT-4 datasets enables incredibly high quality in knowledge, task completion, and style.
This includes data from diverse sources such as GPTeacher, the general, roleplay v1&2, code instruct datasets, Nous Instruct & PDACTL (unpublished), and several others, detailed further below
## Collaborators
The model fine-tuning and the datasets were a collaboration of efforts and resources between Teknium, Karan4D, Emozilla, Huemin Art, and Redmond AI.
Special mention goes to @winglian for assisting in some of the training issues.
Huge shoutout and acknowledgement is deserved for all the dataset creators who generously share their datasets openly.
Among the contributors of datasets:
- GPTeacher was made available by Teknium
- Wizard LM by nlpxucan
- Nous Research Instruct Dataset was provided by Karan4D and HueminArt.
- GPT4-LLM and Unnatural Instructions were provided by Microsoft
- Airoboros dataset by jondurbin
- Camel-AI's domain expert datasets are from Camel-AI
- CodeAlpaca dataset by Sahil 2801.
If anyone was left out, please open a thread in the community tab.
## Prompt Format
The model follows the Alpaca prompt format:
```
### Instruction:
<prompt>
### Response:
<leave a newline blank for model to respond>
```
or
```
### Instruction:
<prompt>
### Input:
<additional context>
### Response:
<leave a newline blank for model to respond>
```
## Benchmark Results
AGI-Eval
```
| Task |Version| Metric |Value | |Stderr|
|agieval_aqua_rat | 0|acc |0.2362|± |0.0267|
| | |acc_norm|0.2480|± |0.0272|
|agieval_logiqa_en | 0|acc |0.3425|± |0.0186|
| | |acc_norm|0.3472|± |0.0187|
|agieval_lsat_ar | 0|acc |0.2522|± |0.0287|
| | |acc_norm|0.2087|± |0.0269|
|agieval_lsat_lr | 0|acc |0.3510|± |0.0212|
| | |acc_norm|0.3627|± |0.0213|
|agieval_lsat_rc | 0|acc |0.4647|± |0.0305|
| | |acc_norm|0.4424|± |0.0303|
|agieval_sat_en | 0|acc |0.6602|± |0.0331|
| | |acc_norm|0.6165|± |0.0340|
|agieval_sat_en_without_passage| 0|acc |0.4320|± |0.0346|
| | |acc_norm|0.4272|± |0.0345|
|agieval_sat_math | 0|acc |0.2909|± |0.0307|
| | |acc_norm|0.2727|± |0.0301|
```
GPT-4All Benchmark Set
```
| Task |Version| Metric |Value | |Stderr|
|arc_challenge| 0|acc |0.5102|± |0.0146|
| | |acc_norm|0.5213|± |0.0146|
|arc_easy | 0|acc |0.7959|± |0.0083|
| | |acc_norm|0.7567|± |0.0088|
|boolq | 1|acc |0.8394|± |0.0064|
|hellaswag | 0|acc |0.6164|± |0.0049|
| | |acc_norm|0.8009|± |0.0040|
|openbookqa | 0|acc |0.3580|± |0.0215|
| | |acc_norm|0.4620|± |0.0223|
|piqa | 0|acc |0.7992|± |0.0093|
| | |acc_norm|0.8069|± |0.0092|
|winogrande | 0|acc |0.7127|± |0.0127|
```
BigBench Reasoning Test
```
| Task |Version| Metric |Value | |Stderr|
|bigbench_causal_judgement | 0|multiple_choice_grade|0.5526|± |0.0362|
|bigbench_date_understanding | 0|multiple_choice_grade|0.7344|± |0.0230|
|bigbench_disambiguation_qa | 0|multiple_choice_grade|0.2636|± |0.0275|
|bigbench_geometric_shapes | 0|multiple_choice_grade|0.0195|± |0.0073|
| | |exact_str_match |0.0000|± |0.0000|
|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|0.2760|± |0.0200|
|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|0.2100|± |0.0154|
|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|0.4400|± |0.0287|
|bigbench_movie_recommendation | 0|multiple_choice_grade|0.2440|± |0.0192|
|bigbench_navigate | 0|multiple_choice_grade|0.4950|± |0.0158|
|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|0.5570|± |0.0111|
|bigbench_ruin_names | 0|multiple_choice_grade|0.3728|± |0.0229|
|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|0.1854|± |0.0123|
|bigbench_snarks | 0|multiple_choice_grade|0.6298|± |0.0360|
|bigbench_sports_understanding | 0|multiple_choice_grade|0.6156|± |0.0155|
|bigbench_temporal_sequences | 0|multiple_choice_grade|0.3140|± |0.0147|
|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|0.2032|± |0.0114|
|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|0.1406|± |0.0083|
|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|0.4400|± |0.0287|
```
These are the highest benchmarks Hermes has seen on every metric, achieving the following average scores:
- GPT4All benchmark average is now 70.0 - from 68.8 in Hermes-Llama1
- 0.3657 on BigBench, up from 0.328 on hermes-llama1
- 0.372 on AGIEval, up from 0.354 on Hermes-llama1
These benchmarks currently have us at #1 on ARC-c, ARC-e, Hellaswag, and OpenBookQA, and 2nd place on Winogrande, comparing to GPT4all's benchmarking list, supplanting Hermes 1 for the new top position.
## Resources for Applied Use Cases:
Check out LM Studio for a nice chatgpt style interface here: https://lmstudio.ai/
For an example of a back and forth chatbot using huggingface transformers and discord, check out: https://github.com/teknium1/alpaca-discord
For an example of a roleplaying discord chatbot, check out this: https://github.com/teknium1/alpaca-roleplay-discordbot
## Future Plans
We plan to continue to iterate on both more high quality data, and new data filtering techniques to eliminate lower quality data going forward.
## Model Usage
The model is available for download on Hugging Face. It is suitable for a wide range of language tasks, from generating creative text to understanding and following complex instructions.
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
| {"language": ["en"], "license": ["mit"], "tags": ["llama-2", "self-instruct", "distillation", "synthetic instruction"]} | featherlite-ai/Featherlite-Hermes-Llama2-13B | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"llama-2",
"self-instruct",
"distillation",
"synthetic instruction",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T04:36:35+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #llama #text-generation #llama-2 #self-instruct #distillation #synthetic instruction #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card: Nous-Hermes-Llama2-13b
This is a Updated fork done by Hexon Labs on the name Featherlite under Llama Series
Compute provided by our project sponsor Redmond AI, thank you! Follow RedmondAI on Twitter @RedmondAI.
## Model Description
Nous-Hermes-Llama2-13b is a state-of-the-art language model fine-tuned on over 300,000 instructions. This model was fine-tuned by Nous Research, with Teknium and Emozilla leading the fine tuning process and dataset curation, Redmond AI sponsoring the compute, and several other contributors.
This Hermes model uses the exact same dataset as Hermes on Llama-1. This is to ensure consistency between the old Hermes and new, for anyone who wanted to keep Hermes as similar to the old one, just more capable.
This model stands out for its long responses, lower hallucination rate, and absence of OpenAI censorship mechanisms. The fine-tuning process was performed with a 4096 sequence length on an 8x a100 80GB DGX machine.
## Example Outputs:
!Example4
!Example1
!Example2
!Example3
## Model Training
The model was trained almost entirely on synthetic GPT-4 outputs. Curating high quality GPT-4 datasets enables incredibly high quality in knowledge, task completion, and style.
This includes data from diverse sources such as GPTeacher, the general, roleplay v1&2, code instruct datasets, Nous Instruct & PDACTL (unpublished), and several others, detailed further below
## Collaborators
The model fine-tuning and the datasets were a collaboration of efforts and resources between Teknium, Karan4D, Emozilla, Huemin Art, and Redmond AI.
Special mention goes to @winglian for assisting in some of the training issues.
Huge shoutout and acknowledgement is deserved for all the dataset creators who generously share their datasets openly.
Among the contributors of datasets:
- GPTeacher was made available by Teknium
- Wizard LM by nlpxucan
- Nous Research Instruct Dataset was provided by Karan4D and HueminArt.
- GPT4-LLM and Unnatural Instructions were provided by Microsoft
- Airoboros dataset by jondurbin
- Camel-AI's domain expert datasets are from Camel-AI
- CodeAlpaca dataset by Sahil 2801.
If anyone was left out, please open a thread in the community tab.
## Prompt Format
The model follows the Alpaca prompt format:
or
## Benchmark Results
AGI-Eval
GPT-4All Benchmark Set
BigBench Reasoning Test
These are the highest benchmarks Hermes has seen on every metric, achieving the following average scores:
- GPT4All benchmark average is now 70.0 - from 68.8 in Hermes-Llama1
- 0.3657 on BigBench, up from 0.328 on hermes-llama1
- 0.372 on AGIEval, up from 0.354 on Hermes-llama1
These benchmarks currently have us at #1 on ARC-c, ARC-e, Hellaswag, and OpenBookQA, and 2nd place on Winogrande, comparing to GPT4all's benchmarking list, supplanting Hermes 1 for the new top position.
## Resources for Applied Use Cases:
Check out LM Studio for a nice chatgpt style interface here: URL
For an example of a back and forth chatbot using huggingface transformers and discord, check out: URL
For an example of a roleplaying discord chatbot, check out this: URL
## Future Plans
We plan to continue to iterate on both more high quality data, and new data filtering techniques to eliminate lower quality data going forward.
## Model Usage
The model is available for download on Hugging Face. It is suitable for a wide range of language tasks, from generating creative text to understanding and following complex instructions.
