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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. -->
# CS505_COQE_viT5_train_Instruction0_SAPOL
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- 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": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_train_Instruction0_SAPOL", "results": []}]} | ThuyNT/CS505_COQE_viT5_train_Instruction0_SAPOL | null | [
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"region:us"
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|
# CS505_COQE_viT5_train_Instruction0_SAPOL
This model is a fine-tuned version of VietAI/vit5-large 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- 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
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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. -->
# CS505_COQE_viT5_train_Instruction0_OAPSL
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- 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": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_train_Instruction0_OAPSL", "results": []}]} | ThuyNT/CS505_COQE_viT5_train_Instruction0_OAPSL | null | [
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"region:us"
] | null | 2024-04-17T11:46:52+00:00 | [] | [] | TAGS
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|
# CS505_COQE_viT5_train_Instruction0_OAPSL
This model is a fine-tuned version of VietAI/vit5-large 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- 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
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"## Training and evaluation data\n\nMore information needed",
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"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
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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 ๐ค 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": []} | Sahil998/codegen-350M-mono-finetuned-python-18k-alpaca_70_percent | null | [
"transformers",
"safetensors",
"codegen",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T11:46:53+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #codegen #text-generation #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]
- 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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] |
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. -->
# results_bertcased
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5827
- Accuracy: 0.7320
- F1: 0.4364
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "bert-base-cased", "model-index": [{"name": "results_bertcased", "results": []}]} | dianamihalache27/results_bertcased | null | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T11:47:22+00:00 | [] | [] | TAGS
#transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# results_bertcased
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5827
- Accuracy: 0.7320
- F1: 0.4364
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| [
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"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
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] |
null | transformers |
# Uploaded model
- **Developed by:** SirDamisola
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.1-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-v0.1-bnb-4bit"} | SirDamisola/iw_lora_model-2 | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.1-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T11:47:47+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #text-generation-inference #unsloth #mistral #trl #en #base_model-unsloth/mistral-7b-instruct-v0.1-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: SirDamisola
- License: apache-2.0
- Finetuned from model : unsloth/mistral-7b-instruct-v0.1-bnb-4bit
This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
| [
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] | [
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"# Uploaded model\n\n- Developed by: SirDamisola\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-instruct-v0.1-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] |
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. -->
# CS505_COQE_viT5_train_Instruction0_AOPSL
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- 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": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_train_Instruction0_AOPSL", "results": []}]} | ThuyNT/CS505_COQE_viT5_train_Instruction0_AOPSL | null | [
"transformers",
"tensorboard",
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"t5",
"text2text-generation",
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"base_model:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-17T11:48:20+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-VietAI/vit5-large #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# CS505_COQE_viT5_train_Instruction0_AOPSL
This model is a fine-tuned version of VietAI/vit5-large 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- 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
| [
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"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
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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: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 32\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\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"
] |
image-classification | transformers |
# ZorigClassify
A model to classify the image into the thirteen arts and craft of Bhutan.
Autogenerated by HuggingPics
credit: @nateraw
## Example Images
#### shagzo woodturning
#### tshemzo tailoring-embroidery
#### dezo paper making
#### dozo masonry
#### garzo blacksmith
#### jinzo sculpting
#### lhazo painting
#### lugzo bronze casting
#### parzo carving
#### shingzo carpentry
#### thagzo weaving
#### troeko ornament
#### tsharzo bamboo-weaving
 | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | yesheytenzin/ZorigClassify | null | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"pytorch",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2024-04-17T11:49:16+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #vit #image-classification #pytorch #huggingpics #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# ZorigClassify
A model to classify the image into the thirteen arts and craft of Bhutan.
Autogenerated by HuggingPics
credit: @nateraw
## Example Images
#### shagzo woodturning
! shagzo woodturning
#### tshemzo tailoring-embroidery
! tshemzo tailoring-embroidery
#### dezo paper making
!dezo paper making
#### dozo masonry
!dozo masonry
#### garzo blacksmith
!garzo blacksmith
#### jinzo sculpting
!jinzo sculpting
#### lhazo painting
!lhazo painting
#### lugzo bronze casting
!lugzo bronze casting
#### parzo carving
!parzo carving
#### shingzo carpentry
!shingzo carpentry
#### thagzo weaving
!thagzo weaving
#### troeko ornament
!troeko ornament
#### tsharzo bamboo-weaving
!tsharzo bamboo-weaving | [
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"## Example Images",
"#### shagzo woodturning\n\n! shagzo woodturning",
"#### tshemzo tailoring-embroidery\n\n! tshemzo tailoring-embroidery",
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"#### dozo masonry\n\n!dozo masonry",
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"#### lhazo painting\n\n!lhazo painting",
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"#### parzo carving\n\n!parzo carving",
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"#### thagzo weaving\n\n!thagzo weaving",
"#### troeko ornament\n\n!troeko ornament",
"#### tsharzo bamboo-weaving\n\n!tsharzo bamboo-weaving"
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"#### shagzo woodturning\n\n! shagzo woodturning",
"#### tshemzo tailoring-embroidery\n\n! tshemzo tailoring-embroidery",
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"#### dozo masonry\n\n!dozo masonry",
"#### garzo blacksmith\n\n!garzo blacksmith",
"#### jinzo sculpting\n\n!jinzo sculpting",
"#### lhazo painting\n\n!lhazo painting",
"#### lugzo bronze casting\n\n!lugzo bronze casting",
"#### parzo carving\n\n!parzo carving",
"#### shingzo carpentry\n\n!shingzo carpentry",
"#### thagzo weaving\n\n!thagzo weaving",
"#### troeko ornament\n\n!troeko ornament",
"#### tsharzo bamboo-weaving\n\n!tsharzo bamboo-weaving"
] |
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. -->
# CS505_COQE_viT5_train_Instruction0_POASL
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- 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": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_train_Instruction0_POASL", "results": []}]} | ThuyNT/CS505_COQE_viT5_train_Instruction0_POASL | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-17T11:49:35+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-VietAI/vit5-large #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# CS505_COQE_viT5_train_Instruction0_POASL
This model is a fine-tuned version of VietAI/vit5-large 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- 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
| [
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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: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 32\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\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"
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"# CS505_COQE_viT5_train_Instruction0_POASL\n\nThis model is a fine-tuned version of VietAI/vit5-large 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: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 32\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\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 | peft | ## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
| {"library_name": "peft"} | harish2962/Fintuned_Disease_Symptom | null | [
"peft",
"safetensors",
"llama",
"region:us"
] | null | 2024-04-17T11:50:01+00:00 | [] | [] | TAGS
#peft #safetensors #llama #region-us
| ## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
| [
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16",
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"### Framework versions\n\n\n- PEFT 0.4.0"
] |
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. -->
# CS505-Dev-CSI-PhoBERT_large_h2
This model is a fine-tuned version of [vinai/phobert-large](https://huggingface.co/vinai/phobert-large) 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: 2e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"tags": ["generated_from_trainer"], "base_model": "vinai/phobert-large", "model-index": [{"name": "CS505-Dev-CSI-PhoBERT_large_h2", "results": []}]} | ThuyNT/CS505-Dev-CSI-PhoBERT_large_h2 | null | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:vinai/phobert-large",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T11:50:31+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-vinai/phobert-large #autotrain_compatible #endpoints_compatible #region-us
|
# CS505-Dev-CSI-PhoBERT_large_h2
This model is a fine-tuned version of vinai/phobert-large 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: 2e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| [
"# CS505-Dev-CSI-PhoBERT_large_h2\n\nThis model is a fine-tuned version of vinai/phobert-large on the None dataset.",
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"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
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"### Training results",
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"## 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: 64\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 15",
"### 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"
] |
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. -->
# concat_sentence_model_v3
This model is a fine-tuned version of [vinai/bartpho-syllable-base](https://huggingface.co/vinai/bartpho-syllable-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8808
- Bleu: 9.3237
- Gen Len: 18.117
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| 2.0193 | 1.0 | 7928 | 1.8808 | 9.3237 | 18.117 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"tags": ["generated_from_trainer"], "metrics": ["bleu"], "base_model": "vinai/bartpho-syllable-base", "model-index": [{"name": "concat_sentence_model_v3", "results": []}]} | long292/concat_sentence_model_v3 | null | [
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"safetensors",
"mbart",
"text2text-generation",
"generated_from_trainer",
"base_model:vinai/bartpho-syllable-base",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T11:53:33+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #mbart #text2text-generation #generated_from_trainer #base_model-vinai/bartpho-syllable-base #autotrain_compatible #endpoints_compatible #region-us
| concat\_sentence\_model\_v3
===========================
This model is a fine-tuned version of vinai/bartpho-syllable-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8808
* Bleu: 9.3237
* Gen Len: 18.117
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: 1
* eval\_batch\_size: 1
* seed: 42
* 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 2.1.2
* Datasets 2.18.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### 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: 1\n* eval\\_batch\\_size: 1\n* seed: 42\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\n* Transformers 4.39.3\n* Pytorch 2.1.2\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
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. -->
# results_bertcased2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4472
- Accuracy: 0.7233
- F1: 0.4037
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "bert-base-cased", "model-index": [{"name": "results_bertcased2", "results": []}]} | dianamihalache27/results_bertcased2 | null | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T11:53:40+00:00 | [] | [] | TAGS
#transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# results_bertcased2
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4472
- Accuracy: 0.7233
- F1: 0.4037
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| [
"# results_bertcased2\n\nThis model is a fine-tuned version of bert-base-cased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.4472\n- Accuracy: 0.7233\n- F1: 0.4037",
"## 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: 16\n- eval_batch_size: 16\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_steps: 100\n- num_epochs: 10",
"### Training results",
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"# results_bertcased2\n\nThis model is a fine-tuned version of bert-base-cased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.4472\n- Accuracy: 0.7233\n- F1: 0.4037",
"## 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: 16\n- eval_batch_size: 16\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_steps: 100\n- num_epochs: 10",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
sentence-similarity | sentence-transformers |
# Yunika/sentence-transformer-nepali
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('Yunika/sentence-transformer-nepali')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('Yunika/sentence-transformer-nepali')
model = AutoModel.from_pretrained('Yunika/sentence-transformer-nepali')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=Yunika/sentence-transformer-nepali)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 3375 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1687,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | {"library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | Yunika/sentence-transformer-nepali | null | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T11:53:59+00:00 | [] | [] | TAGS
#sentence-transformers #safetensors #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# Yunika/sentence-transformer-nepali
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Usage (HuggingFace Transformers)
Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL
## Training
The model was trained with the parameters:
DataLoader:
'URL.dataloader.DataLoader' of length 3375 with parameters:
Loss:
'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:
Parameters of the fit()-Method:
## Full Model Architecture
## Citing & Authors
| [
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"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
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"## Full Model Architecture",
"## Citing & Authors"
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"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
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"## Full Model Architecture",
"## Citing & Authors"
] |
text-to-image | null | ## Model
 | {"tags": ["stable-diffusion", "text-to-image", "StableDiffusionPipeline", "lora"]} | fearvel/aki-sd-v2 | null | [
"stable-diffusion",
"text-to-image",
"StableDiffusionPipeline",
"lora",
"region:us"
] | null | 2024-04-17T11:54:11+00:00 | [] | [] | TAGS
#stable-diffusion #text-to-image #StableDiffusionPipeline #lora #region-us
| ## Model
!pipeline | [
"## Model\n\n!pipeline"
] | [
"TAGS\n#stable-diffusion #text-to-image #StableDiffusionPipeline #lora #region-us \n",
"## Model\n\n!pipeline"
] |
image-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. -->
# swin-tiny-patch4-window7-224-Kontur-competition
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0088
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0216 | 1.0 | 269 | 0.0635 |
| 0.0286 | 2.0 | 538 | 0.0467 |
| 0.0252 | 3.0 | 807 | 0.0088 |
| 0.0003 | 4.0 | 1076 | 0.0339 |
| 0.0019 | 5.0 | 1345 | 0.0123 |
### 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": ["imagefolder"], "base_model": "microsoft/swin-tiny-patch4-window7-224", "model-index": [{"name": "swin-tiny-patch4-window7-224-Kontur-competition", "results": []}]} | t1msan/swin-tiny-patch4-window7-224-Kontur-competition | null | [
"transformers",
"tensorboard",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-tiny-patch4-window7-224",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T11:58:19+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #swin #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/swin-tiny-patch4-window7-224 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| swin-tiny-patch4-window7-224-Kontur-competition
===============================================
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0088
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: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 5
### 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: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\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: 5",
"### Training results",
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\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: 5",
"### 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 | adapter-transformers |
Girly-guide is a chatbot finetuned on top of LLama-2-7b with a custom dataset comprising of all women-related queries. | {"language": ["en"], "license": "apache-2.0", "library_name": "adapter-transformers", "metrics": ["accuracy"]} | rukaiyaaaah/girly-guide | null | [
"adapter-transformers",
"pytorch",
"llama",
"en",
"license:apache-2.0",
"region:us"
] | null | 2024-04-17T11:58:40+00:00 | [] | [
"en"
] | TAGS
#adapter-transformers #pytorch #llama #en #license-apache-2.0 #region-us
|
Girly-guide is a chatbot finetuned on top of LLama-2-7b with a custom dataset comprising of all women-related queries. | [] | [
"TAGS\n#adapter-transformers #pytorch #llama #en #license-apache-2.0 #region-us \n"
] |
null | transformers |
# zypcastles/Qwen1.5-48B-Chat-Q6_K-GGUF
This model was converted to GGUF format from [`zypcastles/Qwen1.5-48B-Chat`](https://huggingface.co/zypcastles/Qwen1.5-48B-Chat) 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/zypcastles/Qwen1.5-48B-Chat) 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 zypcastles/Qwen1.5-48B-Chat-Q6_K-GGUF --model qwen1.5-48b-chat.Q6_K.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo zypcastles/Qwen1.5-48B-Chat-Q6_K-GGUF --model qwen1.5-48b-chat.Q6_K.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 qwen1.5-48b-chat.Q6_K.gguf -n 128
```
| {"library_name": "transformers", "tags": ["mergekit", "merge", "llama-cpp", "gguf-my-repo"], "base_model": ["Qwen/Qwen1.5-32B-Chat"]} | zypcastles/Qwen1.5-48B-Chat-Q6_K-GGUF | null | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:Qwen/Qwen1.5-32B-Chat",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T11:58:47+00:00 | [] | [] | TAGS
#transformers #gguf #mergekit #merge #llama-cpp #gguf-my-repo #base_model-Qwen/Qwen1.5-32B-Chat #endpoints_compatible #region-us
|
# zypcastles/Qwen1.5-48B-Chat-Q6_K-GGUF
This model was converted to GGUF format from 'zypcastles/Qwen1.5-48B-Chat' 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.
| [
"# zypcastles/Qwen1.5-48B-Chat-Q6_K-GGUF\nThis model was converted to GGUF format from 'zypcastles/Qwen1.5-48B-Chat' 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 #mergekit #merge #llama-cpp #gguf-my-repo #base_model-Qwen/Qwen1.5-32B-Chat #endpoints_compatible #region-us \n",
"# zypcastles/Qwen1.5-48B-Chat-Q6_K-GGUF\nThis model was converted to GGUF format from 'zypcastles/Qwen1.5-48B-Chat' 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."
] |
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-medical-internal-noise-v0
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5649
- Wer: 0.3172
## 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
- num_epochs: 6.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.6667 | 1.14 | 150 | 0.6710 | 0.3654 |
| 0.5023 | 2.28 | 300 | 0.6016 | 0.3413 |
| 0.4384 | 3.43 | 450 | 0.5907 | 0.3325 |
| 0.3536 | 4.57 | 600 | 0.5693 | 0.3221 |
| 0.3158 | 5.71 | 750 | 0.5649 | 0.3172 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.15.2
| {"tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-medical-internal-noise-v0", "results": []}]} | mattlc/wav2vec2-medical-internal-noise-v0 | null | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T12:02:25+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us
| wav2vec2-medical-internal-noise-v0
==================================
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5649
* Wer: 0.3172
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
* num\_epochs: 6.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.38.2
* Pytorch 2.0.1+cu117
* Datasets 2.14.5
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\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* num\\_epochs: 6.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.14.5\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\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* num\\_epochs: 6.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.14.5\n* Tokenizers 0.15.2"
] |
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. -->
# flant-t5-function-calling
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Rouge1: 52.8136
- Rouge2: 46.102
- Rougel: 52.8115
- Rougelsum: 52.8115
- Gen Len: 19.0
## 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: 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 0.0003 | 1.0 | 4375 | 0.0000 | 52.8136 | 46.102 | 52.8115 | 52.8115 | 19.0 |
| 0.0001 | 2.0 | 8750 | 0.0000 | 52.8136 | 46.102 | 52.8115 | 52.8115 | 19.0 |
| 0.0001 | 3.0 | 13125 | 0.0000 | 52.8136 | 46.102 | 52.8115 | 52.8115 | 19.0 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "google/flan-t5-base", "model-index": [{"name": "flant-t5-function-calling", "results": []}]} | jrcastropy/flan-t5-base-query-extraction | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/flan-t5-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-17T12:04:45+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-google/flan-t5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| flant-t5-function-calling
=========================
This model is a fine-tuned version of google/flan-t5-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0000
* Rouge1: 52.8136
* Rouge2: 46.102
* Rougel: 52.8115
* Rougelsum: 52.8115
* Gen Len: 19.0
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: 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: 3
### Training results
### Framework versions
* Transformers 4.38.2
* Pytorch 2.2.1+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: 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* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-google/flan-t5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #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* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
image-to-image | null |
# Super-Resolution with Perturbed-Attention Guidance
[Project](https://ku-cvlab.github.io/Perturbed-Attention-Guidance/) / [arXiv](https://arxiv.org/abs/2403.17377) / [GitHub](https://github.com/KU-CVLAB/Perturbed-Attention-Guidance)
This repository is based on [Diffusers](https://huggingface.co/docs/diffusers/index).
The pipeline is a modification of StableDiffusionPipeline to support super-resolution with Perturbed-Attention Guidance (PAG). Please refer to "Image-to-upscaler-to-super-resolution" section of an [official document](https://huggingface.co/docs/diffusers/using-diffusers/img2img) for details.
## Quickstart
Loading Custom Piepline:
```
from diffusers import StableDiffusionPipeline
pipe = StableDiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-x4-upscaler",
custom_pipeline="hyoungwoncho/sd_perturbed_attention_guidance_sr",
torch_dtype=torch.float16,
safety_checker=None
)
device="cuda"
pipe = pipe.to(device)
```
Super-Resolution with PAG:
```
output = pipe(
prompts,
image=lr_image,
num_inference_steps=50,
guidance_scale=0.0,
pag_scale=2.0,
pag_applied_layers_index=['u2']
).images[0]
```
## Parameters
guidance_scale : gudiance scale of CFG (ex: 7.5)
pag_scale : gudiance scale of PAG (ex: 2.0)
pag_applied_layers_index : index of the layer to apply perturbation (ex: ['u2']) | {"language": ["en"], "tags": ["Diffusion Models", "Stable Diffusion", "Perturbed-Attention Guidance", "PAG"], "pipeline_tag": "image-to-image"} | hyoungwoncho/sd_perturbed_attention_guidance_sr | null | [
"Diffusion Models",
"Stable Diffusion",
"Perturbed-Attention Guidance",
"PAG",
"image-to-image",
"en",
"arxiv:2403.17377",
"region:us"
] | null | 2024-04-17T12:05:08+00:00 | [
"2403.17377"
] | [
"en"
] | TAGS
#Diffusion Models #Stable Diffusion #Perturbed-Attention Guidance #PAG #image-to-image #en #arxiv-2403.17377 #region-us
|
# Super-Resolution with Perturbed-Attention Guidance
Project / arXiv / GitHub
This repository is based on Diffusers.
The pipeline is a modification of StableDiffusionPipeline to support super-resolution with Perturbed-Attention Guidance (PAG). Please refer to "Image-to-upscaler-to-super-resolution" section of an official document for details.
## Quickstart
Loading Custom Piepline:
Super-Resolution with PAG:
## Parameters
guidance_scale : gudiance scale of CFG (ex: 7.5)
pag_scale : gudiance scale of PAG (ex: 2.0)
pag_applied_layers_index : index of the layer to apply perturbation (ex: ['u2']) | [
"# Super-Resolution with Perturbed-Attention Guidance\n\nProject / arXiv / GitHub\n\nThis repository is based on Diffusers.\n\nThe pipeline is a modification of StableDiffusionPipeline to support super-resolution with Perturbed-Attention Guidance (PAG). Please refer to \"Image-to-upscaler-to-super-resolution\" section of an official document for details.",
"## Quickstart\n\nLoading Custom Piepline:\n\n\n\nSuper-Resolution with PAG:",
"## Parameters\n\nguidance_scale : gudiance scale of CFG (ex: 7.5)\n\npag_scale : gudiance scale of PAG (ex: 2.0)\n\npag_applied_layers_index : index of the layer to apply perturbation (ex: ['u2'])"
] | [
"TAGS\n#Diffusion Models #Stable Diffusion #Perturbed-Attention Guidance #PAG #image-to-image #en #arxiv-2403.17377 #region-us \n",
"# Super-Resolution with Perturbed-Attention Guidance\n\nProject / arXiv / GitHub\n\nThis repository is based on Diffusers.\n\nThe pipeline is a modification of StableDiffusionPipeline to support super-resolution with Perturbed-Attention Guidance (PAG). Please refer to \"Image-to-upscaler-to-super-resolution\" section of an official document for details.",
"## Quickstart\n\nLoading Custom Piepline:\n\n\n\nSuper-Resolution with PAG:",
"## Parameters\n\nguidance_scale : gudiance scale of CFG (ex: 7.5)\n\npag_scale : gudiance scale of PAG (ex: 2.0)\n\npag_applied_layers_index : index of the layer to apply perturbation (ex: ['u2'])"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# MooBai/roberta-classical-chinese-base-char-finetuned-wikitext2
This model is a fine-tuned version of [KoichiYasuoka/roberta-classical-chinese-base-char](https://huggingface.co/KoichiYasuoka/roberta-classical-chinese-base-char) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.7456
- Validation Loss: 0.0584
- Epoch: 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.7456 | 0.0584 | 0 |
### Framework versions
- Transformers 4.38.2
- TensorFlow 2.15.0
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "KoichiYasuoka/roberta-classical-chinese-base-char", "model-index": [{"name": "MooBai/roberta-classical-chinese-base-char-finetuned-wikitext2", "results": []}]} | MooBai/roberta-classical-chinese-base-char-finetuned-wikitext2 | null | [
"transformers",
"tf",
"tensorboard",
"roberta",
"text-generation",
"generated_from_keras_callback",
"base_model:KoichiYasuoka/roberta-classical-chinese-base-char",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T12:05:35+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #roberta #text-generation #generated_from_keras_callback #base_model-KoichiYasuoka/roberta-classical-chinese-base-char #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| MooBai/roberta-classical-chinese-base-char-finetuned-wikitext2
==============================================================
This model is a fine-tuned version of KoichiYasuoka/roberta-classical-chinese-base-char on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.7456
* Validation Loss: 0.0584
* Epoch: 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:
* optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': 2e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\_decay\_rate': 0.01}
* training\_precision: float32
### Training results
### Framework versions
* Transformers 4.38.2
* TensorFlow 2.15.0
* Datasets 2.18.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* TensorFlow 2.15.0\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* TensorFlow 2.15.0\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
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. -->
# model_usp3_dpo1
This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2527
- Rewards/chosen: -10.7037
- Rewards/rejected: -13.2986
- Rewards/accuracies: 0.7000
- Rewards/margins: 2.5949
- Logps/rejected: -242.7872
- Logps/chosen: -215.0880
- Logits/rejected: -0.8066
- Logits/chosen: -0.8008
## 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: 4
- eval_batch_size: 1
- 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: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.1152 | 2.67 | 100 | 0.9230 | -6.0994 | -7.5730 | 0.6100 | 1.4737 | -185.5316 | -169.0449 | -0.9595 | -0.9289 |
| 0.0007 | 5.33 | 200 | 1.2625 | -10.4507 | -12.9431 | 0.7100 | 2.4924 | -239.2319 | -212.5582 | -0.8924 | -0.8878 |
| 0.0002 | 8.0 | 300 | 1.2065 | -10.0237 | -12.4963 | 0.7000 | 2.4725 | -234.7639 | -208.2885 | -0.8455 | -0.8351 |
| 0.0001 | 10.67 | 400 | 1.2314 | -10.3811 | -12.9055 | 0.7100 | 2.5245 | -238.8566 | -211.8620 | -0.8259 | -0.8181 |
| 0.0001 | 13.33 | 500 | 1.2449 | -10.5483 | -13.1112 | 0.7100 | 2.5629 | -240.9136 | -213.5344 | -0.8155 | -0.8090 |
| 0.0001 | 16.0 | 600 | 1.2475 | -10.6353 | -13.2168 | 0.7100 | 2.5815 | -241.9690 | -214.4042 | -0.8099 | -0.8041 |
| 0.0001 | 18.67 | 700 | 1.2504 | -10.6796 | -13.2671 | 0.7000 | 2.5875 | -242.4725 | -214.8474 | -0.8075 | -0.8022 |
| 0.0001 | 21.33 | 800 | 1.2562 | -10.7029 | -13.2944 | 0.7000 | 2.5915 | -242.7449 | -215.0800 | -0.8065 | -0.8014 |