<img src="URL alt="Built with Axolotl" width="200" height="32"/>
| [
"# Model Card: Nous-Hermes-Llama2-13b\n\nThis is a Updated fork done by Hexon Labs on the name Featherlite under Llama Series\n\nCompute provided by our project sponsor Redmond AI, thank you! Follow RedmondAI on Twitter @RedmondAI.",
"## Model Description\n\nNous-Hermes-Llama2-13b is a state-of-the-art language model fine-tuned on over 300,000 instructions. This model was fine-tuned by Nous Research, with Teknium and Emozilla leading the fine tuning process and dataset curation, Redmond AI sponsoring the compute, and several other contributors.\n\nThis Hermes model uses the exact same dataset as Hermes on Llama-1. This is to ensure consistency between the old Hermes and new, for anyone who wanted to keep Hermes as similar to the old one, just more capable.\n\nThis model stands out for its long responses, lower hallucination rate, and absence of OpenAI censorship mechanisms. The fine-tuning process was performed with a 4096 sequence length on an 8x a100 80GB DGX machine.",
"## Example Outputs:\n!Example4\n!Example1\n!Example2\n!Example3",
"## Model Training\n\nThe model was trained almost entirely on synthetic GPT-4 outputs. Curating high quality GPT-4 datasets enables incredibly high quality in knowledge, task completion, and style.\n\nThis includes data from diverse sources such as GPTeacher, the general, roleplay v1&2, code instruct datasets, Nous Instruct & PDACTL (unpublished), and several others, detailed further below",
"## Collaborators\nThe model fine-tuning and the datasets were a collaboration of efforts and resources between Teknium, Karan4D, Emozilla, Huemin Art, and Redmond AI. \n \nSpecial mention goes to @winglian for assisting in some of the training issues.\n\nHuge shoutout and acknowledgement is deserved for all the dataset creators who generously share their datasets openly. \n\nAmong the contributors of datasets:\n- GPTeacher was made available by Teknium\n- Wizard LM by nlpxucan\n- Nous Research Instruct Dataset was provided by Karan4D and HueminArt. \n- GPT4-LLM and Unnatural Instructions were provided by Microsoft\n- Airoboros dataset by jondurbin\n- Camel-AI's domain expert datasets are from Camel-AI\n- CodeAlpaca dataset by Sahil 2801.\n\nIf anyone was left out, please open a thread in the community tab.",
"## Prompt Format\n\nThe model follows the Alpaca prompt format:\n\n\nor",
"## Benchmark Results\nAGI-Eval\n\nGPT-4All Benchmark Set\n\nBigBench Reasoning Test\n\n\nThese are the highest benchmarks Hermes has seen on every metric, achieving the following average scores:\n- GPT4All benchmark average is now 70.0 - from 68.8 in Hermes-Llama1\n- 0.3657 on BigBench, up from 0.328 on hermes-llama1\n- 0.372 on AGIEval, up from 0.354 on Hermes-llama1\n\nThese benchmarks currently have us at #1 on ARC-c, ARC-e, Hellaswag, and OpenBookQA, and 2nd place on Winogrande, comparing to GPT4all's benchmarking list, supplanting Hermes 1 for the new top position.",
"## Resources for Applied Use Cases:\nCheck out LM Studio for a nice chatgpt style interface here: URL\nFor an example of a back and forth chatbot using huggingface transformers and discord, check out: URL \nFor an example of a roleplaying discord chatbot, check out this: URL",
"## Future Plans\nWe plan to continue to iterate on both more high quality data, and new data filtering techniques to eliminate lower quality data going forward.",
"## Model Usage\nThe model is available for download on Hugging Face. It is suitable for a wide range of language tasks, from generating creative text to understanding and following complex instructions.\n\n<img src=\"URL alt=\"Built with Axolotl\" width=\"200\" height=\"32\"/>"
] | [
"TAGS\n#transformers #pytorch #llama #text-generation #llama-2 #self-instruct #distillation #synthetic instruction #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card: Nous-Hermes-Llama2-13b\n\nThis is a Updated fork done by Hexon Labs on the name Featherlite under Llama Series\n\nCompute provided by our project sponsor Redmond AI, thank you! Follow RedmondAI on Twitter @RedmondAI.",
"## Model Description\n\nNous-Hermes-Llama2-13b is a state-of-the-art language model fine-tuned on over 300,000 instructions. This model was fine-tuned by Nous Research, with Teknium and Emozilla leading the fine tuning process and dataset curation, Redmond AI sponsoring the compute, and several other contributors.\n\nThis Hermes model uses the exact same dataset as Hermes on Llama-1. This is to ensure consistency between the old Hermes and new, for anyone who wanted to keep Hermes as similar to the old one, just more capable.\n\nThis model stands out for its long responses, lower hallucination rate, and absence of OpenAI censorship mechanisms. The fine-tuning process was performed with a 4096 sequence length on an 8x a100 80GB DGX machine.",
"## Example Outputs:\n!Example4\n!Example1\n!Example2\n!Example3",
"## Model Training\n\nThe model was trained almost entirely on synthetic GPT-4 outputs. Curating high quality GPT-4 datasets enables incredibly high quality in knowledge, task completion, and style.\n\nThis includes data from diverse sources such as GPTeacher, the general, roleplay v1&2, code instruct datasets, Nous Instruct & PDACTL (unpublished), and several others, detailed further below",
"## Collaborators\nThe model fine-tuning and the datasets were a collaboration of efforts and resources between Teknium, Karan4D, Emozilla, Huemin Art, and Redmond AI. \n \nSpecial mention goes to @winglian for assisting in some of the training issues.\n\nHuge shoutout and acknowledgement is deserved for all the dataset creators who generously share their datasets openly. \n\nAmong the contributors of datasets:\n- GPTeacher was made available by Teknium\n- Wizard LM by nlpxucan\n- Nous Research Instruct Dataset was provided by Karan4D and HueminArt. \n- GPT4-LLM and Unnatural Instructions were provided by Microsoft\n- Airoboros dataset by jondurbin\n- Camel-AI's domain expert datasets are from Camel-AI\n- CodeAlpaca dataset by Sahil 2801.\n\nIf anyone was left out, please open a thread in the community tab.",
"## Prompt Format\n\nThe model follows the Alpaca prompt format:\n\n\nor",
"## Benchmark Results\nAGI-Eval\n\nGPT-4All Benchmark Set\n\nBigBench Reasoning Test\n\n\nThese are the highest benchmarks Hermes has seen on every metric, achieving the following average scores:\n- GPT4All benchmark average is now 70.0 - from 68.8 in Hermes-Llama1\n- 0.3657 on BigBench, up from 0.328 on hermes-llama1\n- 0.372 on AGIEval, up from 0.354 on Hermes-llama1\n\nThese benchmarks currently have us at #1 on ARC-c, ARC-e, Hellaswag, and OpenBookQA, and 2nd place on Winogrande, comparing to GPT4all's benchmarking list, supplanting Hermes 1 for the new top position.",
"## Resources for Applied Use Cases:\nCheck out LM Studio for a nice chatgpt style interface here: URL\nFor an example of a back and forth chatbot using huggingface transformers and discord, check out: URL \nFor an example of a roleplaying discord chatbot, check out this: URL",
"## Future Plans\nWe plan to continue to iterate on both more high quality data, and new data filtering techniques to eliminate lower quality data going forward.",
"## Model Usage\nThe model is available for download on Hugging Face. It is suitable for a wide range of language tasks, from generating creative text to understanding and following complex instructions.\n\n<img src=\"URL alt=\"Built with Axolotl\" width=\"200\" height=\"32\"/>"
] |
text-generation | mlx |
# lucataco/Mistral-7B-Instruct-v0.2-4bit
This model was converted to MLX format from [`mistralai/Mistral-7B-Instruct-v0.2`]() using mlx-lm version **0.10.0**.
Refer to the [original model card](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("lucataco/Mistral-7B-Instruct-v0.2-4bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
| {"license": "apache-2.0", "tags": ["finetuned", "mlx"], "pipeline_tag": "text-generation", "inference": true, "widget": [{"messages": [{"role": "user", "content": "What is your favorite condiment?"}]}]} | lucataco/Mistral-7B-Instruct-v0.2-4bit | null | [
"mlx",
"safetensors",
"mistral",
"finetuned",
"text-generation",
"conversational",
"license:apache-2.0",
"region:us"
] | null | 2024-04-20T04:37:31+00:00 | [] | [] | TAGS
#mlx #safetensors #mistral #finetuned #text-generation #conversational #license-apache-2.0 #region-us
|
# lucataco/Mistral-7B-Instruct-v0.2-4bit
This model was converted to MLX format from ['mistralai/Mistral-7B-Instruct-v0.2']() using mlx-lm version 0.10.0.
Refer to the original model card for more details on the model.
## Use with mlx
| [
"# lucataco/Mistral-7B-Instruct-v0.2-4bit\nThis model was converted to MLX format from ['mistralai/Mistral-7B-Instruct-v0.2']() using mlx-lm version 0.10.0.\nRefer to the original model card for more details on the model.",
"## Use with mlx"
] | [
"TAGS\n#mlx #safetensors #mistral #finetuned #text-generation #conversational #license-apache-2.0 #region-us \n",
"# lucataco/Mistral-7B-Instruct-v0.2-4bit\nThis model was converted to MLX format from ['mistralai/Mistral-7B-Instruct-v0.2']() using mlx-lm version 0.10.0.\nRefer to the original model card for more details on the model.",
"## Use with mlx"
] |
null | null |
# DavidAU/Tinyllama-2-1b-miniguanaco-Q8_0-GGUF
This model was converted to GGUF format from [`abdgrt/Tinyllama-2-1b-miniguanaco`](https://huggingface.co/abdgrt/Tinyllama-2-1b-miniguanaco) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/abdgrt/Tinyllama-2-1b-miniguanaco) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo DavidAU/Tinyllama-2-1b-miniguanaco-Q8_0-GGUF --model tinyllama-2-1b-miniguanaco.Q8_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo DavidAU/Tinyllama-2-1b-miniguanaco-Q8_0-GGUF --model tinyllama-2-1b-miniguanaco.Q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m tinyllama-2-1b-miniguanaco.Q8_0.gguf -n 128
```
| {"tags": ["llama-cpp", "gguf-my-repo"]} | DavidAU/Tinyllama-2-1b-miniguanaco-Q8_0-GGUF | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"region:us"
] | null | 2024-04-20T04:38:28+00:00 | [] | [] | TAGS
#gguf #llama-cpp #gguf-my-repo #region-us
|
# DavidAU/Tinyllama-2-1b-miniguanaco-Q8_0-GGUF
This model was converted to GGUF format from 'abdgrt/Tinyllama-2-1b-miniguanaco' using URL via the URL's GGUF-my-repo space.
Refer to the original model card for more details on the model.
## Use with URL
Install URL through brew.
Invoke the URL server or the CLI.
CLI:
Server:
Note: You can also use this checkpoint directly through the usage steps listed in the URL repo as well.
| [
"# DavidAU/Tinyllama-2-1b-miniguanaco-Q8_0-GGUF\nThis model was converted to GGUF format from 'abdgrt/Tinyllama-2-1b-miniguanaco' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.",
"## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well."
] | [
"TAGS\n#gguf #llama-cpp #gguf-my-repo #region-us \n",
"# DavidAU/Tinyllama-2-1b-miniguanaco-Q8_0-GGUF\nThis model was converted to GGUF format from 'abdgrt/Tinyllama-2-1b-miniguanaco' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.",
"## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well."
] |
null | null |
# DavidAU/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft-Q8_0-GGUF
This model was converted to GGUF format from [`abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft`](https://huggingface.co/abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo DavidAU/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft-Q8_0-GGUF --model tinyllama-1.1b-openhermes-2.5-chat-v0.1-sft.Q8_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo DavidAU/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft-Q8_0-GGUF --model tinyllama-1.1b-openhermes-2.5-chat-v0.1-sft.Q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m tinyllama-1.1b-openhermes-2.5-chat-v0.1-sft.Q8_0.gguf -n 128
```
| {"language": ["en"], "license": "apache-2.0", "tags": ["llama-cpp", "gguf-my-repo"], "datasets": ["teknium/OpenHermes-2.5", "abhinand/ultrachat_200k_sharegpt"], "model-index": [{"name": "TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "type": "ai2_arc", "config": "ARC-Challenge", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "acc_norm", "value": 33.79, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "HellaSwag (10-Shot)", "type": "hellaswag", "split": "validation", "args": {"num_few_shot": 10}}, "metrics": [{"type": "acc_norm", "value": 58.72, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "MMLU (5-Shot)", "type": "cais/mmlu", "config": "all", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 24.52, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "TruthfulQA (0-shot)", "type": "truthful_qa", "config": "multiple_choice", "split": "validation", "args": {"num_few_shot": 0}}, "metrics": [{"type": "mc2", "value": 36.22}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "Winogrande (5-shot)", "type": "winogrande", "config": "winogrande_xl", "split": "validation", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 60.93, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "GSM8k (5-shot)", "type": "gsm8k", "config": "main", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 5.38, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft", "name": "Open LLM Leaderboard"}}]}]} | DavidAU/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft-Q8_0-GGUF | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"dataset:teknium/OpenHermes-2.5",
"dataset:abhinand/ultrachat_200k_sharegpt",
"license:apache-2.0",
"model-index",
"region:us"
] | null | 2024-04-20T04:38:39+00:00 | [] | [
"en"
] | TAGS
#gguf #llama-cpp #gguf-my-repo #en #dataset-teknium/OpenHermes-2.5 #dataset-abhinand/ultrachat_200k_sharegpt #license-apache-2.0 #model-index #region-us
|
# DavidAU/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft-Q8_0-GGUF
This model was converted to GGUF format from 'abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft' using URL via the URL's GGUF-my-repo space.
Refer to the original model card for more details on the model.
## Use with URL
Install URL through brew.
Invoke the URL server or the CLI.
CLI:
Server:
Note: You can also use this checkpoint directly through the usage steps listed in the URL repo as well.
| [
"# DavidAU/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft-Q8_0-GGUF\nThis model was converted to GGUF format from 'abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.",
"## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well."