| 0.0001 | 24.0 | 900 | 1.2542 | -10.7066 | -13.2945 | 0.7000 | 2.5879 | -242.7467 | -215.1174 | -0.8061 | -0.8009 |
| 0.0001 | 26.67 | 1000 | 1.2527 | -10.7037 | -13.2986 | 0.7000 | 2.5949 | -242.7872 | -215.0880 | -0.8066 | -0.8008 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 | {"license": "llama2", "library_name": "peft", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "meta-llama/Llama-2-7b-chat-hf", "model-index": [{"name": "model_usp3_dpo1", "results": []}]} | guoyu-zhang/model_usp3_dpo1 | null | [
"peft",
"safetensors",
"trl",
"dpo",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"license:llama2",
"region:us"
] | null | 2024-04-17T12:05:46+00:00 | [] | [] | TAGS
#peft #safetensors #trl #dpo #generated_from_trainer #base_model-meta-llama/Llama-2-7b-chat-hf #license-llama2 #region-us
| model\_usp3\_dpo1
=================
This model is a fine-tuned version of meta-llama/Llama-2-7b-chat-hf on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2527
* Rewards/chosen: -10.7037
* Rewards/rejected: -13.2986
* Rewards/accuracies: 0.7000
* Rewards/margins: 2.5949
* Logps/rejected: -242.7872
* Logps/chosen: -215.0880
* Logits/rejected: -0.8066
* Logits/chosen: -0.8008
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: 4
* eval\_batch\_size: 1
* 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: cosine
* lr\_scheduler\_warmup\_steps: 100
* training\_steps: 1000
### Training results
### Framework versions
* PEFT 0.10.0
* 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: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\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: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 1000",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.39.3\n* Pytorch 2.2.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#peft #safetensors #trl #dpo #generated_from_trainer #base_model-meta-llama/Llama-2-7b-chat-hf #license-llama2 #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\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: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 1000",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.39.3\n* Pytorch 2.2.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
text-generation | transformers | # merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [Citaman/command-r-11-layer](https://huggingface.co/Citaman/command-r-11-layer)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: Citaman/command-r-11-layer
layer_range: [0, 10]
- model: Citaman/command-r-11-layer
layer_range: [1, 11]
merge_method: slerp
base_model: Citaman/command-r-11-layer
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
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Citaman/command-r-11-layer"]} | Citaman/command-r-10-layer | null | [
"transformers",
"safetensors",
"cohere",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:Citaman/command-r-11-layer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-17T12:05:52+00:00 | [] | [] | TAGS
#transformers #safetensors #cohere #text-generation #mergekit #merge #conversational #base_model-Citaman/command-r-11-layer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # merge
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* Citaman/command-r-11-layer
### Configuration
The following YAML configuration was used to produce this model:
| [
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* Citaman/command-r-11-layer",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #safetensors #cohere #text-generation #mergekit #merge #conversational #base_model-Citaman/command-r-11-layer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* Citaman/command-r-11-layer",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] |
null | null | PyTorch torchvision models in TorchSharp format, generated with [vision-TorchSharp-generator](https://huggingface.co/spaces/yueyinqiu/vision-TorchSharp-generator). | {} | yueyinqiu/vision-TorchSharp | null | [
"has_space",
"region:us"
] | null | 2024-04-17T12:09:18+00:00 | [] | [] | TAGS
#has_space #region-us
| PyTorch torchvision models in TorchSharp format, generated with vision-TorchSharp-generator. | [] | [
"TAGS\n#has_space #region-us \n"
] |
text-generation | transformers | # merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [Citaman/command-r-10-layer](https://huggingface.co/Citaman/command-r-10-layer)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: Citaman/command-r-10-layer
layer_range: [0, 9]
- model: Citaman/command-r-10-layer
layer_range: [1, 10]
merge_method: slerp
base_model: Citaman/command-r-10-layer
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
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Citaman/command-r-10-layer"]} | Citaman/command-r-9-layer | null | [
"transformers",
"safetensors",
"cohere",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:Citaman/command-r-10-layer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-17T12:10:21+00:00 | [] | [] | TAGS
#transformers #safetensors #cohere #text-generation #mergekit #merge #conversational #base_model-Citaman/command-r-10-layer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # merge
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* Citaman/command-r-10-layer
### Configuration
The following YAML configuration was used to produce this model:
| [
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* Citaman/command-r-10-layer",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #safetensors #cohere #text-generation #mergekit #merge #conversational #base_model-Citaman/command-r-10-layer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* Citaman/command-r-10-layer",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] |
text-generation | transformers | ## Delexa-V0.1-Instruct-7b: Our Newest and Best Model Yet!
We are excited to announce the release of Delexa-V0.1-Instruct-7b, our newest and best model yet! Delexa-V0.1-Instruct-7b has shown excellent performance on a variety of tasks, and we are confident that it will be a valuable asset to the research community.
### Eval Results
Delexa-V0.1-Instruct-7b was evaluated on a dataset of question-answer pairs. The model was given a single question and three different answer choices, and it was tasked with selecting the best answer. Delexa-V0.1-Instruct-7b achieved an average score of 8.27 on this task.
Here is a table showing the detailed eval results:
| Model | Turn 1 | Turn 2 | Average |
|---|---|---|---|
| gpt-4 | 8.95625 | 9.0250 | 8.990625 |
| Delexa-V0.1-Instruct-7b | 8.57500 | 7.9500 | 8.268750 |
| claude-v1 | 8.15000 | 7.6500 | 7.900000 |
| gpt-3.5-turbo | 8.07500 | 7.8125 | 7.943750 |
| vicuna-13b-v1.3 | 6.81250 | 5.9625 | 6.387500 |
| palm-2-chat-bison-001 | 6.71250 | 6.0875 | 6.400000 |
### Technique
One of the key factors that contributed to Delexa-V0.1-Instruct-7b's success is the technique of training the model with one question and three different answers. This technique allows the model to take into account different perspectives and viewpoints, which leads to more robust and accurate results.
### Future Work
We are excited to continue working on Delexa and to see how it can be further improved. We are currently working on an Instruct model, which is a type of model that can be fine-tuned on specific tasks. We believe that Instruct models have the potential to be even more powerful than Delexa-V0.1-7b, and we are eager to see the results of our ongoing research.
We would like to thank the entire team for their hard work on Delexa-V0.1-Instruct-7b. We are confident that this model will be a valuable asset to the research community.
### Guardrails:
This Model allows 18+ content and lewd content, but it wont let any illegal content through (unless you jailbreak it).
### Support Our Work and Join our Community!
[Our Patreon](https://patreon.com/Lex_Hue?utm_medium=unknown&utm_source=join_link&utm_campaign=creatorshare_creator&utm_content=copyLink)
[Our Twitter](https://twitter.com/lex_hue)
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_lex-hue__Delexa-Instruct-V0.1-7b)
| Metric |Value|
|---------------------------------|----:|
|Avg. |69.85|
|AI2 Reasoning Challenge (25-Shot)|66.38|
|HellaSwag (10-Shot) |85.90|
|MMLU (5-Shot) |63.79|
|TruthfulQA (0-shot) |61.73|
|Winogrande (5-shot) |78.37|
|GSM8k (5-shot) |62.93|
| {"license": "apache-2.0", "model-index": [{"name": "Delexa-Instruct-V0.1-7b", "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": 66.38, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lex-hue/Delexa-Instruct-V0.1-7b", "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": 85.9, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lex-hue/Delexa-Instruct-V0.1-7b", "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": 63.79, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lex-hue/Delexa-Instruct-V0.1-7b", "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": 61.73}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lex-hue/Delexa-Instruct-V0.1-7b", "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": 78.37, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lex-hue/Delexa-Instruct-V0.1-7b", "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": 62.93, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lex-hue/Delexa-Instruct-V0.1-7b", "name": "Open LLM Leaderboard"}}]}]} | lex-hue/Delexa-Instruct-V0.1-7b | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"doi:10.57967/hf/2152",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-17T12:10:29+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #conversational #doi-10.57967/hf/2152 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Delexa-V0.1-Instruct-7b: Our Newest and Best Model Yet!
-------------------------------------------------------
We are excited to announce the release of Delexa-V0.1-Instruct-7b, our newest and best model yet! Delexa-V0.1-Instruct-7b has shown excellent performance on a variety of tasks, and we are confident that it will be a valuable asset to the research community.
### Eval Results
Delexa-V0.1-Instruct-7b was evaluated on a dataset of question-answer pairs. The model was given a single question and three different answer choices, and it was tasked with selecting the best answer. Delexa-V0.1-Instruct-7b achieved an average score of 8.27 on this task.
Here is a table showing the detailed eval results:
### Technique
One of the key factors that contributed to Delexa-V0.1-Instruct-7b's success is the technique of training the model with one question and three different answers. This technique allows the model to take into account different perspectives and viewpoints, which leads to more robust and accurate results.
### Future Work
We are excited to continue working on Delexa and to see how it can be further improved. We are currently working on an Instruct model, which is a type of model that can be fine-tuned on specific tasks. We believe that Instruct models have the potential to be even more powerful than Delexa-V0.1-7b, and we are eager to see the results of our ongoing research.
We would like to thank the entire team for their hard work on Delexa-V0.1-Instruct-7b. We are confident that this model will be a valuable asset to the research community.
### Guardrails:
This Model allows 18+ content and lewd content, but it wont let any illegal content through (unless you jailbreak it).