] | [
"TAGS\n#gguf #llama-cpp #gguf-my-repo #en #dataset-teknium/OpenHermes-2.5 #dataset-abhinand/ultrachat_200k_sharegpt #license-apache-2.0 #model-index #region-us \n",
"# DavidAU/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft-Q8_0-GGUF\nThis model was converted to GGUF format from 'abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v0.1-sft' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.",
"## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well."
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed] | {"library_name": "transformers", "tags": []} | IntervitensInc/intv_l3_mk4 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T04:39:08+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
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## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | sparsh35/gemman1.12bnormalawq | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-20T04:40:00+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #gemma #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #gemma #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
null | transformers |
# DavidAU/Heimer-dpo-TinyLlama-1.1B-Q8_0-GGUF
This model was converted to GGUF format from [`abideen/Heimer-dpo-TinyLlama-1.1B`](https://huggingface.co/abideen/Heimer-dpo-TinyLlama-1.1B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/abideen/Heimer-dpo-TinyLlama-1.1B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo DavidAU/Heimer-dpo-TinyLlama-1.1B-Q8_0-GGUF --model heimer-dpo-tinyllama-1.1b.Q8_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo DavidAU/Heimer-dpo-TinyLlama-1.1B-Q8_0-GGUF --model heimer-dpo-tinyllama-1.1b.Q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m heimer-dpo-tinyllama-1.1b.Q8_0.gguf -n 128
```
| {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T", "abideen/Heimer-ipo-TinyLlama-1.1B", "abideen/Heimer-kto-TinyLlama-1.1B", "Intel/orca_dpo_pairs", "llama-cpp", "gguf-my-repo"], "datasets": ["Intel/orca_dpo_pairs"]} | DavidAU/Heimer-dpo-TinyLlama-1.1B-Q8_0-GGUF | null | [
"transformers",
"gguf",
"TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T",
"abideen/Heimer-ipo-TinyLlama-1.1B",
"abideen/Heimer-kto-TinyLlama-1.1B",
"Intel/orca_dpo_pairs",
"llama-cpp",
"gguf-my-repo",
"en",
"dataset:Intel/orca_dpo_pairs",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T04:41:07+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T #abideen/Heimer-ipo-TinyLlama-1.1B #abideen/Heimer-kto-TinyLlama-1.1B #Intel/orca_dpo_pairs #llama-cpp #gguf-my-repo #en #dataset-Intel/orca_dpo_pairs #license-apache-2.0 #endpoints_compatible #region-us
|
# DavidAU/Heimer-dpo-TinyLlama-1.1B-Q8_0-GGUF
This model was converted to GGUF format from 'abideen/Heimer-dpo-TinyLlama-1.1B' using URL via the URL's GGUF-my-repo space.
Refer to the original model card for more details on the model.
## Use with URL
Install URL through brew.
Invoke the URL server or the CLI.
CLI:
Server:
Note: You can also use this checkpoint directly through the usage steps listed in the URL repo as well.
| [
"# DavidAU/Heimer-dpo-TinyLlama-1.1B-Q8_0-GGUF\nThis model was converted to GGUF format from 'abideen/Heimer-dpo-TinyLlama-1.1B' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.",
"## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well."
] | [
"TAGS\n#transformers #gguf #TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T #abideen/Heimer-ipo-TinyLlama-1.1B #abideen/Heimer-kto-TinyLlama-1.1B #Intel/orca_dpo_pairs #llama-cpp #gguf-my-repo #en #dataset-Intel/orca_dpo_pairs #license-apache-2.0 #endpoints_compatible #region-us \n",
"# DavidAU/Heimer-dpo-TinyLlama-1.1B-Q8_0-GGUF\nThis model was converted to GGUF format from 'abideen/Heimer-dpo-TinyLlama-1.1B' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.",
"## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well."
] |
null | null |
# DavidAU/TinyLlama-1.1B-chat-dare-v1-Q8_0-GGUF
This model was converted to GGUF format from [`abuelnasr/TinyLlama-1.1B-chat-dare-v1`](https://huggingface.co/abuelnasr/TinyLlama-1.1B-chat-dare-v1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/abuelnasr/TinyLlama-1.1B-chat-dare-v1) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo DavidAU/TinyLlama-1.1B-chat-dare-v1-Q8_0-GGUF --model tinyllama-1.1b-chat-dare-v1.Q8_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo DavidAU/TinyLlama-1.1B-chat-dare-v1-Q8_0-GGUF --model tinyllama-1.1b-chat-dare-v1.Q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m tinyllama-1.1b-chat-dare-v1.Q8_0.gguf -n 128
```
| {"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "l3utterfly/tinyllama-1.1b-layla-v1", "llama-cpp", "gguf-my-repo"]} | DavidAU/TinyLlama-1.1B-chat-dare-v1-Q8_0-GGUF | null | [
"gguf",
"merge",
"mergekit",
"lazymergekit",
"TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"l3utterfly/tinyllama-1.1b-layla-v1",
"llama-cpp",
"gguf-my-repo",
"license:apache-2.0",
"region:us"
] | null | 2024-04-20T04:41:21+00:00 | [] | [] | TAGS
#gguf #merge #mergekit #lazymergekit #TinyLlama/TinyLlama-1.1B-Chat-v1.0 #l3utterfly/tinyllama-1.1b-layla-v1 #llama-cpp #gguf-my-repo #license-apache-2.0 #region-us
|
# DavidAU/TinyLlama-1.1B-chat-dare-v1-Q8_0-GGUF
This model was converted to GGUF format from 'abuelnasr/TinyLlama-1.1B-chat-dare-v1' using URL via the URL's GGUF-my-repo space.
Refer to the original model card for more details on the model.
## Use with URL
Install URL through brew.
Invoke the URL server or the CLI.
CLI:
Server:
Note: You can also use this checkpoint directly through the usage steps listed in the URL repo as well.
| [
"# DavidAU/TinyLlama-1.1B-chat-dare-v1-Q8_0-GGUF\nThis model was converted to GGUF format from 'abuelnasr/TinyLlama-1.1B-chat-dare-v1' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.",
"## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well."
] | [
"TAGS\n#gguf #merge #mergekit #lazymergekit #TinyLlama/TinyLlama-1.1B-Chat-v1.0 #l3utterfly/tinyllama-1.1b-layla-v1 #llama-cpp #gguf-my-repo #license-apache-2.0 #region-us \n",
"# DavidAU/TinyLlama-1.1B-chat-dare-v1-Q8_0-GGUF\nThis model was converted to GGUF format from 'abuelnasr/TinyLlama-1.1B-chat-dare-v1' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.",
"## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well."
] |
text-generation | transformers |
## Model Name: Multilingual LLM for English, Igbo, and Yoruba Translation
This model specializes in understanding and generating text across English, Igbo, and Yoruba, trained on curated datasets including "tiny_stories" and "dolly_hhrlhf." Utilizing advanced techniques to ensure efficiency and accuracy, the model supports a wide range of applications from translation to content creation.
## Key Features:
- **Trilingual Support:** Seamlessly processes and generates content in English, Igbo, and Yoruba, promoting linguistic diversity and accessibility.
- **Custom Training Approach:** Employs a tailored training setup, leveraging specific prompts and responses to enhance model performance on relevant tasks.
- **High Sequence Capability:** Handles extensive text inputs (up to 3453 tokens), making it suitable for detailed narratives and complex translation tasks.
- **Efficiency Optimizations:** Incorporates strategies such as dataset packing, reducing computational demands while maintaining high-quality output.
## Applications:
- **Translation Services:** Offers precise, context-aware translations, bridging communication gaps between English, Igbo, and Yoruba speakers.
- **Content Generation:** Generates culturally and linguistically nuanced content, catering to a diverse audience.
- **Educational Tools:** Assists in language learning and preservation, providing resources in underrepresented languages.
## Future Directions:
- Further refinement with diverse text sources to enhance understanding and generation capabilities.
- Expansion to additional languages, supporting broader multilingual communication.
This model represents a step towards more inclusive language technologies, recognizing the importance of language diversity in global communication.
## Training Loss:
- Below is the training loss.
| Step | Training Loss |
|------|---------------|
| 200 | 1.490900 |
| 400 | 1.375600 |
| 600 | 1.304100 |
| 800 | 1.198700 |
| 1000 | 1.228200 |
| 1125 | 1.226600 |
## About the Creators
[Christopher Ibe](https://www.linkedin.com/in/christopher-ibe-ekeocha/) and [Okezie Okoye](https://www.linkedin.com/in/okezie-okoye-43432b62/) continue to lead Hypa AI towards new frontiers in AI translation. Their dedication to leveraging advanced AI for genuine understanding and connection across language barriers is what sets Hypa AI apart in the field of artificial intelligence.
*Hypa AI* remains steadfast in its mission to pioneer intelligent solutions that are not just technologically advanced but are also culturally aware, ensuring that the future of AI is as diverse and inclusive as the world it serves.
*AfroVoices*, a subsidiary of Hypa AI, is dedicated to amplifying African voices, languages, and cultures in the intelligence age. Focused on bridging the digital representation gap, AfroVoices curates datasets and resources for African languages, promoting inclusivity and cultural appreciation in AI technologies. Their mission goes beyond technological innovation, aiming to celebrate the richness of African linguistic diversity on a global stage.
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"language": ["ig", "yo", "en"], "license": "apache-2.0", "library_name": "transformers", "datasets": ["ccibeekeoc42/TinyStories_yoruba", "ccibeekeoc42/TinyStories_igbo", "ccibeekeoc42/DollyHHRLHF_igbo", "ccibeekeoc42/DollyHHRLHF_yoruba"], "metrics": ["accuracy"], "pipeline_tag": "text-generation"} | ccibeekeoc42/Llama3-8b-chat-SFT-2024-04-20 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"ig",
"yo",
"en",
"dataset:ccibeekeoc42/TinyStories_yoruba",
"dataset:ccibeekeoc42/TinyStories_igbo",
"dataset:ccibeekeoc42/DollyHHRLHF_igbo",
"dataset:ccibeekeoc42/DollyHHRLHF_yoruba",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T04:42:33+00:00 | [
"1910.09700"
] | [
"ig",
"yo",
"en"
] | TAGS
#transformers #pytorch #llama #text-generation #conversational #ig #yo #en #dataset-ccibeekeoc42/TinyStories_yoruba #dataset-ccibeekeoc42/TinyStories_igbo #dataset-ccibeekeoc42/DollyHHRLHF_igbo #dataset-ccibeekeoc42/DollyHHRLHF_yoruba #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Name: Multilingual LLM for English, Igbo, and Yoruba Translation
----------------------------------------------------------------------
This model specializes in understanding and generating text across English, Igbo, and Yoruba, trained on curated datasets including "tiny\_stories" and "dolly\_hhrlhf." Utilizing advanced techniques to ensure efficiency and accuracy, the model supports a wide range of applications from translation to content creation.
Key Features:
-------------
* Trilingual Support: Seamlessly processes and generates content in English, Igbo, and Yoruba, promoting linguistic diversity and accessibility.
* Custom Training Approach: Employs a tailored training setup, leveraging specific prompts and responses to enhance model performance on relevant tasks.
* High Sequence Capability: Handles extensive text inputs (up to 3453 tokens), making it suitable for detailed narratives and complex translation tasks.
* Efficiency Optimizations: Incorporates strategies such as dataset packing, reducing computational demands while maintaining high-quality output.
Applications:
-------------
* Translation Services: Offers precise, context-aware translations, bridging communication gaps between English, Igbo, and Yoruba speakers.
* Content Generation: Generates culturally and linguistically nuanced content, catering to a diverse audience.
* Educational Tools: Assists in language learning and preservation, providing resources in underrepresented languages.
Future Directions:
------------------
* Further refinement with diverse text sources to enhance understanding and generation capabilities.
* Expansion to additional languages, supporting broader multilingual communication.
This model represents a step towards more inclusive language technologies, recognizing the importance of language diversity in global communication.