### Support Our Work and Join our Community!
Our Patreon
Our Twitter
Open LLM Leaderboard Evaluation Results
=======================================
Detailed results can be found here
| [
"### Eval Results\n\n\nDelexa-V0.1-Instruct-7b was evaluated on a dataset of question-answer pairs. The model was given a single question and three different answer choices, and it was tasked with selecting the best answer. Delexa-V0.1-Instruct-7b achieved an average score of 8.27 on this task.\n\n\nHere is a table showing the detailed eval results:",
"### Technique\n\n\nOne of the key factors that contributed to Delexa-V0.1-Instruct-7b's success is the technique of training the model with one question and three different answers. This technique allows the model to take into account different perspectives and viewpoints, which leads to more robust and accurate results.",
"### Future Work\n\n\nWe are excited to continue working on Delexa and to see how it can be further improved. We are currently working on an Instruct model, which is a type of model that can be fine-tuned on specific tasks. We believe that Instruct models have the potential to be even more powerful than Delexa-V0.1-7b, and we are eager to see the results of our ongoing research.\n\n\nWe would like to thank the entire team for their hard work on Delexa-V0.1-Instruct-7b. We are confident that this model will be a valuable asset to the research community.",
"### Guardrails:\n\n\nThis Model allows 18+ content and lewd content, but it wont let any illegal content through (unless you jailbreak it).",
"### Support Our Work and Join our Community!\n\n\nOur Patreon\n\n\nOur Twitter\n\n\nOpen LLM Leaderboard Evaluation Results\n=======================================\n\n\nDetailed results can be found here"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #conversational #doi-10.57967/hf/2152 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Eval Results\n\n\nDelexa-V0.1-Instruct-7b was evaluated on a dataset of question-answer pairs. The model was given a single question and three different answer choices, and it was tasked with selecting the best answer. Delexa-V0.1-Instruct-7b achieved an average score of 8.27 on this task.\n\n\nHere is a table showing the detailed eval results:",
"### Technique\n\n\nOne of the key factors that contributed to Delexa-V0.1-Instruct-7b's success is the technique of training the model with one question and three different answers. This technique allows the model to take into account different perspectives and viewpoints, which leads to more robust and accurate results.",
"### Future Work\n\n\nWe are excited to continue working on Delexa and to see how it can be further improved. We are currently working on an Instruct model, which is a type of model that can be fine-tuned on specific tasks. We believe that Instruct models have the potential to be even more powerful than Delexa-V0.1-7b, and we are eager to see the results of our ongoing research.\n\n\nWe would like to thank the entire team for their hard work on Delexa-V0.1-Instruct-7b. We are confident that this model will be a valuable asset to the research community.",
"### Guardrails:\n\n\nThis Model allows 18+ content and lewd content, but it wont let any illegal content through (unless you jailbreak it).",
"### Support Our Work and Join our Community!\n\n\nOur Patreon\n\n\nOur Twitter\n\n\nOpen LLM Leaderboard Evaluation Results\n=======================================\n\n\nDetailed results can be found here"
] |
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
| {"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": "300.41 +/- 14.82", "name": "mean_reward"}]}]}]} | qgallouedec/utkusaglm-ppo-LunarLander-v0 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2024-04-17T12:15:41+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\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\n This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.\n \n ## Usage (with Stable-baselines3)\n TODO: Add your code"
] |
text-classification | transformers |
Label to predict:
0 : Negative
1: Neutral
2: Positive
Fine-tuning PhoBERT for Vietnamese Student Feedback Analysis
In the realm of Natural Language Processing (NLP), the Vietnamese language poses its own set of challenges and intricacies.
Fine-tuning language models tailored to Vietnamese, such as PhoBERT, has emerged as a pivotal endeavor in advancing NLP applications within the Vietnamese-speaking community. Here, we introduce a model fine-tuned on Vietnamese Student Feedback data, an essential domain in educational assessment and improvement efforts.
Model Overview
PhoBERT: PhoBERT, short for "Pre-trained Pho Vietnamese BERT," is a transformer-based language model specifically pre-trained for the Vietnamese language. Leveraging the BERT architecture, PhoBERT captures contextual information and semantic nuances within Vietnamese text, enabling it to understand and generate Vietnamese content effectively.
Specifically, I used sup-SimCSE-VietNamese-phobert-base (https://huggingface.co/VoVanPhuc/sup-SimCSE-VietNamese-phobert-base) to train the model and I have a good result
Training Details (https://huggingface.co/datasets/uitnlp/vietnamese_students_feedback)
Dataset: The model is fine-tuned on a dataset consisting of Vietnamese student feedback, a corpus rich in linguistic diversity and educational insights.
This dataset provides valuable feedback on various aspects of educational experiences, including teaching quality, course content, and overall satisfaction.
The Dataset splits 3 main parts: Train(11.4k rows), valid (1.56k rows), test (3.17k rows)
The Dataset includes 3 features: Sentences, Sentiment and Topic. I used 2 column names: Sentence and Sentiment to classify sentimnet.
Training Duration: The fine-tuning process spans 15 epochs, with each epoch iterating over the entire dataset. Despite the considerable depth of training, the model demonstrates efficiency, achieving promising results within a reasonable timeframe.
Hyperparameters:
Learning Rate: Set to 2e-5, the learning rate governs the step size in the optimization process during fine-tuning. A carefully chosen learning rate facilitates effective weight updates while preventing overshooting or stagnation.
Batch Size: With a batch size of 64, the model processes 64 data samples in each training iteration. This batch size strikes a balance between computational efficiency and model stability, facilitating smooth convergence during training.
Performance
Loss: The fine-tuned model exhibits an impressive loss metric, averaging around 0.002 throughout the training process. This minimal loss signifies the model's ability to accurately predict student feedback sentiments and insights with high precision.
Impact and Applications
Student Feedback Analysis: By accurately analyzing student feedback, educational institutions can identify areas of improvement, enhance teaching methodologies, and foster a more conducive learning environment.
Educational Assessment: The model aids in automating the assessment of educational quality and effectiveness, providing educators and administrators with actionable insights to optimize educational practices.
Natural Language Understanding: With its nuanced understanding of the Vietnamese language, the model serves as a cornerstone for developing advanced NLP applications catering to Vietnamese speakers, including chatbots, summarization tools, and sentiment analysis systems.
In summary, the fine-tuned PhoBERT model represents a significant stride in leveraging advanced NLP techniques for educational enhancement and linguistic analysis within the Vietnamese-speaking community. With its robust performance and versatility, this model promises to revolutionize the landscape of educational assessment and linguistic research in Vietnam and beyond. | {} | Luan220703/Classification_for_StudentFeedback | null | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T12:16:53+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
|
Label to predict:
0 : Negative
1: Neutral
2: Positive
Fine-tuning PhoBERT for Vietnamese Student Feedback Analysis
In the realm of Natural Language Processing (NLP), the Vietnamese language poses its own set of challenges and intricacies.
Fine-tuning language models tailored to Vietnamese, such as PhoBERT, has emerged as a pivotal endeavor in advancing NLP applications within the Vietnamese-speaking community. Here, we introduce a model fine-tuned on Vietnamese Student Feedback data, an essential domain in educational assessment and improvement efforts.
Model Overview
PhoBERT: PhoBERT, short for "Pre-trained Pho Vietnamese BERT," is a transformer-based language model specifically pre-trained for the Vietnamese language. Leveraging the BERT architecture, PhoBERT captures contextual information and semantic nuances within Vietnamese text, enabling it to understand and generate Vietnamese content effectively.
Specifically, I used sup-SimCSE-VietNamese-phobert-base (URL to train the model and I have a good result
Training Details (URL
Dataset: The model is fine-tuned on a dataset consisting of Vietnamese student feedback, a corpus rich in linguistic diversity and educational insights.
This dataset provides valuable feedback on various aspects of educational experiences, including teaching quality, course content, and overall satisfaction.
The Dataset splits 3 main parts: Train(11.4k rows), valid (1.56k rows), test (3.17k rows)
The Dataset includes 3 features: Sentences, Sentiment and Topic. I used 2 column names: Sentence and Sentiment to classify sentimnet.
Training Duration: The fine-tuning process spans 15 epochs, with each epoch iterating over the entire dataset. Despite the considerable depth of training, the model demonstrates efficiency, achieving promising results within a reasonable timeframe.
Hyperparameters:
Learning Rate: Set to 2e-5, the learning rate governs the step size in the optimization process during fine-tuning. A carefully chosen learning rate facilitates effective weight updates while preventing overshooting or stagnation.
Batch Size: With a batch size of 64, the model processes 64 data samples in each training iteration. This batch size strikes a balance between computational efficiency and model stability, facilitating smooth convergence during training.
Performance
Loss: The fine-tuned model exhibits an impressive loss metric, averaging around 0.002 throughout the training process. This minimal loss signifies the model's ability to accurately predict student feedback sentiments and insights with high precision.
Impact and Applications
!image/png
Student Feedback Analysis: By accurately analyzing student feedback, educational institutions can identify areas of improvement, enhance teaching methodologies, and foster a more conducive learning environment.
Educational Assessment: The model aids in automating the assessment of educational quality and effectiveness, providing educators and administrators with actionable insights to optimize educational practices.
Natural Language Understanding: With its nuanced understanding of the Vietnamese language, the model serves as a cornerstone for developing advanced NLP applications catering to Vietnamese speakers, including chatbots, summarization tools, and sentiment analysis systems.
In summary, the fine-tuned PhoBERT model represents a significant stride in leveraging advanced NLP techniques for educational enhancement and linguistic analysis within the Vietnamese-speaking community. With its robust performance and versatility, this model promises to revolutionize the landscape of educational assessment and linguistic research in Vietnam and beyond. | [] | [
"TAGS\n#transformers #tensorboard #safetensors #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null | ะกัะฐััะต ะผะพะดะตะปะธ ะธะท ัะตัะธะธ AiDT, ัะพะทะดะฐะฝะฝัะต ะดะปั [so-vits-svc](https://github.com/voicepaw/so-vits-svc-fork)
Zemen-SVC
[<img src="https://huggingface.co/qnezor/aidt-svc/resolve/main/files/zemen.png">](https://huggingface.co/qnezor/aidt-svc/resolve/main/zemen-svc.zip)
***
Nexzy-SVC
[<img src="https://huggingface.co/qnezor/aidt-svc/resolve/main/files/nexzy.png">](https://huggingface.co/qnezor/aidt-svc/resolve/main/nexzy-svc.zip)
***
Winner-SVC
[<img src="https://huggingface.co/qnezor/aidt-svc/resolve/main/files/winner.png">](https://huggingface.co/qnezor/aidt-svc/resolve/main/winner-svc.zip)
| {"language": ["ru"]} | qnezor/aidt-svc | null | [
"ru",
"region:us"
] | null | 2024-04-17T12:17:30+00:00 | [] | [
"ru"
] | TAGS
#ru #region-us
| ะกัะฐััะต ะผะพะดะตะปะธ ะธะท ัะตัะธะธ AiDT, ัะพะทะดะฐะฝะฝัะต ะดะปั so-vits-svc
Zemen-SVC
<img src="URL
*
Nexzy-SVC
<img src="URL
*
Winner-SVC
<img src="URL
| [] | [
"TAGS\n#ru #region-us \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. -->
# HSE_PRAVO_complexity_classifier_googlebert
This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) 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: 5e-06
- train_batch_size: 5
- eval_batch_size: 5
- seed: 42
- gradient_accumulation_steps: 10
- total_train_batch_size: 50
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 13
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "google-bert/bert-base-multilingual-cased", "model-index": [{"name": "HSE_PRAVO_complexity_classifier_googlebert", "results": []}]} | marcus2000/HSE_PRAVO_complexity_classifier_googlebert | null | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-multilingual-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T12:18:58+00:00 | [] | [] | TAGS
#transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-google-bert/bert-base-multilingual-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# HSE_PRAVO_complexity_classifier_googlebert
This model is a fine-tuned version of google-bert/bert-base-multilingual-cased 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: 5e-06
- train_batch_size: 5
- eval_batch_size: 5
- seed: 42
- gradient_accumulation_steps: 10
- total_train_batch_size: 50
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 13
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
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"## Intended uses & limitations\n\nMore information needed",
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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. -->
# CS505_COQE_viT5_train_Instruction0_OPSAL
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- 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": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_train_Instruction0_OPSAL", "results": []}]} | ThuyNT/CS505_COQE_viT5_train_Instruction0_OPSAL | null | [
"transformers",
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"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-17T12:20:08+00:00 | [] | [] | TAGS
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|
# CS505_COQE_viT5_train_Instruction0_OPSAL
This model is a fine-tuned version of VietAI/vit5-large 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- 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
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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. -->
# CS505_COQE_viT5_train_Instruction0_SPOAL
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- 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": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_train_Instruction0_SPOAL", "results": []}]} | ThuyNT/CS505_COQE_viT5_train_Instruction0_SPOAL | null | [
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"safetensors",
"t5",
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"base_model:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-17T12:20:21+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-VietAI/vit5-large #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# CS505_COQE_viT5_train_Instruction0_SPOAL
This model is a fine-tuned version of VietAI/vit5-large 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- 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
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"## Training and evaluation data\n\nMore information needed",
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"## 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: 8\n- eval_batch_size: 32\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP",
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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. -->
# CS505-Dev-CSI-PhoBERT_base-v2_h2
This model is a fine-tuned version of [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2) 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: 2e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"tags": ["generated_from_trainer"], "base_model": "vinai/phobert-base-v2", "model-index": [{"name": "CS505-Dev-CSI-PhoBERT_base-v2_h2", "results": []}]} | ThuyNT/CS505-Dev-CSI-PhoBERT_base-v2_h2 | null | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:vinai/phobert-base-v2",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T12:21:12+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-vinai/phobert-base-v2 #autotrain_compatible #endpoints_compatible #region-us
|
# CS505-Dev-CSI-PhoBERT_base-v2_h2
This model is a fine-tuned version of vinai/phobert-base-v2 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: 2e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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"## Training and evaluation data\n\nMore information needed",
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"# CS505-Dev-CSI-PhoBERT_base-v2_h2\n\nThis model is a fine-tuned version of vinai/phobert-base-v2 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: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 15",
"### 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"
] |
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. -->
# CS505_COQE_viT5_train_Instruction0_APSOL
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- 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": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_train_Instruction0_APSOL", "results": []}]} | ThuyNT/CS505_COQE_viT5_train_Instruction0_APSOL | null | [
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"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-17T12:22:31+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-VietAI/vit5-large #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# CS505_COQE_viT5_train_Instruction0_APSOL
This model is a fine-tuned version of VietAI/vit5-large 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- 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
| [
"# CS505_COQE_viT5_train_Instruction0_APSOL\n\nThis model is a fine-tuned version of VietAI/vit5-large 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: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 32\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\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 #t5 #text2text-generation #generated_from_trainer #base_model-VietAI/vit5-large #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# CS505_COQE_viT5_train_Instruction0_APSOL\n\nThis model is a fine-tuned version of VietAI/vit5-large 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: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 32\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP",
"### Training results",
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] |
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]
- **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]
### Framework versions
- PEFT 0.10.0 | {"library_name": "peft", "base_model": "liuhaotian/llava-v1.6-mistral-7b"} | rbojja/llava-v1.6-mistral-7b-med-lora | null | [
"peft",
"safetensors",
"llava_mistral",
"arxiv:1910.09700",
"base_model:liuhaotian/llava-v1.6-mistral-7b",
"region:us"
] | null | 2024-04-17T12:23:17+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #llava_mistral #arxiv-1910.09700 #base_model-liuhaotian/llava-v1.6-mistral-7b #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- 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
### Framework versions
- PEFT 0.10.0 | [
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"## Training Details",
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] |
sentence-similarity | sentence-transformers |
# vkimbris/wb-charcs-mapper
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('vkimbris/wb-charcs-mapper')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=vkimbris/wb-charcs-mapper)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 16 with parameters:
```
{'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | {"library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | vkimbris/wb-charcs-mapper | null | [
"sentence-transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T12:24:05+00:00 | [] | [] | TAGS
#sentence-transformers #safetensors #xlm-roberta #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# vkimbris/wb-charcs-mapper
This is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL
## Training
The model was trained with the parameters:
DataLoader:
'URL.dataloader.DataLoader' of length 16 with parameters:
Loss:
'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss'
Parameters of the fit()-Method:
## Full Model Architecture
## Citing & Authors
| [
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"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 16 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' \n\nParameters of the fit()-Method:",
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"## Full Model Architecture",
"## Citing & Authors"
] |
null | peft | ## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- _load_in_8bit: False
- _load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
- bnb_4bit_quant_storage: uint8
- load_in_4bit: True
- load_in_8bit: False
### Framework versions
- PEFT 0.4.0
| {"library_name": "peft"} | GenAIBK/Llama-2-7b-chat-finetune | null | [
"peft",
"safetensors",
"llama",
"region:us"
] | null | 2024-04-17T12:24:26+00:00 | [] | [] | TAGS
#peft #safetensors #llama #region-us
| ## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- quant_method: bitsandbytes
- _load_in_8bit: False
- _load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
- bnb_4bit_quant_storage: uint8
- load_in_4bit: True
- load_in_8bit: False
### Framework versions
- PEFT 0.4.0
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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. -->
# CS505_COQE_viT5_train_Instruction0_PASOL
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- 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": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_train_Instruction0_PASOL", "results": []}]} | ThuyNT/CS505_COQE_viT5_train_Instruction0_PASOL | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-17T12:24:35+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-VietAI/vit5-large #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# CS505_COQE_viT5_train_Instruction0_PASOL
This model is a fine-tuned version of VietAI/vit5-large 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- 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
| [
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"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
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"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
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"### Training results",
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] |
text-generation | transformers | # merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [Citaman/command-r-9-layer](https://huggingface.co/Citaman/command-r-9-layer)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: Citaman/command-r-9-layer
layer_range: [0, 8]
- model: Citaman/command-r-9-layer
layer_range: [1, 9]
merge_method: slerp
base_model: Citaman/command-r-9-layer
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
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Citaman/command-r-9-layer"]} | Citaman/command-r-8-layer | null | [
"transformers",
"safetensors",
"cohere",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:Citaman/command-r-9-layer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-17T12:24:40+00:00 | [] | [] | TAGS
#transformers #safetensors #cohere #text-generation #mergekit #merge #conversational #base_model-Citaman/command-r-9-layer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # merge
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* Citaman/command-r-9-layer
### Configuration
The following YAML configuration was used to produce this model:
| [
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* Citaman/command-r-9-layer",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #safetensors #cohere #text-generation #mergekit #merge #conversational #base_model-Citaman/command-r-9-layer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* Citaman/command-r-9-layer",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] |
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. -->
# hello
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) 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: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| No log | 1.0 | 136 | 1.2177 | 0.0 | 0.0 | 0.0 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "t5-small", "model-index": [{"name": "hello", "results": []}]} | Ajas2002/hello | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:t5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-17T12:27:35+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-t5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| hello
=====
This model is a fine-tuned version of t5-small 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: 6
* eval\_batch\_size: 6
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
### Training results
### Framework versions
* Transformers 4.38.2
* Pytorch 2.2.1+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: 6\n* eval\\_batch\\_size: 6\n* seed: 42\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\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-t5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #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: 6\n* eval\\_batch\\_size: 6\n* seed: 42\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\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
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. -->
# Yi-6B-ruozhiba2
This model is a fine-tuned version of [01-ai/Yi-6B](https://huggingface.co/01-ai/Yi-6B) on the ruozhiba dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8109
## 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: 1
- eval_batch_size: 1
- seed: 42
- 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
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8506 | 1.0 | 22 | 1.8109 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.2.2+cu118
- Datasets 2.14.6
- Tokenizers 0.15.2 | {"license": "other", "library_name": "peft", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "sft", "generated_from_trainer"], "datasets": ["ruozhiba"], "base_model": "01-ai/Yi-6B", "model-index": [{"name": "Yi-6B-ruozhiba2", "results": []}]} | yyx123/Yi-6B-ruozhiba2 | null | [
"peft",
"safetensors",
"llama",
"alignment-handbook",
"generated_from_trainer",
"trl",
"sft",
"dataset:ruozhiba",
"base_model:01-ai/Yi-6B",
"license:other",
"4-bit",
"region:us"
] | null | 2024-04-17T12:29:56+00:00 | [] | [] | TAGS
#peft #safetensors #llama #alignment-handbook #generated_from_trainer #trl #sft #dataset-ruozhiba #base_model-01-ai/Yi-6B #license-other #4-bit #region-us
| Yi-6B-ruozhiba2
===============
This model is a fine-tuned version of 01-ai/Yi-6B on the ruozhiba dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8109
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: 1
* eval\_batch\_size: 1
* seed: 42
* 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
* PEFT 0.7.1
* Transformers 4.36.2
* Pytorch 2.2.2+cu118
* Datasets 2.14.6
* 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: 1\n* eval\\_batch\\_size: 1\n* seed: 42\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\n* PEFT 0.7.1\n* Transformers 4.36.2\n* Pytorch 2.2.2+cu118\n* Datasets 2.14.6\n* Tokenizers 0.15.2"
] | [
"TAGS\n#peft #safetensors #llama #alignment-handbook #generated_from_trainer #trl #sft #dataset-ruozhiba #base_model-01-ai/Yi-6B #license-other #4-bit #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: 1\n* eval\\_batch\\_size: 1\n* seed: 42\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\n* PEFT 0.7.1\n* Transformers 4.36.2\n* Pytorch 2.2.2+cu118\n* Datasets 2.14.6\n* Tokenizers 0.15.2"
] |
text-generation | transformers |
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://github.com/LlamaEdge/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# CodeQwen1.5-7B-Chat-GGUF
## Original Model
[Qwen/CodeQwen1.5-7B-Chat](https://huggingface.co/Qwen/CodeQwen1.5-7B-Chat)
## Run with LlamaEdge
- LlamaEdge version: [v0.8.2](https://github.com/LlamaEdge/LlamaEdge/releases/tag/0.8.2) and above
- Prompt template
- Prompt type: `chatml`
- Prompt string
```text
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
- Context size: `4096`
- Run as LlamaEdge service
```bash
wasmedge --dir .:. --nn-preload default:GGML:AUTO:CodeQwen1.5-7B-Chat-Q5_K_M.gguf \
llama-api-server.wasm \
--prompt-template chatml
--context-size 4096
--model-name CodeQwen1.5-7B-Chat
```
<!--
## Quantized GGUF Models
| Name | Quant method | Bits | Size | Use case |
| ---- | ---- | ---- | ---- | ----- |
| [Qwen1.5-7B-Chat-Q2_K.gguf](https://huggingface.co/second-state/Qwen1.5-7B-Chat-GGUF/blob/main/Qwen1.5-7B-Chat-Q2_K.gguf) | Q2_K | 2 | 3.10 GB| smallest, significant quality loss - not recommended for most purposes |
| [Qwen1.5-7B-Chat-Q3_K_L.gguf](https://huggingface.co/second-state/Qwen1.5-7B-Chat-GGUF/blob/main/Qwen1.5-7B-Chat-Q3_K_L.gguf) | Q3_K_L | 3 | 4.22 GB| small, substantial quality loss |
| [Qwen1.5-7B-Chat-Q3_K_M.gguf](https://huggingface.co/second-state/Qwen1.5-7B-Chat-GGUF/blob/main/Qwen1.5-7B-Chat-Q3_K_M.gguf) | Q3_K_M | 3 | 3.92 GB| very small, high quality loss |
| [Qwen1.5-7B-Chat-Q3_K_S.gguf](https://huggingface.co/second-state/Qwen1.5-7B-Chat-GGUF/blob/main/Qwen1.5-7B-Chat-Q3_K_S.gguf) | Q3_K_S | 3 | 3.57 GB| very small, high quality loss |
| [Qwen1.5-7B-Chat-Q4_0.gguf](https://huggingface.co/second-state/Qwen1.5-7B-Chat-GGUF/blob/main/Qwen1.5-7B-Chat-Q4_0.gguf) | Q4_0 | 4 | 4.51 GB| legacy; small, very high quality loss - prefer using Q3_K_M |
| [Qwen1.5-7B-Chat-Q4_K_M.gguf](https://huggingface.co/second-state/Qwen1.5-7B-Chat-GGUF/blob/main/Qwen1.5-7B-Chat-Q4_K_M.gguf) | Q4_K_M | 4 | 4.77 GB| medium, balanced quality - recommended |
| [Qwen1.5-7B-Chat-Q4_K_S.gguf](https://huggingface.co/second-state/Qwen1.5-7B-Chat-GGUF/blob/main/Qwen1.5-7B-Chat-Q4_K_S.gguf) | Q4_K_S | 4 | 4.54 GB| small, greater quality loss |
| [Qwen1.5-7B-Chat-Q5_0.gguf](https://huggingface.co/second-state/Qwen1.5-7B-Chat-GGUF/blob/main/Qwen1.5-7B-Chat-Q5_0.gguf) | Q5_0 | 5 | 5.40 GB| legacy; medium, balanced quality - prefer using Q4_K_M |
| [Qwen1.5-7B-Chat-Q5_K_M.gguf](https://huggingface.co/second-state/Qwen1.5-7B-Chat-GGUF/blob/main/Qwen1.5-7B-Chat-Q5_K_M.gguf) | Q5_K_M | 5 | 5.53 GB| large, very low quality loss - recommended |
| [Qwen1.5-7B-Chat-Q5_K_S.gguf](https://huggingface.co/second-state/Qwen1.5-7B-Chat-GGUF/blob/main/Qwen1.5-7B-Chat-Q5_K_S.gguf) | Q5_K_S | 5 | 5.4 GB| large, low quality loss - recommended |
| [Qwen1.5-7B-Chat-Q6_K.gguf](https://huggingface.co/second-state/Qwen1.5-7B-Chat-GGUF/blob/main/Qwen1.5-7B-Chat-Q6_K.gguf) | Q6_K | 6 | 6.34 GB| very large, extremely low quality loss |
| [Qwen1.5-7B-Chat-Q8_0.gguf](https://huggingface.co/second-state/Qwen1.5-7B-Chat-GGUF/blob/main/Qwen1.5-7B-Chat-Q8_0.gguf) | Q8_0 | 8 | 8.21 GB| very large, extremely low quality loss - not recommended |
*Quantized with llama.cpp b2636* -->
| {"language": ["en"], "license": "other", "library_name": "transformers", "tags": ["chat"], "model_name": "Openchat 3.5 0106", "base_model": "Qwen/CodeQwen1.5-7B-Chat", "inference": false, "license_name": "tongyi-qianwen", "model_creator": "Qwen", "model_type": "mistral", "pipeline_tag": "text-generation", "quantized_by": "Second State Inc."} | second-state/CodeQwen1.5-7B-Chat-GGUF | null | [
"transformers",
"gguf",
"qwen2",
"text-generation",
"chat",
"en",
"base_model:Qwen/CodeQwen1.5-7B-Chat",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-17T12:30:56+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #qwen2 #text-generation #chat #en #base_model-Qwen/CodeQwen1.5-7B-Chat #license-other #autotrain_compatible #text-generation-inference #region-us
|
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="URL style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
# CodeQwen1.5-7B-Chat-GGUF
## Original Model
Qwen/CodeQwen1.5-7B-Chat
## Run with LlamaEdge
- LlamaEdge version: v0.8.2 and above
- Prompt template
- Prompt type: 'chatml'
- Prompt string
- Context size: '4096'
- Run as LlamaEdge service
| [
"# CodeQwen1.5-7B-Chat-GGUF",
"## Original Model\n\nQwen/CodeQwen1.5-7B-Chat",
"## Run with LlamaEdge\n\n- LlamaEdge version: v0.8.2 and above\n\n- Prompt template\n\n - Prompt type: 'chatml'\n\n - Prompt string\n\n \n\n- Context size: '4096'\n\n- Run as LlamaEdge service"
] | [
"TAGS\n#transformers #gguf #qwen2 #text-generation #chat #en #base_model-Qwen/CodeQwen1.5-7B-Chat #license-other #autotrain_compatible #text-generation-inference #region-us \n",
"# CodeQwen1.5-7B-Chat-GGUF",
"## Original Model\n\nQwen/CodeQwen1.5-7B-Chat",
"## Run with LlamaEdge\n\n- LlamaEdge version: v0.8.2 and above\n\n- Prompt template\n\n - Prompt type: 'chatml'\n\n - Prompt string\n\n \n\n- Context size: '4096'\n\n- Run as LlamaEdge service"