Training Loss:
--------------
* Below is the training loss.
About the Creators
------------------
Christopher Ibe and Okezie Okoye continue to lead Hypa AI towards new frontiers in AI translation. Their dedication to leveraging advanced AI for genuine understanding and connection across language barriers is what sets Hypa AI apart in the field of artificial intelligence.
*Hypa AI* remains steadfast in its mission to pioneer intelligent solutions that are not just technologically advanced but are also culturally aware, ensuring that the future of AI is as diverse and inclusive as the world it serves.
*AfroVoices*, a subsidiary of Hypa AI, is dedicated to amplifying African voices, languages, and cultures in the intelligence age. Focused on bridging the digital representation gap, AfroVoices curates datasets and resources for African languages, promoting inclusivity and cultural appreciation in AI technologies. Their mission goes beyond technological innovation, aiming to celebrate the richness of African linguistic diversity on a global stage.
---
Model Card for Model ID
=======================
Model Details
-------------
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
* Developed by:
* Funded by [optional]:
* Shared by [optional]:
* Model type:
* Language(s) (NLP):
* License:
* Finetuned from model [optional]:
### Model Sources [optional]
* Repository:
* Paper [optional]:
* Demo [optional]:
Uses
----
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
Bias, Risks, and Limitations
----------------------------
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
---------------------------------
Use the code below to get started with the model.
Training Details
----------------
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
* Training regime:
#### Speeds, Sizes, Times [optional]
Evaluation
----------
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
Model Examination [optional]
----------------------------
Environmental Impact
--------------------
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
* Hardware Type:
* Hours used:
* Cloud Provider:
* Compute Region:
* Carbon Emitted:
Technical Specifications [optional]
-----------------------------------
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
Glossary [optional]
-------------------
More Information [optional]
---------------------------
Model Card Authors [optional]
-----------------------------
Model Card Contact
------------------
| [
"### Model Description\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n\n* Developed by:\n* Funded by [optional]:\n* Shared by [optional]:\n* Model type:\n* Language(s) (NLP):\n* License:\n* Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n* Repository:\n* Paper [optional]:\n* Demo [optional]:\n\n\nUses\n----",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use\n\n\nBias, Risks, and Limitations\n----------------------------",
"### Recommendations\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.\n\n\nHow to Get Started with the Model\n---------------------------------\n\n\nUse the code below to get started with the model.\n\n\nTraining Details\n----------------",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n\n* Training regime:",
"#### Speeds, Sizes, Times [optional]\n\n\nEvaluation\n----------",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary\n\n\nModel Examination [optional]\n----------------------------\n\n\nEnvironmental Impact\n--------------------\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n\n* Hardware Type:\n* Hours used:\n* Cloud Provider:\n* Compute Region:\n* Carbon Emitted:\n\n\nTechnical Specifications [optional]\n-----------------------------------",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n[optional]\n\n\nBibTeX:\n\n\nAPA:\n\n\nGlossary [optional]\n-------------------\n\n\nMore Information [optional]\n---------------------------\n\n\nModel Card Authors [optional]\n-----------------------------\n\n\nModel Card Contact\n------------------"
] | [
"TAGS\n#transformers #pytorch #llama #text-generation #conversational #ig #yo #en #dataset-ccibeekeoc42/TinyStories_yoruba #dataset-ccibeekeoc42/TinyStories_igbo #dataset-ccibeekeoc42/DollyHHRLHF_igbo #dataset-ccibeekeoc42/DollyHHRLHF_yoruba #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Model Description\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n\n* Developed by:\n* Funded by [optional]:\n* Shared by [optional]:\n* Model type:\n* Language(s) (NLP):\n* License:\n* Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n* Repository:\n* Paper [optional]:\n* Demo [optional]:\n\n\nUses\n----",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use\n\n\nBias, Risks, and Limitations\n----------------------------",
"### Recommendations\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.\n\n\nHow to Get Started with the Model\n---------------------------------\n\n\nUse the code below to get started with the model.\n\n\nTraining Details\n----------------",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n\n* Training regime:",
"#### Speeds, Sizes, Times [optional]\n\n\nEvaluation\n----------",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary\n\n\nModel Examination [optional]\n----------------------------\n\n\nEnvironmental Impact\n--------------------\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n\n* Hardware Type:\n* Hours used:\n* Cloud Provider:\n* Compute Region:\n* Carbon Emitted:\n\n\nTechnical Specifications [optional]\n-----------------------------------",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n[optional]\n\n\nBibTeX:\n\n\nAPA:\n\n\nGlossary [optional]\n-------------------\n\n\nMore Information [optional]\n---------------------------\n\n\nModel Card Authors [optional]\n-----------------------------\n\n\nModel Card Contact\n------------------"
] |
feature-extraction | transformers |
<h1 align="center">UForm</h1>
<h3 align="center">
Pocket-Sized Multimodal AI<br/>
For Content Understanding and Generation<br/>
In Python, JavaScript, and Swift<br/>
</h3>
---
The `uform3-image-text-multilingual-base` UForm model is a tiny vision and multilingual language encoder, covering __21 languages__, mapping them into a shared vector space.
This model produces up to __256-dimensional embeddings__ and is made of:
* Text encoder: 12-layer BERT for up to 50 input tokens.
* Visual encoder: ViT-B/16 for images of 224 x 224 resolution.
Unlike most CLIP-like multomodal models, this model shares 4 layers between the text and visual encoder to allow for more data- and parameter-efficient training.
Also unlike most models, UForm provides checkpoints compatible with PyTorch, ONNX, and CoreML, covering the absolute majority of AI-capable devices, with pre-quantized weights and inference code.
If you need a larger, more accurate, or multilingual model, check our [HuggingFace Hub](https://huggingface.co/unum-cloud/).
For more details on running the model, check out the [UForm GitHub repository](https://github.com/unum-cloud/uform/).
## Evaluation
For all evaluations, the multimodal part was used unless otherwise stated.
### Monolingual
| Dataset | Recall@1 | Recall@5 | Recall@10 |
| :-------- | ------: | --------: | --------: |
| Zero-Shot Flickr | 0.558 | 0.813 | 0.874 |
| MS-COCO ¹ | 0.401 | 0.680 | 0.781 |
> ¹ It's important to note, that the MS-COCO train split was present in the training data.
### Multilingual
Recall@10 on the [XTD-10](https://github.com/adobe-research/Cross-lingual-Test-Dataset-XTD10) dataset:
| English | German | Spanish | French | Italian | Russian | Japanese | Korean | Turkish | Chinese | Polish |
| -------: | -------: | -------: | -------: | -------: | -------: | -------: | -------: | -------: | -------: | ------:|
| 96.1 | 93.5 | 95.7 | 94.1 | 94.4 | 90.4 | 90.2 | 91.3 | 95.2 | 93.8 | 95.8 |
Recall@1, Recall@5, and Recall@10 on the [COCO-SM](https://github.com/kimihailv/coco-sm/tree/main) dataset:
| Target Language | OpenCLIP @ 1 | UForm @ 1 | OpenCLIP @ 5 | UForm @ 5 | OpenCLIP @ 10 | UForm @ 10 | Speakers |
| :-------------------- | -----------: | ------------: | -----------: | -------------:| ------------: | --------------:| -------: |
| Arabic | 22.7 | **31.7** | 44.9 | **57.8** | 55.8 | **69.2** | 274 M |
| Armenian | 5.6 | **22.0** | 14.3 | **44.7** | 20.2 | **56.0** | 4 M |
| Chinese | 27.3 | **32.2** | 51.3 | **59.0** | 62.1 | **70.5** | 1'118 M |
| English | **37.8** | 37.7 | 63.5 | **65.0** | 73.5 | **75.9** | 1'452 M |
| French | 31.3 | **35.4** | 56.5 | **62.6** | 67.4 | **73.3** | 274 M |
| German | 31.7 | **35.1** | 56.9 | **62.2** | 67.4 | **73.3** | 134 M |
| Hebrew | 23.7 | **26.7** | 46.3 | **51.8** | 57.0 | **63.5** | 9 M |
| Hindi | 20.7 | **31.3** | 42.5 | **57.9** | 53.7 | **69.6** | 602 M |
| Indonesian | 26.9 | **30.7** | 51.4 | **57.0** | 62.7 | **68.6** | 199 M |
| Italian | 31.3 | **34.9** | 56.7 | **62.1** | 67.1 | **73.1** | 67 M |
| Japanese | 27.4 | **32.6** | 51.5 | **59.2** | 62.6 | **70.6** | 125 M |
| Korean | 24.4 | **31.5** | 48.1 | **57.8** | 59.2 | **69.2** | 81 M |
| Persian | 24.0 | **28.8** | 47.0 | **54.6** | 57.8 | **66.2** | 77 M |
| Polish | 29.2 | **33.6** | 53.9 | **60.1** | 64.7 | **71.3** | 41 M |
| Portuguese | 31.6 | **32.7** | 57.1 | **59.6** | 67.9 | **71.0** | 257 M |
| Russian | 29.9 | **33.9** | 54.8 | **60.9** | 65.8 | **72.0** | 258 M |
| Spanish | 32.6 | **35.6** | 58.0 | **62.8** | 68.8 | **73.7** | 548 M |
| Thai | 21.5 | **28.7** | 43.0 | **54.6** | 53.7 | **66.0** | 61 M |
| Turkish | 25.5 | **33.0** | 49.1 | **59.6** | 60.3 | **70.8** | 88 M |
| Ukranian | 26.0 | **30.6** | 49.9 | **56.7** | 60.9 | **68.1** | 41 M |
| Vietnamese | 25.4 | **28.3** | 49.2 | **53.9** | 60.3 | **65.5** | 85 M |
| | | | | | | | |
| Mean | 26.5±6.4 | **31.8±3.5** | 49.8±9.8 | **58.1±4.5** | 60.4±10.6 | **69.4±4.3** | - |
| Google Translate | 27.4±6.3 | **31.5±3.5** | 51.1±9.5 | **57.8±4.4** | 61.7±10.3 | **69.1±4.3** | - |
| Microsoft Translator | 27.2±6.4 | **31.4±3.6** | 50.8±9.8 | **57.7±4.7** | 61.4±10.6 | **68.9±4.6** | - |
| Meta NLLB | 24.9±6.7 | **32.4±3.5** | 47.5±10.3 | **58.9±4.5** | 58.2±11.2 | **70.2±4.3** | - |
For a deeper comparison of output ranking check the following table for the Normalized Discounted Cumulative Gains for the first 20 results - NDCG@20:
| | Arabic | Armenian | Chinese | French | German | Hebrew | Hindi | Indonesian | Italian | Japanese | Korean | Persian | Polish | Portuguese | Russian | Spanish | Thai | Turkish | Ukranian | Vietnamese | Mean (all) | Mean (Google Translate) | Mean(Microsoft Translator) | Mean(NLLB)
| :------------ | ----: | ----: | ----: | ----: | ----: | ----: | ----: | ----: | ----: | ----: | ----: | ----: | ----: | ----: | ----: | ----: | ----: | ----: | ----: | ----: | ----: | ----: | ----: | ----: |
| OpenCLIP NDCG | 0.639 | 0.204 | 0.731 | 0.823 | 0.806 | 0.657 | 0.616 | 0.733 | 0.811 | 0.737 | 0.686 | 0.667 | 0.764 | 0.832 | 0.777 | 0.849 | 0.606 | 0.701 | 0.704 | 0.697 | 0.716 ± 0.149 | 0.732 ± 0.145 | 0.730 ± 0.149 | 0.686 ± 0.158
| UForm NDCG | 0.868 | 0.691 | 0.880 | 0.932 | 0.927 | 0.791 | 0.879 | 0.870 | 0.930 | 0.885 | 0.869 | 0.831 | 0.897 | 0.897 | 0.906 | 0.939 | 0.822 | 0.898 | 0.851 | 0.818 | 0.875 ± 0.064 | 0.869 ± 0.063 | 0.869 ± 0.066 | 0.888 ± 0.064
## Installation
```bash
pip install "uform[torch,onnx]"
```
## Usage
To load the model:
```python
from uform import get_model, Modality
import requests
from io import BytesIO
from PIL import Image
model_name = 'unum-cloud/uform3-image-text-multilingual-base'
modalities = [Modality.TEXT_ENCODER, Modality.IMAGE_ENCODER]
processors, models = get_model(model_name, modalities=modalities)
model_text = models[Modality.TEXT_ENCODER]
model_image = models[Modality.IMAGE_ENCODER]
processor_text = processors[Modality.TEXT_ENCODER]
processor_image = processors[Modality.IMAGE_ENCODER]
```
To encode the content:
```python
text = 'a cityscape bathed in the warm glow of the sun, with varied architecture and a towering, snow-capped mountain rising majestically in the background'
image_url = 'https://media-cdn.tripadvisor.com/media/photo-s/1b/28/6b/53/lovely-armenia.jpg'
image_url = Image.open(BytesIO(requests.get(image_url).content))
image_data = processor_image(image)
text_data = processor_text(text)
image_features, image_embedding = model_image.encode(image_data, return_features=True)
text_features, text_embedding = model_text.encode(text_data, return_features=True)
```
| {"language": ["en", "ar", "hy", "zh", "fr", "de", "he", "hi", "id", "it", "ja", "ko", "fa", "pl", "pt", "ru", "es", "th", "tr", "uk", "vi"], "license": "apache-2.0", "tags": ["clip", "vision"], "datasets": ["sbu_captions", "visual_genome", "ChristophSchuhmann/MS_COCO_2017_URL_TEXT", "Ziyang/yfcc15m"], "pipeline_tag": "feature-extraction"} | unum-cloud/uform3-image-text-multilingual-base | null | [
"transformers",
"coreml",
"onnx",
"clip",
"vision",
"feature-extraction",
"en",
"ar",
"hy",
"zh",
"fr",
"de",
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"es",
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"uk",
"vi",
"dataset:sbu_captions",
"dataset:visual_genome",
"dataset:ChristophSchuhmann/MS_COCO_2017_URL_TEXT",
"dataset:Ziyang/yfcc15m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T04:43:12+00:00 | [] | [
"en",
"ar",
"hy",
"zh",
"fr",
"de",
"he",
"hi",
"id",
"it",
"ja",
"ko",
"fa",
"pl",
"pt",
"ru",
"es",
"th",
"tr",
"uk",
"vi"
] | TAGS
#transformers #coreml #onnx #clip #vision #feature-extraction #en #ar #hy #zh #fr #de #he #hi #id #it #ja #ko #fa #pl #pt #ru #es #th #tr #uk #vi #dataset-sbu_captions #dataset-visual_genome #dataset-ChristophSchuhmann/MS_COCO_2017_URL_TEXT #dataset-Ziyang/yfcc15m #license-apache-2.0 #endpoints_compatible #region-us
| UForm
=====
###
Pocket-Sized Multimodal AI
For Content Understanding and Generation
In Python, JavaScript, and Swift
---
The 'uform3-image-text-multilingual-base' UForm model is a tiny vision and multilingual language encoder, covering **21 languages**, mapping them into a shared vector space.
This model produces up to **256-dimensional embeddings** and is made of:
* Text encoder: 12-layer BERT for up to 50 input tokens.
* Visual encoder: ViT-B/16 for images of 224 x 224 resolution.
Unlike most CLIP-like multomodal models, this model shares 4 layers between the text and visual encoder to allow for more data- and parameter-efficient training.
Also unlike most models, UForm provides checkpoints compatible with PyTorch, ONNX, and CoreML, covering the absolute majority of AI-capable devices, with pre-quantized weights and inference code.
If you need a larger, more accurate, or multilingual model, check our HuggingFace Hub.
For more details on running the model, check out the UForm GitHub repository.
Evaluation
----------
For all evaluations, the multimodal part was used unless otherwise stated.
### Monolingual
>
> ¹ It's important to note, that the MS-COCO train split was present in the training data.
>
>
>
### Multilingual
Recall@10 on the XTD-10 dataset:
Recall@1, Recall@5, and Recall@10 on the COCO-SM dataset:
For a deeper comparison of output ranking check the following table for the Normalized Discounted Cumulative Gains for the first 20 results - NDCG@20:
Installation
------------
Usage
-----
To load the model:
To encode the content:
| [
"### \nPocket-Sized Multimodal AI\nFor Content Understanding and Generation\nIn Python, JavaScript, and Swift\n\n\n\n\n---\n\n\nThe 'uform3-image-text-multilingual-base' UForm model is a tiny vision and multilingual language encoder, covering **21 languages**, mapping them into a shared vector space.\nThis model produces up to **256-dimensional embeddings** and is made of:\n\n\n* Text encoder: 12-layer BERT for up to 50 input tokens.\n* Visual encoder: ViT-B/16 for images of 224 x 224 resolution.\n\n\nUnlike most CLIP-like multomodal models, this model shares 4 layers between the text and visual encoder to allow for more data- and parameter-efficient training.\nAlso unlike most models, UForm provides checkpoints compatible with PyTorch, ONNX, and CoreML, covering the absolute majority of AI-capable devices, with pre-quantized weights and inference code.\nIf you need a larger, more accurate, or multilingual model, check our HuggingFace Hub.\nFor more details on running the model, check out the UForm GitHub repository.\n\n\nEvaluation\n----------\n\n\nFor all evaluations, the multimodal part was used unless otherwise stated.",
"### Monolingual\n\n\n\n\n> \n> ¹ It's important to note, that the MS-COCO train split was present in the training data.\n> \n> \n>",
"### Multilingual\n\n\nRecall@10 on the XTD-10 dataset:\n\n\n\nRecall@1, Recall@5, and Recall@10 on the COCO-SM dataset:\n\n\n\nFor a deeper comparison of output ranking check the following table for the Normalized Discounted Cumulative Gains for the first 20 results - NDCG@20:\n\n\n\nInstallation\n------------\n\n\nUsage\n-----\n\n\nTo load the model:\n\n\nTo encode the content:"
] | [
"TAGS\n#transformers #coreml #onnx #clip #vision #feature-extraction #en #ar #hy #zh #fr #de #he #hi #id #it #ja #ko #fa #pl #pt #ru #es #th #tr #uk #vi #dataset-sbu_captions #dataset-visual_genome #dataset-ChristophSchuhmann/MS_COCO_2017_URL_TEXT #dataset-Ziyang/yfcc15m #license-apache-2.0 #endpoints_compatible #region-us \n",
"### \nPocket-Sized Multimodal AI\nFor Content Understanding and Generation\nIn Python, JavaScript, and Swift\n\n\n\n\n---\n\n\nThe 'uform3-image-text-multilingual-base' UForm model is a tiny vision and multilingual language encoder, covering **21 languages**, mapping them into a shared vector space.\nThis model produces up to **256-dimensional embeddings** and is made of:\n\n\n* Text encoder: 12-layer BERT for up to 50 input tokens.\n* Visual encoder: ViT-B/16 for images of 224 x 224 resolution.\n\n\nUnlike most CLIP-like multomodal models, this model shares 4 layers between the text and visual encoder to allow for more data- and parameter-efficient training.\nAlso unlike most models, UForm provides checkpoints compatible with PyTorch, ONNX, and CoreML, covering the absolute majority of AI-capable devices, with pre-quantized weights and inference code.\nIf you need a larger, more accurate, or multilingual model, check our HuggingFace Hub.\nFor more details on running the model, check out the UForm GitHub repository.\n\n\nEvaluation\n----------\n\n\nFor all evaluations, the multimodal part was used unless otherwise stated.",
"### Monolingual\n\n\n\n\n> \n> ¹ It's important to note, that the MS-COCO train split was present in the training data.\n> \n> \n>",
"### Multilingual\n\n\nRecall@10 on the XTD-10 dataset:\n\n\n\nRecall@1, Recall@5, and Recall@10 on the COCO-SM dataset:\n\n\n\nFor a deeper comparison of output ranking check the following table for the Normalized Discounted Cumulative Gains for the first 20 results - NDCG@20:\n\n\n\nInstallation\n------------\n\n\nUsage\n-----\n\n\nTo load the model:\n\n\nTo encode the content:"
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed] | {"license": "apache-2.0", "library_name": "transformers", "datasets": ["AIGym/function_calling_v2"]} | AIGym/Llama-2-7b-chat-hf-structured-responses | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"dataset:AIGym/function_calling_v2",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T04:43:49+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #dataset-AIGym/function_calling_v2 #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
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## Uses
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## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
## Evaluation
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
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## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #dataset-AIGym/function_calling_v2 #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
automatic-speech-recognition | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": ["asr-leaderboard", "a", "s", "r", "-", "l", "e", "d", "b", "o"]} | zeon8985army/AliasterPrayBig-AliasterBeggBraveWhoAmIV3-en-1-full | null | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"asr-leaderboard",
"a",
"s",
"r",
"-",
"l",
"e",
"d",
"b",
"o",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T04:45:26+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #whisper #automatic-speech-recognition #asr-leaderboard #a #s #r #- #l #e #d #b #o #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #whisper #automatic-speech-recognition #asr-leaderboard #a #s #r #- #l #e #d #b #o #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-generation | transformers |
<img src=https://huggingface.co/lodrick-the-lafted/Copus-2x8B/resolve/main/copus.png>
MoE'd up:
- [dreamgen/opus-v1.2-llama-3-8b](https://huggingface.co/dreamgen/opus-v1.2-llama-3-8b)
- [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct)_
Which were the two most interesting llama3 finetunes as of yet. Resulting model seems OK. It's not on Miqu's level, anyway.
Blah, blah, llama 3 license (no tag for it yet). Also not going to name my model Llama-3-Copus. Come at me, Zuck. | {"license": "llama2"} | blockblockblock/Copus-2x8B-bpw2.25 | null | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"conversational",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T04:49:55+00:00 | [] | [] | TAGS
#transformers #safetensors #mixtral #text-generation #conversational #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<img src=URL
MoE'd up:
- dreamgen/opus-v1.2-llama-3-8b
- NousResearch/Meta-Llama-3-8B-Instruct_
Which were the two most interesting llama3 finetunes as of yet. Resulting model seems OK. It's not on Miqu's level, anyway.
Blah, blah, llama 3 license (no tag for it yet). Also not going to name my model Llama-3-Copus. Come at me, Zuck. | [] | [
"TAGS\n#transformers #safetensors #mixtral #text-generation #conversational #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: yunkimmy/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy"]} | yunkimmy/ppo-Huggy | null | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | null | 2024-04-20T04:50:25+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us
|
# ppo Agent playing Huggy
This is a trained model of a ppo agent playing Huggy
using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your
browser: URL
- A *longer tutorial* to understand how works ML-Agents:
URL
### Resume the training
### Watch your Agent play
You can watch your agent playing directly in your browser
1. If the environment is part of ML-Agents official environments, go to URL
2. Step 1: Find your model_id: yunkimmy/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play
| [
"# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: yunkimmy/ppo-Huggy\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
"TAGS\n#ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us \n",
"# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: yunkimmy/ppo-Huggy\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] |
text-generation | transformers |
## Model Details
Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety.
**Model developers** Meta
**Variations** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants.
**Input** Models input text only.
**Output** Models generate text and code only.
**Model Architecture** Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
<table>
<tr>
<td>
</td>
<td><strong>Training Data</strong>
</td>
<td><strong>Params</strong>
</td>
<td><strong>Context length</strong>
</td>
<td><strong>GQA</strong>
</td>
<td><strong>Token count</strong>
</td>
<td><strong>Knowledge cutoff</strong>
</td>
</tr>
<tr>
<td rowspan="2" >Llama 3
</td>
<td rowspan="2" >A new mix of publicly available online data.