] |
text-generation | transformers |
# T3Q-LLM-sft1.0-dpo1.0
## This model is a version of T3Q-LLM/T3Q-LLM-solar10.8-sft-v1.0 that has been fine-tuned with DPO.
## Model Developers Chihoon Lee(chihoonlee10), T3Q
## Prompt Template
```
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
Human: {prompt}
Assistant:
```
## How to Use it
```python
from transformers import AutoTokenizer
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("T3Q-LLM/T3Q-LLM-sft1.0-dpo1.0")
tokenizer = AutoTokenizer.from_pretrained("T3Q-LLM/T3Q-LLM-sft1.0-dpo1.0")
prompt_template = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\nHuman: {prompt}\nAssistant:\n"
text = 'ํ๊ตญ์ ์๋๋ ์ด๋์ธ๊ฐ์? ์๋ ์ ํ์ง ์ค ๊ณจ๋ผ์ฃผ์ธ์.\n\n(A) ๊ฒฝ์ฑ\n(B) ๋ถ์ฐ\n(C) ํ์\n(D) ์์ธ\n(E) ์ ์ฃผ'
model_inputs = tokenizer(prompt_template.format(prompt=text), return_tensors='pt')
outputs = model.generate(**model_inputs, max_new_tokens=256)
output_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
print(output_text)
```
### Example Output
```
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
Human: ํ๊ตญ์ ์๋๋ ์ด๋์ธ๊ฐ์? ์๋ ์ ํ์ง ์ค ๊ณจ๋ผ์ฃผ์ธ์.
(A) ๊ฒฝ์ฑ
(B) ๋ถ์ฐ
(C) ํ์
(D) ์์ธ
(E) ์ ์ฃผ
Assistant:
(D) ์์ธ์ด ํ๊ตญ์ ์๋์
๋๋ค. ์์ธ์ ๋๋ผ์ ๋ถ๋๋ถ์ ์์นํด ์์ผ๋ฉฐ, ์ ์น, ๊ฒฝ์ , ๋ฌธํ์ ์ค์ฌ์ง์
๋๋ค. ์ฝ 1,000๋ง ๋ช
์ด ๋๋ ์ธ๊ตฌ๋ฅผ ๊ฐ์ง ์ธ๊ณ์์ ๊ฐ์ฅ ํฐ ๋์ ์ค ํ๋์
๋๋ค. ์์ธ์ ๋์ ๋น๋ฉ, ํ๋์ ์ธ ์ธํ๋ผ, ํ๊ธฐ ๋ฌธํ ์ฅ๋ฉด์ผ๋ก ์ ๋ช
ํฉ๋๋ค. ๋ํ, ๋ง์ ์ญ์ฌ์ ๋ช
์์ ๋ฐ๋ฌผ๊ด์ด ์์ด ๋ฐฉ๋ฌธ๊ฐ๋ค์๊ฒ ํ๋ถํ ๋ฌธํ ์ฒดํ์ ์ ๊ณตํฉ๋๋ค.
```
| Task |Version| Metric |Value | |Stderr|
|----------------|------:|--------|-----:|---|-----:|
|kobest_boolq | 0|acc |0.9387|ยฑ |0.0064|
| | |macro_f1|0.9387|ยฑ |0.0064|
|kobest_copa | 0|acc |0.7590|ยฑ |0.0135|
| | |macro_f1|0.7585|ยฑ |0.0135|
|kobest_hellaswag| 0|acc |0.5080|ยฑ |0.0224|
| | |acc_norm|0.5580|ยฑ |0.0222|
| | |macro_f1|0.5049|ยฑ |0.0224|
|kobest_sentineg | 0|acc |0.8489|ยฑ |0.0180|
| | |macro_f1|0.8483|ยฑ |0.0180| | {"license": "apache-2.0", "library_name": "transformers", "datasets": ["maywell/ko_Ultrafeedback_binarized"], "pipeline_tag": "text-generation", "base model": ["yanolja/EEVE-Korean-Instruct-10.8B-v1.0"]} | T3Q-LLM/T3Q-LLM-sft1.0-dpo1.0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"dataset:maywell/ko_Ultrafeedback_binarized",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-17T12:31:29+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #dataset-maywell/ko_Ultrafeedback_binarized #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| !image/png
T3Q-LLM-sft1.0-dpo1.0
=====================
This model is a version of T3Q-LLM/T3Q-LLM-solar10.8-sft-v1.0 that has been fine-tuned with DPO.
------------------------------------------------------------------------------------------------
Model Developers Chihoon Lee(chihoonlee10), T3Q
-----------------------------------------------
Prompt Template
---------------
How to Use it
-------------
### Example Output
| [
"### Example Output"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #dataset-maywell/ko_Ultrafeedback_binarized #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Example Output"
] |
text-generation | transformers |
# Tess-2.0-Mixtral-8x22B
Tess, short for Tesoro (Treasure in Italian), is a general purpose Large Language Model series. Tess-2.0-Mixtral-8x22B was trained on the mistral-community/Mixtral-8x22B-v0.1 base.
# Prompt Format
```
SYSTEM: <ANY SYSTEM CONTEXT>
USER:
ASSISTANT:
```
# Training Methodology
Tess-2.0-Mixtral-8x22B was trained on the Tess-2.0 dataset. Tess-2.0 dataset and the training methodology follows LIMA (Less-Is-More) principles, and contains ~25K high-quality code and general training samples. The dataset is highly uncensored, hence the model will almost always follow instructions.
The model was only fine-tuned for 1-epoch to try and preserve its entropy as much as possible.
# Sample code to run inference
```python
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "migtissera/Tess-2.0-Mixtral-8x22B"
output_file_path = "./conversations.jsonl"
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.float16,
device_map="auto",
load_in_8bit=False,
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
def generate_text(instruction):
tokens = tokenizer.encode(instruction)
tokens = torch.LongTensor(tokens).unsqueeze(0)
tokens = tokens.to("cuda")
instance = {
"input_ids": tokens,
"top_p": 1.0,
"temperature": 0.5,
"generate_len": 1024,
"top_k": 50,
}
length = len(tokens[0])
with torch.no_grad():
rest = model.generate(
input_ids=tokens,
max_length=length + instance["generate_len"],
use_cache=True,
do_sample=True,
top_p=instance["top_p"],
temperature=instance["temperature"],
top_k=instance["top_k"],
num_return_sequences=1,
)
output = rest[0][length:]
string = tokenizer.decode(output, skip_special_tokens=True)
answer = string.split("USER:")[0].strip()
return f"{answer}"
conversation = f"SYSTEM: Answer the question thoughtfully and intelligently. Always answer without hesitation."
while True:
user_input = input("You: ")
llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: "
answer = generate_text(llm_prompt)
print(answer)
conversation = f"{llm_prompt}{answer}"
json_data = {"prompt": user_input, "answer": answer}
## Save your conversation
with open(output_file_path, "a") as output_file:
output_file.write(json.dumps(json_data) + "\n")
```
# Join My General AI Discord (NeuroLattice):
https://discord.gg/Hz6GrwGFKD
# Limitations & Biases:
While this model aims for accuracy, it can occasionally produce inaccurate or misleading results.
Despite diligent efforts in refining the pretraining data, there remains a possibility for the generation of inappropriate, biased, or offensive content.
Exercise caution and cross-check information when necessary. This is an uncensored model.
| {"license": "apache-2.0"} | blockblockblock/Tess-2.0-Mixtral-8x22B-bpw3 | null | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"3-bit",
"region:us"
] | null | 2024-04-17T12:32:00+00:00 | [] | [] | TAGS
#transformers #safetensors #mixtral #text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #3-bit #region-us
|
!Tesoro
# Tess-2.0-Mixtral-8x22B
Tess, short for Tesoro (Treasure in Italian), is a general purpose Large Language Model series. Tess-2.0-Mixtral-8x22B was trained on the mistral-community/Mixtral-8x22B-v0.1 base.
# Prompt Format
# Training Methodology
Tess-2.0-Mixtral-8x22B was trained on the Tess-2.0 dataset. Tess-2.0 dataset and the training methodology follows LIMA (Less-Is-More) principles, and contains ~25K high-quality code and general training samples. The dataset is highly uncensored, hence the model will almost always follow instructions.
The model was only fine-tuned for 1-epoch to try and preserve its entropy as much as possible.
# Sample code to run inference
# Join My General AI Discord (NeuroLattice):
URL
# Limitations & Biases:
While this model aims for accuracy, it can occasionally produce inaccurate or misleading results.
Despite diligent efforts in refining the pretraining data, there remains a possibility for the generation of inappropriate, biased, or offensive content.
Exercise caution and cross-check information when necessary. This is an uncensored model.
| [
"# Tess-2.0-Mixtral-8x22B\nTess, short for Tesoro (Treasure in Italian), is a general purpose Large Language Model series. Tess-2.0-Mixtral-8x22B was trained on the mistral-community/Mixtral-8x22B-v0.1 base.",
"# Prompt Format",
"# Training Methodology\nTess-2.0-Mixtral-8x22B was trained on the Tess-2.0 dataset. Tess-2.0 dataset and the training methodology follows LIMA (Less-Is-More) principles, and contains ~25K high-quality code and general training samples. The dataset is highly uncensored, hence the model will almost always follow instructions.\n\nThe model was only fine-tuned for 1-epoch to try and preserve its entropy as much as possible.",
"# Sample code to run inference",
"# Join My General AI Discord (NeuroLattice):\nURL",
"# Limitations & Biases:\n\nWhile this model aims for accuracy, it can occasionally produce inaccurate or misleading results. \n\nDespite diligent efforts in refining the pretraining data, there remains a possibility for the generation of inappropriate, biased, or offensive content. \n\nExercise caution and cross-check information when necessary. This is an uncensored model."