</td>
<td>8B
</td>
<td>8k
</td>
<td>Yes
</td>
<td rowspan="2" >15T+
</td>
<td>March, 2023
</td>
</tr>
<tr>
<td>70B
</td>
<td>8k
</td>
<td>Yes
</td>
<td>December, 2023
</td>
</tr>
</table>
**Llama 3 family of models**. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date** April 18, 2024.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license)
Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
**Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**.
**Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy.
## How to use
This repository contains two versions of Meta-Llama-3-8B-Instruct, for use with transformers and with the original `llama3` codebase.
### Use with transformers
See the snippet below for usage with Transformers:
```python
import transformers
import torch
model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
prompt = pipeline.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = pipeline(
prompt,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])
```
### Use with `llama3`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3)
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Meta-Llama-3-8B-Instruct --include "original/*" --local-dir Meta-Llama-3-8B-Instruct
```
For Hugging Face support, we recommend using transformers or TGI, but a similar command works.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint Pretraining utilized a cumulative** 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program.
<table>
<tr>
<td>
</td>
<td><strong>Time (GPU hours)</strong>
</td>
<td><strong>Power Consumption (W)</strong>
</td>
<td><strong>Carbon Emitted(tCO2eq)</strong>
</td>
</tr>
<tr>
<td>Llama 3 8B
</td>
<td>1.3M
</td>
<td>700
</td>
<td>390
</td>
</tr>
<tr>
<td>Llama 3 70B
</td>
<td>6.4M
</td>
<td>700
</td>
<td>1900
</td>
</tr>
<tr>
<td>Total
</td>
<td>7.7M
</td>
<td>
</td>
<td>2290
</td>
</tr>
</table>
**CO2 emissions during pre-training**. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively.
## Benchmarks
In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md).
### Base pretrained models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama2 7B</strong>
</td>
<td><strong>Llama2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama2 70B</strong>
</td>
</tr>
<tr>
<td rowspan="6" >General
</td>
<td>MMLU (5-shot)
</td>
<td>66.6
</td>
<td>45.7
</td>
<td>53.8
</td>
<td>79.5
</td>
<td>69.7
</td>
</tr>
<tr>
<td>AGIEval English (3-5 shot)
</td>
<td>45.9
</td>
<td>28.8
</td>
<td>38.7
</td>
<td>63.0
</td>
<td>54.8
</td>
</tr>
<tr>
<td>CommonSenseQA (7-shot)
</td>
<td>72.6
</td>
<td>57.6
</td>
<td>67.6
</td>
<td>83.8
</td>
<td>78.7
</td>
</tr>
<tr>
<td>Winogrande (5-shot)
</td>
<td>76.1
</td>
<td>73.3
</td>
<td>75.4
</td>
<td>83.1
</td>
<td>81.8
</td>
</tr>
<tr>
<td>BIG-Bench Hard (3-shot, CoT)
</td>
<td>61.1
</td>
<td>38.1
</td>
<td>47.0
</td>
<td>81.3
</td>
<td>65.7
</td>
</tr>
<tr>
<td>ARC-Challenge (25-shot)
</td>
<td>78.6
</td>
<td>53.7
</td>
<td>67.6
</td>
<td>93.0
</td>
<td>85.3
</td>
</tr>
<tr>
<td>Knowledge reasoning
</td>
<td>TriviaQA-Wiki (5-shot)
</td>
<td>78.5
</td>
<td>72.1
</td>
<td>79.6
</td>
<td>89.7
</td>
<td>87.5
</td>
</tr>
<tr>
<td rowspan="4" >Reading comprehension
</td>
<td>SQuAD (1-shot)
</td>
<td>76.4
</td>
<td>72.2
</td>
<td>72.1
</td>
<td>85.6
</td>
<td>82.6
</td>
</tr>
<tr>
<td>QuAC (1-shot, F1)
</td>
<td>44.4
</td>
<td>39.6
</td>
<td>44.9
</td>
<td>51.1
</td>
<td>49.4
</td>
</tr>
<tr>
<td>BoolQ (0-shot)
</td>
<td>75.7
</td>
<td>65.5
</td>
<td>66.9
</td>
<td>79.0
</td>
<td>73.1
</td>
</tr>
<tr>
<td>DROP (3-shot, F1)
</td>
<td>58.4
</td>
<td>37.9
</td>
<td>49.8
</td>
<td>79.7
</td>
<td>70.2
</td>
</tr>
</table>
### Instruction tuned models
<table>
<tr>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama 2 7B</strong>
</td>
<td><strong>Llama 2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama 2 70B</strong>
</td>
</tr>
<tr>
<td>MMLU (5-shot)
</td>
<td>68.4
</td>
<td>34.1
</td>
<td>47.8
</td>
<td>82.0
</td>
<td>52.9
</td>
</tr>
<tr>
<td>GPQA (0-shot)
</td>
<td>34.2
</td>
<td>21.7
</td>
<td>22.3
</td>
<td>39.5
</td>
<td>21.0
</td>
</tr>
<tr>
<td>HumanEval (0-shot)
</td>
<td>62.2
</td>
<td>7.9
</td>
<td>14.0
</td>
<td>81.7
</td>
<td>25.6
</td>
</tr>
<tr>
<td>GSM-8K (8-shot, CoT)
</td>
<td>79.6
</td>
<td>25.7
</td>
<td>77.4
</td>
<td>93.0
</td>
<td>57.5
</td>
</tr>
<tr>
<td>MATH (4-shot, CoT)
</td>
<td>30.0
</td>
<td>3.8
</td>
<td>6.7
</td>
<td>50.4
</td>
<td>11.6
</td>
</tr>
</table>
### Responsibility & Safety
We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.
Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.
Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.
As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started.
#### Llama 3-Instruct
As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case.
<span style="text-decoration:underline;">Safety</span>
For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable.
<span style="text-decoration:underline;">Refusals</span>
In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2.
We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date.
#### Responsible release
In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.
Misuse
If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/).
#### Critical risks
<span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives)
We have conducted a two fold assessment of the safety of the model in this area:
* Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks.
* Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model).
### <span style="text-decoration:underline;">Cyber Security </span>
We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval).
### <span style="text-decoration:underline;">Child Safety</span>
Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
### Community
Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.
Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide)
## Citation instructions
@article{llama3modelcard,
title={Llama 3 Model Card},
author={AI@Meta},
year={2024},
url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}
## Contributors
Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos
| {"language": ["en"], "license": "other", "tags": ["facebook", "meta", "pytorch", "llama", "llama-3"], "pipeline_tag": "text-generation", "license_name": "llama3", "license_link": "LICENSE", "extra_gated_prompt": "### META LLAMA 3 COMMUNITY LICENSE AGREEMENT\nMeta Llama 3 Version Release Date: April 18, 2024\n\"Agreement\" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein.\n\"Documentation\" means the specifications, manuals and documentation accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/.\n\"Licensee\" or \"you\" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity\u2019s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf.\n\"Meta Llama 3\" means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://llama.meta.com/llama-downloads.\n\"Llama Materials\" means, collectively, Meta\u2019s proprietary Meta Llama 3 and Documentation (and any portion thereof) made available under this Agreement.\n\"Meta\" or \"we\" means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland).\n \n1. License Rights and Redistribution.\na. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta\u2019s intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials.\nb. Redistribution and Use.\ni. If you distribute or make available the Llama Materials (or any derivative works thereof), or a product or service that uses any of them, including another AI model, you shall (A) provide a copy of this Agreement with any such Llama Materials; and (B) prominently display \u201cBuilt with Meta Llama 3\u201d on a related website, user interface, blogpost, about page, or product documentation. If you use the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is distributed or made available, you shall also include \u201cLlama 3\u201d at the beginning of any such AI model name.\nii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you.\niii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a \u201cNotice\u201d text file distributed as a part of such copies: \u201cMeta Llama 3 is licensed under the Meta Llama 3 Community License, Copyright \u00a9 Meta Platforms, Inc. All Rights Reserved.\u201d\niv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at https://llama.meta.com/llama3/use-policy), which is hereby incorporated by reference into this Agreement.\nv. You will not use the Llama Materials or any output or results of the Llama Materials to improve any other large language model (excluding Meta Llama 3 or derivative works thereof).\n2. Additional Commercial Terms. If, on the Meta Llama 3 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee\u2019s affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights.\n3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN \u201cAS IS\u201d BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.\n4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING.\n5. Intellectual Property.\na. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you a license to use \u201cLlama 3\u201d (the \u201cMark\u201d) solely as required to comply with the last sentence of Section 1.b.i. You will comply with Meta\u2019s brand guidelines (currently accessible at https://about.meta.com/brand/resources/meta/company-brand/ ). All goodwill arising out of your use of the Mark will inure to the benefit of Meta.\nb. Subject to Meta\u2019s ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications.\nc. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Meta Llama 3 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials.\n6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement.\n7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement.\n### Meta Llama 3 Acceptable Use Policy\nMeta is committed to promoting safe and fair use of its tools and features, including Meta Llama 3. If you access or use Meta Llama 3, you agree to this Acceptable Use Policy (\u201cPolicy\u201d). The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy)\n#### Prohibited Uses\nWe want everyone to use Meta Llama 3 safely and responsibly. You agree you will not use, or allow others to use, Meta Llama 3 to: 1. Violate the law or others\u2019 rights, including to:\n 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:\n 1. Violence or terrorism\n 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material\n 3. Human trafficking, exploitation, and sexual violence\n 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.\n 5. Sexual solicitation\n 6. Any other criminal activity\n 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals\n 3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services\n 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices\n 5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws\n 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials\n 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system\n2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Meta Llama 3 related to the following:\n 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State\n 2. Guns and illegal weapons (including weapon development)\n 3. Illegal drugs and regulated/controlled substances\n 4. Operation of critical infrastructure, transportation technologies, or heavy machinery\n 5. Self-harm or harm to others, including suicide, cutting, and eating disorders\n 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual\n3. Intentionally deceive or mislead others, including use of Meta Llama 3 related to the following:\n 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation\n 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content\n 3. Generating, promoting, or further distributing spam\n 4. Impersonating another individual without consent, authorization, or legal right\n 5. Representing that the use of Meta Llama 3 or outputs are human-generated\n 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement\n4. Fail to appropriately disclose to end users any known dangers of your AI system\nPlease report any violation of this Policy, software \u201cbug,\u201d or other problems that could lead to a violation of this Policy through one of the following means:\n * Reporting issues with the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3)\n * Reporting risky content generated by the model:\n developers.facebook.com/llama_output_feedback\n * Reporting bugs and security concerns: facebook.com/whitehat/info\n * Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected]", "extra_gated_fields": {"First Name": "text", "Last Name": "text", "Date of birth": "date_picker", "Country": "country", "Affiliation": "text", "geo": "ip_location", "By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy": "checkbox"}, "extra_gated_description": "The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).", "extra_gated_button_content": "Submit"} | Pranjal4554/dummy_model | null | [
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"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T04:51:41+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #llama #text-generation #facebook #meta #pytorch #llama-3 #conversational #en #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Model Details
-------------
Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety.
Model developers Meta
Variations Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants.
Input Models input text only.
Output Models generate text and code only.
Model Architecture Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
Llama 3 family of models. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability.
Model Release Date April 18, 2024.
Status This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
License A custom commercial license is available at: URL
Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model README. For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go here.
Intended Use
------------
Intended Use Cases Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
Out-of-scope Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English.
Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy.
How to use
----------
This repository contains two versions of Meta-Llama-3-8B-Instruct, for use with transformers and with the original 'llama3' codebase.
### Use with transformers
See the snippet below for usage with Transformers:
### Use with 'llama3'
Please, follow the instructions in the repository
To download Original checkpoints, see the example command below leveraging 'huggingface-cli':
For Hugging Face support, we recommend using transformers or TGI, but a similar command works.