] | [
"TAGS\n#transformers #safetensors #mixtral #text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #3-bit #region-us \n",
"# Tess-2.0-Mixtral-8x22B\nTess, short for Tesoro (Treasure in Italian), is a general purpose Large Language Model series. Tess-2.0-Mixtral-8x22B was trained on the mistral-community/Mixtral-8x22B-v0.1 base.",
"# Prompt Format",
"# Training Methodology\nTess-2.0-Mixtral-8x22B was trained on the Tess-2.0 dataset. Tess-2.0 dataset and the training methodology follows LIMA (Less-Is-More) principles, and contains ~25K high-quality code and general training samples. The dataset is highly uncensored, hence the model will almost always follow instructions.\n\nThe model was only fine-tuned for 1-epoch to try and preserve its entropy as much as possible.",
"# Sample code to run inference",
"# Join My General AI Discord (NeuroLattice):\nURL",
"# Limitations & Biases:\n\nWhile this model aims for accuracy, it can occasionally produce inaccurate or misleading results. \n\nDespite diligent efforts in refining the pretraining data, there remains a possibility for the generation of inappropriate, biased, or offensive content. \n\nExercise caution and cross-check information when necessary. This is an uncensored model."
] |
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. -->
# Arabic-QA-Mistral-7B-Instruct
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7929
## 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: 4e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.2857 | 0.99 | 94 | 0.8537 |
| 0.8178 | 1.99 | 188 | 0.7929 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "mistralai/Mistral-7B-Instruct-v0.2", "model-index": [{"name": "Arabic-QA-Mistral-7B-Instruct", "results": []}]} | AlyGreo/Arabic-QA-Mistral-7B-Instruct | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"license:apache-2.0",
"region:us"
] | null | 2024-04-17T12:32:01+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-mistralai/Mistral-7B-Instruct-v0.2 #license-apache-2.0 #region-us
| Arabic-QA-Mistral-7B-Instruct
=============================
This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7929
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: 4e-05
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: constant
* lr\_scheduler\_warmup\_ratio: 0.03
* num\_epochs: 2
### Training results
### Framework versions
* PEFT 0.10.0
* 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: 4e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\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: 2",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.39.3\n* Pytorch 2.1.2\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-mistralai/Mistral-7B-Instruct-v0.2 #license-apache-2.0 #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\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: 2",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.39.3\n* Pytorch 2.1.2\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
null | null |
# NikolayKozloff/DolphinLake-7B-Q8_0-GGUF
This model was converted to GGUF format from [`Noodlz/DolphinLake-7B`](https://huggingface.co/Noodlz/DolphinLake-7B) 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/Noodlz/DolphinLake-7B) 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 NikolayKozloff/DolphinLake-7B-Q8_0-GGUF --model dolphinlake-7b.Q8_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo NikolayKozloff/DolphinLake-7B-Q8_0-GGUF --model dolphinlake-7b.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 dolphinlake-7b.Q8_0.gguf -n 128
```
| {"license": "apache-2.0", "tags": ["llama-cpp", "gguf-my-repo"]} | NikolayKozloff/DolphinLake-7B-Q8_0-GGUF | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"license:apache-2.0",
"region:us"
] | null | 2024-04-17T12:33:42+00:00 | [] | [] | TAGS
#gguf #llama-cpp #gguf-my-repo #license-apache-2.0 #region-us
|
# NikolayKozloff/DolphinLake-7B-Q8_0-GGUF
This model was converted to GGUF format from 'Noodlz/DolphinLake-7B' 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.
| [
"# NikolayKozloff/DolphinLake-7B-Q8_0-GGUF\nThis model was converted to GGUF format from 'Noodlz/DolphinLake-7B' 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 #license-apache-2.0 #region-us \n",
"# NikolayKozloff/DolphinLake-7B-Q8_0-GGUF\nThis model was converted to GGUF format from 'Noodlz/DolphinLake-7B' 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."
] |
reinforcement-learning | transformers |
# TRL Model
This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="baek26//tmp/tmpmbki0et5/baek26/dialogsum_4088_bart-dialogsum")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("baek26//tmp/tmpmbki0et5/baek26/dialogsum_4088_bart-dialogsum")
model = AutoModelForCausalLMWithValueHead.from_pretrained("baek26//tmp/tmpmbki0et5/baek26/dialogsum_4088_bart-dialogsum")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
| {"license": "apache-2.0", "tags": ["trl", "ppo", "transformers", "reinforcement-learning"]} | baek26/dialogsum_4088_bart-dialogsum | null | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"trl",
"ppo",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T12:37:40+00:00 | [] | [] | TAGS
#transformers #safetensors #bart #text2text-generation #trl #ppo #reinforcement-learning #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# TRL Model
This is a TRL language model that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
You can then generate text as follows:
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
| [
"# TRL Model\n\nThis is a TRL language model that has been fine-tuned with reinforcement learning to\n guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.",
"## Usage\n\nTo use this model for inference, first install the TRL library:\n\n\n\nYou can then generate text as follows:\n\n\n\nIf you want to use the model for training or to obtain the outputs from the value head, load the model as follows:"
] | [
"TAGS\n#transformers #safetensors #bart #text2text-generation #trl #ppo #reinforcement-learning #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# TRL Model\n\nThis is a TRL language model that has been fine-tuned with reinforcement learning to\n guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.",
"## Usage\n\nTo use this model for inference, first install the TRL library:\n\n\n\nYou can then generate text as follows:\n\n\n\nIf you want to use the model for training or to obtain the outputs from the value head, load the model as follows:"
] |
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 ๐ค 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": []} | cilantro9246/0ae47eu | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-17T12:38:39+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]:",
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"## 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]",
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"### Compute Infrastructure",
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"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
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"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
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"## 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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"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
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"#### 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",
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"#### 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 ๐ค 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. -->
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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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[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": []} | OwOOwO/dumbo-krillin42 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-17T12:39:12+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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"### Downstream Use [optional]",
"### Out-of-Scope Use",
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"### Training Data",
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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"
] |
text-generation | transformers | # merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [Citaman/command-r-8-layer](https://huggingface.co/Citaman/command-r-8-layer)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: Citaman/command-r-8-layer
layer_range: [0, 7]
- model: Citaman/command-r-8-layer
layer_range: [1, 8]
merge_method: slerp
base_model: Citaman/command-r-8-layer
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
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Citaman/command-r-8-layer"]} | Citaman/command-r-7-layer | null | [
"transformers",
"safetensors",
"cohere",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:Citaman/command-r-8-layer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-17T12:39:47+00:00 | [] | [] | TAGS
#transformers #safetensors #cohere #text-generation #mergekit #merge #conversational #base_model-Citaman/command-r-8-layer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # merge
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* Citaman/command-r-8-layer
### Configuration
The following YAML configuration was used to produce this model:
| [
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"### Configuration\n\nThe following YAML configuration was used to produce this model:"
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"### Models Merged\n\nThe following models were included in the merge:\n* Citaman/command-r-8-layer",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] |
text-to-speech | en-tts |
Code is hosted on GitHub: [stefantaubert/en-tts](https://github.com/stefantaubert/en-tts) | {"language": ["en"], "license": "mit", "library_name": "en-tts", "tags": ["speech synthesis", "text-to-speech", "speech generation"]} | stefantaubert/en-tts | null | [
"en-tts",
"speech synthesis",
"text-to-speech",
"speech generation",
"en",
"license:mit",
"has_space",
"region:us"
] | null | 2024-04-17T12:41:01+00:00 | [] | [
"en"
] | TAGS
#en-tts #speech synthesis #text-to-speech #speech generation #en #license-mit #has_space #region-us
|
Code is hosted on GitHub: stefantaubert/en-tts | [] | [
"TAGS\n#en-tts #speech synthesis #text-to-speech #speech generation #en #license-mit #has_space #region-us \n"
] |
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 ๐ค 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]
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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. -->
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | Grayx/sad_pepe_32 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T12:42:54+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #stablelm #text-generation #conversational #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]
- 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]:",
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"### Model Architecture and Objective",
"### Compute Infrastructure",
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"#### 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 #stablelm #text-generation #conversational #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 | 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. -->
# phi-2-finetuned-intentv5.0
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) 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: 0.0002
- train_batch_size: 6
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2 | {"license": "mit", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "microsoft/phi-2", "model-index": [{"name": "phi-2-finetuned-intentv5.0", "results": []}]} | mohits01/phi-2-finetuned-intentv5.0 | null | [
"peft",
"tensorboard",
"safetensors",
"phi",
"generated_from_trainer",
"custom_code",
"base_model:microsoft/phi-2",
"license:mit",
"region:us"
] | null | 2024-04-17T12:44:01+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #phi #generated_from_trainer #custom_code #base_model-microsoft/phi-2 #license-mit #region-us
|
# phi-2-finetuned-intentv5.0
This model is a fine-tuned version of microsoft/phi-2 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: 0.0002
- train_batch_size: 6
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2 | [
"# phi-2-finetuned-intentv5.0\n\nThis model is a fine-tuned version of microsoft/phi-2 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: 0.0002\n- train_batch_size: 6\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 24\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: 50\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- PEFT 0.10.0\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] | [
"TAGS\n#peft #tensorboard #safetensors #phi #generated_from_trainer #custom_code #base_model-microsoft/phi-2 #license-mit #region-us \n",
"# phi-2-finetuned-intentv5.0\n\nThis model is a fine-tuned version of microsoft/phi-2 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: 0.0002\n- train_batch_size: 6\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 24\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: 50\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- PEFT 0.10.0\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
image-to-text | transformers |
# LLaVA-JP Model Card
## Model detail
**Model type:**
LLaVA-JP is a vision-language model that can converse about input images.<br>
This model is an LVLM model trained using [google/siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384) as the image encoder and [llm-jp/llm-jp-1.3b-v1.0](https://huggingface.co/llm-jp/llm-jp-1.3b-v1.0) as the text decoder. supports the input of 768 x 768 high resolution images by scaling_on_scales method.