Hardware and Software
---------------------
Training Factors We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
Carbon Footprint Pretraining utilized a cumulative 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program.
CO2 emissions during pre-training. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
Training Data
-------------
Overview Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
Data Freshness The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively.
Benchmarks
----------
In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see here.
### Base pretrained models
### Instruction tuned models
### Responsibility & Safety
We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.
Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.
Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.
As part of the Llama 3 release, we updated our Responsible Use Guide to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including Meta Llama Guard 2 and Code Shield safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a reference implementation to get you started.
#### Llama 3-Instruct
As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case.
Safety
For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable.
Refusals
In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2.
We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date.
#### Responsible release
In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.
Misuse
If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at URL
#### Critical risks
CBRNE (Chemical, Biological, Radiological, Nuclear, and high yield Explosives)
We have conducted a two fold assessment of the safety of the model in this area:
* Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks.
* Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model).
### Cyber Security
We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of equivalent coding capability.
### Child Safety
Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
### Community
Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our Github repository.
Finally, we put in place a set of resources including an output reporting mechanism and bug bounty program to continuously improve the Llama technology with the help of the community.
Ethical Considerations and Limitations
--------------------------------------
The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating Purple Llama solutions into your workflows and specifically Llama Guard which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.
Please see the Responsible Use Guide available at URL
instructions
@article{llama3modelcard,
title={Llama 3 Model Card},
author={AI@Meta},
year={2024},
url = {URL
}
Contributors
------------
Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos
| [
"### Use with transformers\n\n\nSee the snippet below for usage with Transformers:",
"### Use with 'llama3'\n\n\nPlease, follow the instructions in the repository\n\n\nTo download Original checkpoints, see the example command below leveraging 'huggingface-cli':\n\n\nFor Hugging Face support, we recommend using transformers or TGI, but a similar command works.\n\n\nHardware and Software\n---------------------\n\n\nTraining Factors We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.\n\n\nCarbon Footprint Pretraining utilized a cumulative 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program.\n\n\n\nCO2 emissions during pre-training. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.\n\n\nTraining Data\n-------------\n\n\nOverview Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.\n\n\nData Freshness The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively.\n\n\nBenchmarks\n----------\n\n\nIn this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see here.",
"### Base pretrained models",
"### Instruction tuned models",
"### Responsibility & Safety\n\n\nWe believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.\n\n\nFoundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.\n\n\nRather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.\n\n\nAs part of the Llama 3 release, we updated our Responsible Use Guide to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including Meta Llama Guard 2 and Code Shield safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a reference implementation to get you started.",
"#### Llama 3-Instruct\n\n\nAs outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case.\n\n\nSafety\n\n\nFor our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable.\n\n\nRefusals\n\n\nIn addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2.\n\n\nWe built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date.",
"#### Responsible release\n\n\nIn addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.\n\n\nMisuse\n\n\nIf you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at URL",
"#### Critical risks\n\n\nCBRNE (Chemical, Biological, Radiological, Nuclear, and high yield Explosives)\n\n\nWe have conducted a two fold assessment of the safety of the model in this area:\n\n\n* Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks.\n* Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model).",
"### Cyber Security\n\n\nWe have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of equivalent coding capability.",
"### Child Safety\n\n\nChild Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.",
"### Community\n\n\nGenerative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our Github repository.\n\n\nFinally, we put in place a set of resources including an output reporting mechanism and bug bounty program to continuously improve the Llama technology with the help of the community.\n\n\nEthical Considerations and Limitations\n--------------------------------------\n\n\nThe core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.\n\n\nBut Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating Purple Llama solutions into your workflows and specifically Llama Guard which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.\n\n\nPlease see the Responsible Use Guide available at URL\n\n\ninstructions\n\n\n@article{llama3modelcard,\n\n\ntitle={Llama 3 Model Card},\n\n\nauthor={AI@Meta},\n\n\nyear={2024},\n\n\nurl = {URL\n\n\n}\n\n\nContributors\n------------\n\n\nAaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #facebook #meta #pytorch #llama-3 #conversational #en #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Use with transformers\n\n\nSee the snippet below for usage with Transformers:",
"### Use with 'llama3'\n\n\nPlease, follow the instructions in the repository\n\n\nTo download Original checkpoints, see the example command below leveraging 'huggingface-cli':\n\n\nFor Hugging Face support, we recommend using transformers or TGI, but a similar command works.\n\n\nHardware and Software\n---------------------\n\n\nTraining Factors We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.\n\n\nCarbon Footprint Pretraining utilized a cumulative 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program.\n\n\n\nCO2 emissions during pre-training. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.\n\n\nTraining Data\n-------------\n\n\nOverview Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.\n\n\nData Freshness The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively.\n\n\nBenchmarks\n----------\n\n\nIn this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see here.",
"### Base pretrained models",
"### Instruction tuned models",
"### Responsibility & Safety\n\n\nWe believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.\n\n\nFoundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.\n\n\nRather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.\n\n\nAs part of the Llama 3 release, we updated our Responsible Use Guide to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including Meta Llama Guard 2 and Code Shield safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a reference implementation to get you started.",
"#### Llama 3-Instruct\n\n\nAs outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case.\n\n\nSafety\n\n\nFor our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable.\n\n\nRefusals\n\n\nIn addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2.\n\n\nWe built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date.",
"#### Responsible release\n\n\nIn addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.\n\n\nMisuse\n\n\nIf you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at URL",
"#### Critical risks\n\n\nCBRNE (Chemical, Biological, Radiological, Nuclear, and high yield Explosives)\n\n\nWe have conducted a two fold assessment of the safety of the model in this area:\n\n\n* Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks.\n* Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model).",
"### Cyber Security\n\n\nWe have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of equivalent coding capability.",
"### Child Safety\n\n\nChild Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.",
"### Community\n\n\nGenerative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our Github repository.\n\n\nFinally, we put in place a set of resources including an output reporting mechanism and bug bounty program to continuously improve the Llama technology with the help of the community.\n\n\nEthical Considerations and Limitations\n--------------------------------------\n\n\nThe core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.\n\n\nBut Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating Purple Llama solutions into your workflows and specifically Llama Guard which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.\n\n\nPlease see the Responsible Use Guide available at URL\n\n\ninstructions\n\n\n@article{llama3modelcard,\n\n\ntitle={Llama 3 Model Card},\n\n\nauthor={AI@Meta},\n\n\nyear={2024},\n\n\nurl = {URL\n\n\n}\n\n\nContributors\n------------\n\n\nAaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos"
] |
text-generation | peft |
A finetuning experiment on llama3 8b it with selected 5k examples from argilla dpo 7k | {"language": ["en"], "license": "other", "library_name": "peft", "tags": ["text-generation", "llama", "orpo", "llama3", "text-generation-inference"], "datasets": ["argilla/dpo-mix-7k"], "base_model": "meta-llama/Meta-Llama-3-8B-Instruct", "pipeline_tag": "text-generation"} | eren23/DPOMixLLama-3-8B-lora | null | [
"peft",
"safetensors",
"text-generation",
"llama",
"orpo",
"llama3",
"text-generation-inference",
"conversational",
"en",
"dataset:argilla/dpo-mix-7k",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"license:other",
"region:us"
] | null | 2024-04-20T04:52:25+00:00 | [] | [
"en"
] | TAGS
#peft #safetensors #text-generation #llama #orpo #llama3 #text-generation-inference #conversational #en #dataset-argilla/dpo-mix-7k #base_model-meta-llama/Meta-Llama-3-8B-Instruct #license-other #region-us
|
A finetuning experiment on llama3 8b it with selected 5k examples from argilla dpo 7k | [] | [
"TAGS\n#peft #safetensors #text-generation #llama #orpo #llama3 #text-generation-inference #conversational #en #dataset-argilla/dpo-mix-7k #base_model-meta-llama/Meta-Llama-3-8B-Instruct #license-other #region-us \n"
] |
image-segmentation | null | | Model Name | File Name |
|--------------|---------------------------------------------------|
| tang | self_supervised_nv_swin_unetr_5050.pt |
| jose | self_supervised_nv_swin_unetr_50000.pth |
| univ_swin | supervised_clip_driven_universal_swin_unetr_2100.pth |
| sup_swin | supervised_suprem_swinunetr_2100.pth |
| genesis | self_supervised_models_genesis_unet_620.pt |
| unimiss_tiny | self_supervised_unimiss_nnunet_tiny_5022.pth |
| unimiss_small| self_supervised_unimiss_nnunet_small_5022.pth |
| med3d | supervised_med3D_residual_unet_1623.pth |
| dodnet | supervised_dodnet_unet_920.pth |
| univ_unet | supervised_clip_driven_universal_unet_2100.pth |
| sup_unet | supervised_suprem_unet_2100.pth |
| sup_seg | supervised_suprem_segresnet_2100.pth |
| voco | VoCo_10k.pt | | {"language": ["en"], "license": "mit", "tags": ["medical"], "pipeline_tag": "image-segmentation"} | jethro682/pretrain_mri | null | [
"medical",
"image-segmentation",
"en",
"license:mit",
"region:us"
] | null | 2024-04-20T04:56:21+00:00 | [] | [
"en"
] | TAGS
#medical #image-segmentation #en #license-mit #region-us
| [] | [
"TAGS\n#medical #image-segmentation #en #license-mit #region-us \n"
] |
|
audio-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilhubert-finetuned-gtzan-music-genre-classification
This model is a fine-tuned version of [yuval6967/distilhubert-finetuned-gtzan](https://huggingface.co/yuval6967/distilhubert-finetuned-gtzan) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4478
- Accuracy: 0.935
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 100 | 0.3000 | 0.935 |
| No log | 2.0 | 200 | 0.4770 | 0.905 |
| No log | 3.0 | 300 | 0.5666 | 0.93 |
| No log | 4.0 | 400 | 0.4572 | 0.92 |
| 0.0298 | 5.0 | 500 | 0.6038 | 0.9 |
| 0.0298 | 6.0 | 600 | 0.4111 | 0.925 |
| 0.0298 | 7.0 | 700 | 0.4528 | 0.93 |
| 0.0298 | 8.0 | 800 | 0.4400 | 0.94 |
| 0.0298 | 9.0 | 900 | 0.4638 | 0.935 |
| 0.0081 | 10.0 | 1000 | 0.4478 | 0.935 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["marsyas/gtzan"], "metrics": ["accuracy"], "base_model": "yuval6967/distilhubert-finetuned-gtzan", "model-index": [{"name": "distilhubert-finetuned-gtzan-music-genre-classification", "results": [{"task": {"type": "audio-classification", "name": "Audio Classification"}, "dataset": {"name": "GTZAN", "type": "marsyas/gtzan", "config": "all", "split": "train", "args": "all"}, "metrics": [{"type": "accuracy", "value": 0.935, "name": "Accuracy"}]}]}]} | FredDYyy/distilhubert-finetuned-gtzan-finetuned-gtzan | null | [
"transformers",
"tensorboard",
"safetensors",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:yuval6967/distilhubert-finetuned-gtzan",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T04:58:28+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #hubert #audio-classification #generated_from_trainer #dataset-marsyas/gtzan #base_model-yuval6967/distilhubert-finetuned-gtzan #license-apache-2.0 #model-index #endpoints_compatible #region-us
| distilhubert-finetuned-gtzan-music-genre-classification
=======================================================
This model is a fine-tuned version of yuval6967/distilhubert-finetuned-gtzan on the GTZAN dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4478
* Accuracy: 0.935
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.39.3
* Pytorch 2.1.2
* Datasets 2.18.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.1.2\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tensorboard #safetensors #hubert #audio-classification #generated_from_trainer #dataset-marsyas/gtzan #base_model-yuval6967/distilhubert-finetuned-gtzan #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.1.2\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
null | mlx |
# lucataco/Mixtral-8x7B-v0.1-4bit
This model was converted to MLX format from [`mistralai/Mixtral-8x7B-v0.1`]() using mlx-lm version **0.10.0**.
Refer to the [original model card](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("lucataco/Mixtral-8x7B-v0.1-4bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
| {"language": ["fr", "it", "de", "es", "en"], "license": "apache-2.0", "tags": ["moe", "mlx"]} | lucataco/Mixtral-8x7B-v0.1-4bit | null | [
"mlx",
"safetensors",
"mixtral",
"moe",
"fr",
"it",
"de",
"es",
"en",
"license:apache-2.0",
"region:us"
] | null | 2024-04-20T05:00:40+00:00 | [] | [
"fr",
"it",
"de",
"es",
"en"
] | TAGS
#mlx #safetensors #mixtral #moe #fr #it #de #es #en #license-apache-2.0 #region-us
|
# lucataco/Mixtral-8x7B-v0.1-4bit
This model was converted to MLX format from ['mistralai/Mixtral-8x7B-v0.1']() using mlx-lm version 0.10.0.
Refer to the original model card for more details on the model.
## Use with mlx
| [
"# lucataco/Mixtral-8x7B-v0.1-4bit\nThis model was converted to MLX format from ['mistralai/Mixtral-8x7B-v0.1']() using mlx-lm version 0.10.0.\nRefer to the original model card for more details on the model.",
"## Use with mlx"
] | [
"TAGS\n#mlx #safetensors #mixtral #moe #fr #it #de #es #en #license-apache-2.0 #region-us \n",
"# lucataco/Mixtral-8x7B-v0.1-4bit\nThis model was converted to MLX format from ['mistralai/Mixtral-8x7B-v0.1']() using mlx-lm version 0.10.0.\nRefer to the original model card for more details on the model.",
"## Use with mlx"
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": ["trl", "sft"]} | b2bp8ip/llama_mbpp_syn | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-20T05:04:34+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #trl #sft #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #trl #sft #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "252.43 +/- 16.80", "name": "mean_reward", "verified": false}]}]}]} | binganao/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2024-04-20T05:06:52+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] |
null | transformers | ## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/TigerResearch/tigerbot-70b-chat-v4
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/tigerbot-70b-chat-v4-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/tigerbot-70b-chat-v4-i1-GGUF/resolve/main/tigerbot-70b-chat-v4.i1-IQ1_S.gguf) | i1-IQ1_S | 14.9 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/tigerbot-70b-chat-v4-i1-GGUF/resolve/main/tigerbot-70b-chat-v4.i1-IQ1_M.gguf) | i1-IQ1_M | 16.3 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/tigerbot-70b-chat-v4-i1-GGUF/resolve/main/tigerbot-70b-chat-v4.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 18.7 | |
| [GGUF](https://huggingface.co/mradermacher/tigerbot-70b-chat-v4-i1-GGUF/resolve/main/tigerbot-70b-chat-v4.i1-IQ2_XS.gguf) | i1-IQ2_XS | 20.7 | |
| [GGUF](https://huggingface.co/mradermacher/tigerbot-70b-chat-v4-i1-GGUF/resolve/main/tigerbot-70b-chat-v4.i1-IQ2_S.gguf) | i1-IQ2_S | 21.8 | |
| [GGUF](https://huggingface.co/mradermacher/tigerbot-70b-chat-v4-i1-GGUF/resolve/main/tigerbot-70b-chat-v4.i1-IQ2_M.gguf) | i1-IQ2_M | 23.6 | |
| [GGUF](https://huggingface.co/mradermacher/tigerbot-70b-chat-v4-i1-GGUF/resolve/main/tigerbot-70b-chat-v4.i1-Q2_K.gguf) | i1-Q2_K | 25.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/tigerbot-70b-chat-v4-i1-GGUF/resolve/main/tigerbot-70b-chat-v4.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 27.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/tigerbot-70b-chat-v4-i1-GGUF/resolve/main/tigerbot-70b-chat-v4.i1-IQ3_XS.gguf) | i1-IQ3_XS | 28.8 | |
| [GGUF](https://huggingface.co/mradermacher/tigerbot-70b-chat-v4-i1-GGUF/resolve/main/tigerbot-70b-chat-v4.i1-IQ3_S.gguf) | i1-IQ3_S | 30.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/tigerbot-70b-chat-v4-i1-GGUF/resolve/main/tigerbot-70b-chat-v4.i1-Q3_K_S.gguf) | i1-Q3_K_S | 30.4 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/tigerbot-70b-chat-v4-i1-GGUF/resolve/main/tigerbot-70b-chat-v4.i1-IQ3_M.gguf) | i1-IQ3_M | 31.4 | |
| [GGUF](https://huggingface.co/mradermacher/tigerbot-70b-chat-v4-i1-GGUF/resolve/main/tigerbot-70b-chat-v4.i1-Q3_K_M.gguf) | i1-Q3_K_M | 33.7 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/tigerbot-70b-chat-v4-i1-GGUF/resolve/main/tigerbot-70b-chat-v4.i1-Q3_K_L.gguf) | i1-Q3_K_L | 36.6 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/tigerbot-70b-chat-v4-i1-GGUF/resolve/main/tigerbot-70b-chat-v4.i1-IQ4_XS.gguf) | i1-IQ4_XS | 37.3 | |
| [GGUF](https://huggingface.co/mradermacher/tigerbot-70b-chat-v4-i1-GGUF/resolve/main/tigerbot-70b-chat-v4.i1-Q4_0.gguf) | i1-Q4_0 | 39.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/tigerbot-70b-chat-v4-i1-GGUF/resolve/main/tigerbot-70b-chat-v4.i1-Q4_K_S.gguf) | i1-Q4_K_S | 39.7 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/tigerbot-70b-chat-v4-i1-GGUF/resolve/main/tigerbot-70b-chat-v4.i1-Q4_K_M.gguf) | i1-Q4_K_M | 41.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/tigerbot-70b-chat-v4-i1-GGUF/resolve/main/tigerbot-70b-chat-v4.i1-Q5_K_S.gguf) | i1-Q5_K_S | 48.0 | |
| [GGUF](https://huggingface.co/mradermacher/tigerbot-70b-chat-v4-i1-GGUF/resolve/main/tigerbot-70b-chat-v4.i1-Q5_K_M.gguf) | i1-Q5_K_M | 49.3 | |
| [PART 1](https://huggingface.co/mradermacher/tigerbot-70b-chat-v4-i1-GGUF/resolve/main/tigerbot-70b-chat-v4.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/tigerbot-70b-chat-v4-i1-GGUF/resolve/main/tigerbot-70b-chat-v4.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 57.1 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "base_model": "TigerResearch/tigerbot-70b-chat-v4", "quantized_by": "mradermacher"} | mradermacher/tigerbot-70b-chat-v4-i1-GGUF | null | [
"transformers",
"gguf",
"en",
"base_model:TigerResearch/tigerbot-70b-chat-v4",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-20T05:08:09+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #en #base_model-TigerResearch/tigerbot-70b-chat-v4 #license-apache-2.0 #endpoints_compatible #region-us
| About
-----
weighted/imatrix quants of URL
static quants are available at URL
Usage
-----
If you are unsure how to use GGUF files, refer to one of TheBloke's
READMEs for
more details, including on how to concatenate multi-part files.
Provided Quants
---------------
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
!URL
And here are Artefact2's thoughts on the matter:
URL
FAQ / Model Request
-------------------
See URL for some answers to
questions you might have and/or if you want some other model quantized.
Thanks
------
I thank my company, nethype GmbH, for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
| [] | [
"TAGS\n#transformers #gguf #en #base_model-TigerResearch/tigerbot-70b-chat-v4 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
null | null |
# DavidAU/TinyLlama-1.1B-orca-gpt4-Q8_0-GGUF
This model was converted to GGUF format from [`acalatrava/TinyLlama-1.1B-orca-gpt4`](https://huggingface.co/acalatrava/TinyLlama-1.1B-orca-gpt4) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/acalatrava/TinyLlama-1.1B-orca-gpt4) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo DavidAU/TinyLlama-1.1B-orca-gpt4-Q8_0-GGUF --model tinyllama-1.1b-orca-gpt4.Q8_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo DavidAU/TinyLlama-1.1B-orca-gpt4-Q8_0-GGUF --model tinyllama-1.1b-orca-gpt4.Q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m tinyllama-1.1b-orca-gpt4.Q8_0.gguf -n 128
```
| {"language": ["en"], "license": "apache-2.0", "tags": ["llama-cpp", "gguf-my-repo"], "datasets": ["cerebras/SlimPajama-627B", "bigcode/starcoderdata", "sam-mosaic/orca-gpt4-chatml"]} | DavidAU/TinyLlama-1.1B-orca-gpt4-Q8_0-GGUF | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"dataset:cerebras/SlimPajama-627B",
"dataset:bigcode/starcoderdata",
"dataset:sam-mosaic/orca-gpt4-chatml",
"license:apache-2.0",
"region:us"
] | null | 2024-04-20T05:10:14+00:00 | [] | [
"en"
] | TAGS
#gguf #llama-cpp #gguf-my-repo #en #dataset-cerebras/SlimPajama-627B #dataset-bigcode/starcoderdata #dataset-sam-mosaic/orca-gpt4-chatml #license-apache-2.0 #region-us
|
# DavidAU/TinyLlama-1.1B-orca-gpt4-Q8_0-GGUF
This model was converted to GGUF format from 'acalatrava/TinyLlama-1.1B-orca-gpt4' using URL via the URL's GGUF-my-repo space.
Refer to the original model card for more details on the model.
## Use with URL
Install URL through brew.
Invoke the URL server or the CLI.
CLI:
Server:
Note: You can also use this checkpoint directly through the usage steps listed in the URL repo as well.
| [
"# DavidAU/TinyLlama-1.1B-orca-gpt4-Q8_0-GGUF\nThis model was converted to GGUF format from 'acalatrava/TinyLlama-1.1B-orca-gpt4' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.",
"## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well."
] | [
"TAGS\n#gguf #llama-cpp #gguf-my-repo #en #dataset-cerebras/SlimPajama-627B #dataset-bigcode/starcoderdata #dataset-sam-mosaic/orca-gpt4-chatml #license-apache-2.0 #region-us \n",
"# DavidAU/TinyLlama-1.1B-orca-gpt4-Q8_0-GGUF\nThis model was converted to GGUF format from 'acalatrava/TinyLlama-1.1B-orca-gpt4' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.",
"## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well."
] |
fill-mask | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | emma7897/bert_two | null | [
"transformers",
"safetensors",
"bert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2024-04-20T05:12:05+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #bert #fill-mask #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #bert #fill-mask #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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"## Glossary [optional]",
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"## Model Card Contact"
] |
text-generation | transformers |
<img src=https://huggingface.co/lodrick-the-lafted/Copus-2x8B/resolve/main/copus.png>
MoE'd up:
- [dreamgen/opus-v1.2-llama-3-8b](https://huggingface.co/dreamgen/opus-v1.2-llama-3-8b)
- [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct)_
Which were the two most interesting llama3 finetunes as of yet. Resulting model seems OK. It's not on Miqu's level, anyway.
Blah, blah, llama 3 license (no tag for it yet). Also not going to name my model Llama-3-Copus. Come at me, Zuck. | {"license": "llama2"} | blockblockblock/Copus-2x8B-bpw2.5 | null | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"conversational",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-20T05:12:42+00:00 | [] | [] | TAGS
#transformers #safetensors #mixtral #text-generation #conversational #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<img src=URL
MoE'd up:
- dreamgen/opus-v1.2-llama-3-8b
- NousResearch/Meta-Llama-3-8B-Instruct_
Which were the two most interesting llama3 finetunes as of yet. Resulting model seems OK. It's not on Miqu's level, anyway.
Blah, blah, llama 3 license (no tag for it yet). Also not going to name my model Llama-3-Copus. Come at me, Zuck. | [] | [
"TAGS\n#transformers #safetensors #mixtral #text-generation #conversational #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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