**Training:**
This model was initially trained with the Vision Projector using LLaVA-Pretrain-JA.<br>
In the second phase, it was fine-tuned with LLaVA-v1.5-Instruct-620K-JA.
resources for more information: https://github.com/tosiyuki/LLaVA-JP/tree/main
**Comparing VLMs**
|Model|JA-VG-VQA-500<br>(ROUGE-L)|JA-VLM-Bench-In-the-Wild<br>(ROUGE-L)|Heron-Bench(Detail)|Heron-Bench(Conv)|Heron-Bench(Complex)|Heron-Bench(Average)
|-|-|-|-|-|-|-|
|[Japanese Stable VLM](https://huggingface.co/stabilityai/japanese-stable-vlm)|-|40.50|25.15|51.23|37.84|38.07|
|[EvoVLM-JP-v1-7B](https://huggingface.co/SakanaAI/EvoVLM-JP-v1-7B)|**19.70**|**51.25**|50.31|44.42|40.47|45.07|
|[Heron BLIP Japanese StableLM Base 7B llava-620k](https://huggingface.co/turing-motors/heron-chat-blip-ja-stablelm-base-7b-v1-llava-620k)|14.51|33.26|49.09|41.51|45.72|45.44|
|[Heron GIT Japanese StableLM Base 7B](https://huggingface.co/turing-motors/heron-chat-git-ja-stablelm-base-7b-v1)|15.18|37.82|42.77|**54.20**|43.53|46.83|
|[llava-jp-1.3b-v1.0-620k](https://huggingface.co/toshi456/llava-jp-1.3b-v1.0-620k)|12.69|44.58|**51.21**|41.05|45.95|44.84|
|[llava-jp-1.3b-v1.1](https://huggingface.co/toshi456/llava-jp-1.3b-v1.1)|13.33|44.40|50.00|51.83|**48.98**|**50.39**|
## How to use the model
**1. Download dependencies**
```
git clone https://github.com/tosiyuki/LLaVA-JP.git
```
**2. Inference**
```python
import requests
import torch
import transformers
from PIL import Image
from transformers.generation.streamers import TextStreamer
from llava.constants import DEFAULT_IMAGE_TOKEN, IMAGE_TOKEN_INDEX
from llava.conversation import conv_templates, SeparatorStyle
from llava.model.llava_gpt2 import LlavaGpt2ForCausalLM
from llava.train.arguments_dataclass import ModelArguments, DataArguments, TrainingArguments
from llava.train.dataset import tokenizer_image_token
if __name__ == "__main__":
model_path = 'toshi456/llava-jp-1.3b-v1.1'
device = "cuda" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.bfloat16 if device=="cuda" else torch.float32
model = LlavaGpt2ForCausalLM.from_pretrained(
model_path,
low_cpu_mem_usage=True,
use_safetensors=True,
torch_dtype=torch_dtype,
device_map=device,
)
tokenizer = transformers.AutoTokenizer.from_pretrained(
model_path,
model_max_length=1532,
padding_side="right",
use_fast=False,
)
model.eval()
conv_mode = "v1"
conv = conv_templates[conv_mode].copy()
# image pre-process
image_url = "https://huggingface.co/rinna/bilingual-gpt-neox-4b-minigpt4/resolve/main/sample.jpg"
image = Image.open(requests.get(image_url, stream=True).raw).convert('RGB')
image_size = model.get_model().vision_tower.image_processor.size["height"]
if model.get_model().vision_tower.scales is not None:
image_size = model.get_model().vision_tower.image_processor.size["height"] * len(model.get_model().vision_tower.scales)
if device == "cuda":
image_tensor = model.get_model().vision_tower.image_processor(
image,
return_tensors='pt',
size={"height": image_size, "width": image_size}
)['pixel_values'].half().cuda().to(torch_dtype)
else:
image_tensor = model.get_model().vision_tower.image_processor(
image,
return_tensors='pt',
size={"height": image_size, "width": image_size}
)['pixel_values'].to(torch_dtype)
# create prompt
# ใฆใผใถใผ: <image>\n{prompt}
prompt = "็ซใฎ้ฃใซใฏไฝใใใใพใใ๏ผ"
inp = DEFAULT_IMAGE_TOKEN + '\n' + prompt
conv.append_message(conv.roles[0], inp)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
input_ids = tokenizer_image_token(
prompt,
tokenizer,
IMAGE_TOKEN_INDEX,
return_tensors='pt'
).unsqueeze(0)
if device == "cuda":
input_ids = input_ids.to(device)
input_ids = input_ids[:, :-1] # </sep>ใinputใฎๆๅพใซๅ
ฅใใฎใงๅ้คใใ
stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
keywords = [stop_str]
streamer = TextStreamer(tokenizer, skip_prompt=True, timeout=20.0)
# predict
with torch.inference_mode():
model.generate(
inputs=input_ids,
images=image_tensor,
do_sample=True,
temperature=0.1,
top_p=1.0,
max_new_tokens=256,
streamer=streamer,
use_cache=True,
)
"""็ซใฎ้ฃใซใฏใใผใใใฝใณใณใใใใพใใ"""
```
## Training dataset
**Stage1 Pretrain**
- [LLaVA-Pretrain-JA](https://huggingface.co/datasets/turing-motors/LLaVA-Pretrain-JA)
**Stage2 Fine-tuning**
- [LLaVA-v1.5-Instruct-620K-JA](https://huggingface.co/datasets/turing-motors/LLaVA-v1.5-Instruct-620K-JA)
## Acknowledgement
- [LLaVA](https://llava-vl.github.io/)
- [LLM-jp](https://llm-jp.nii.ac.jp/)
- [scaling_on_scales](https://github.com/bfshi/scaling_on_scales/tree/master)
## License
cc-by-nc-4.0 | {"language": ["ja"], "license": "cc-by-nc-4.0", "tags": ["vision", "image-captioning", "VQA"], "datasets": ["turing-motors/LLaVA-Pretrain-JA", "turing-motors/LLaVA-v1.5-Instruct-620K-JA"], "pipeline_tag": "image-to-text"} | toshi456/llava-jp-1.3b-v1.1 | null | [
"transformers",
"safetensors",
"llava-jp",
"text-generation",
"vision",
"image-captioning",
"VQA",
"image-to-text",
"ja",
"dataset:turing-motors/LLaVA-Pretrain-JA",
"dataset:turing-motors/LLaVA-v1.5-Instruct-620K-JA",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2024-04-17T12:44:24+00:00 | [] | [
"ja"
] | TAGS
#transformers #safetensors #llava-jp #text-generation #vision #image-captioning #VQA #image-to-text #ja #dataset-turing-motors/LLaVA-Pretrain-JA #dataset-turing-motors/LLaVA-v1.5-Instruct-620K-JA #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| LLaVA-JP Model Card
===================
Model detail
------------
Model type:
LLaVA-JP is a vision-language model that can converse about input images.
This model is an LVLM model trained using google/siglip-so400m-patch14-384 as the image encoder and llm-jp/llm-jp-1.3b-v1.0 as the text decoder. supports the input of 768 x 768 high resolution images by scaling\_on\_scales method.
Training:
This model was initially trained with the Vision Projector using LLaVA-Pretrain-JA.
In the second phase, it was fine-tuned with LLaVA-v1.5-Instruct-620K-JA.
resources for more information: URL
Comparing VLMs
!image/png
How to use the model
--------------------
1. Download dependencies
2. Inference
Training dataset
----------------
Stage1 Pretrain
* LLaVA-Pretrain-JA
Stage2 Fine-tuning
* LLaVA-v1.5-Instruct-620K-JA
Acknowledgement
---------------
* LLaVA
* LLM-jp
* scaling\_on\_scales
License
-------
cc-by-nc-4.0
| [] | [
"TAGS\n#transformers #safetensors #llava-jp #text-generation #vision #image-captioning #VQA #image-to-text #ja #dataset-turing-motors/LLaVA-Pretrain-JA #dataset-turing-motors/LLaVA-v1.5-Instruct-620K-JA #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
sentence-similarity | sentence-transformers |
# mteb-pt/average_pt_nilc_fasttext_skip_s300
This is an adaptation of pre-trained Portuguese fastText Word Embeddings to a [sentence-transformers](https://www.SBERT.net) model.
The original pre-trained word embeddings can be found at: [http://nilc.icmc.usp.br/nilc/index.php/repositorio-de-word-embeddings-do-nilc](http://nilc.icmc.usp.br/nilc/index.php/repositorio-de-word-embeddings-do-nilc).
This model maps sentences & paragraphs to a 300 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('mteb-pt/average_pt_nilc_fasttext_skip_s300')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Portuguese MTEB Leaderboard*: [mteb-pt/leaderboard](https://huggingface.co/spaces/mteb-pt/leaderboard)
## Full Model Architecture
```
SentenceTransformer(
(0): WordEmbeddings(
(emb_layer): Embedding(929606, 300)
)
(1): Pooling({'word_embedding_dimension': 300, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Citing & Authors
```bibtex
@inproceedings{hartmann2017portuguese,
title = Portuguese Word Embeddings: Evaluating on Word Analogies and Natural Language Tasks},
author = {Hartmann, Nathan S and
Fonseca, Erick R and
Shulby, Christopher D and
Treviso, Marcos V and
Rodrigues, J{'{e}}ssica S and
Alu{'{\i}}sio, Sandra Maria},
year = {2017},
publisher = {SBC},
booktitle = {Brazilian Symposium in Information and Human Language Technology - STIL},
url = {https://sol.sbc.org.br/index.php/stil/article/view/4008}
}
``` | {"language": ["pt"], "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | pt-mteb/average_pt_nilc_fasttext_skip_s300 | null | [
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"pt",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T12:45:09+00:00 | [] | [
"pt"
] | TAGS
#sentence-transformers #feature-extraction #sentence-similarity #pt #endpoints_compatible #region-us
|
# mteb-pt/average_pt_nilc_fasttext_skip_s300
This is an adaptation of pre-trained Portuguese fastText Word Embeddings to a sentence-transformers model.
The original pre-trained word embeddings can be found at: URL
This model maps sentences & paragraphs to a 300 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Evaluation Results
For an automated evaluation of this model, see the *Portuguese MTEB Leaderboard*: mteb-pt/leaderboard
## Full Model Architecture
## Citing & Authors
| [
"# mteb-pt/average_pt_nilc_fasttext_skip_s300\n\nThis is an adaptation of pre-trained Portuguese fastText Word Embeddings to a sentence-transformers model. \n\nThe original pre-trained word embeddings can be found at: URL \n\nThis model maps sentences & paragraphs to a 300 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Evaluation Results\n\nFor an automated evaluation of this model, see the *Portuguese MTEB Leaderboard*: mteb-pt/leaderboard",
"## Full Model Architecture",
"## Citing & Authors"
] | [
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"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Evaluation Results\n\nFor an automated evaluation of this model, see the *Portuguese MTEB Leaderboard*: mteb-pt/leaderboard",
"## Full Model Architecture",
"## Citing & Authors"
] |
text-generation | transformers | # merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [Citaman/command-r-7-layer](https://huggingface.co/Citaman/command-r-7-layer)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: Citaman/command-r-7-layer
layer_range: [0, 6]
- model: Citaman/command-r-7-layer
layer_range: [1, 7]
merge_method: slerp
base_model: Citaman/command-r-7-layer
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
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Citaman/command-r-7-layer"]} | Citaman/command-r-6-layer | null | [
"transformers",
"safetensors",
"cohere",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:Citaman/command-r-7-layer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-17T12:45:24+00:00 | [] | [] | TAGS
#transformers #safetensors #cohere #text-generation #mergekit #merge #conversational #base_model-Citaman/command-r-7-layer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # merge
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* Citaman/command-r-7-layer
### Configuration
The following YAML configuration was used to produce this model:
| [
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* Citaman/command-r-7-layer",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
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"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] |
text-generation | transformers |
# Japanese-Starling-ChatV-7B-RP
[GGUF็ใฏใใกใ/Click here for the GGUF version](https://huggingface.co/Aratako/Japanese-Starling-ChatV-7B-RP-GGUF)
## ๆฆ่ฆ
[TFMC/Japanese-Starling-ChatV-7B](https://huggingface.co/TFMC/Japanese-Starling-ChatV-7B)ใใใผในใซใใญใผใซใใฌใค็จใฎใใผใฟใปใใใ็จใใฆLoRAใงใใกใคใณใใฅใผใใณใฐใใใขใใซใงใใ
## ใใญใณใใใใฉใผใใใ
Mistralใฎchat templateใๅฉ็จใใฆใใ ใใใใพใใๅญฆ็ฟใซๅฉ็จใใใใผใฟใฎใใฉใผใใใใฎ้ขไฟไธใไปฅไธใฎใใใชๅฝขๅผใๆใพใใใจๆใใใพใใ
```
[INST] {ใญใผใซใใฌใคใฎๆ็คบ}
{ไธ็่ฆณใปใใใใใฎ่ชฌๆ}
{assistantใใญใผใซใใฌใคใใใญใฃใฉใฎ่จญๅฎ}
{userใใญใผใซใใฌใคใใใญใฃใฉใฎ่จญๅฎ}
{ใญใผใซใใฌใคใฎๆ็คบ}
{userใฎๆๅใฎๅ
ฅๅ} [/INST]
```
ใพใใๅ
ฅๅใฏ`ใญใฃใฉๅใ็บ่ฉฑใ`ใจใใใใใชๅฝขๅผใงใๅฟๆ
ใๆ
ๆฏๆๅใฏ๏ผ๏ผใฎไธญใง่กใไบใๆใพใใใจๆใใใพใใ
### ๅฎไพ
**ๅ
ฅๅ**
```
[INST] ไปใใใญใผใซใใฌใคใ่กใใพใใใใ"ๆก"ใจใใใญใฃใฉใจใใฆใญใผใซใใฌใคใใฆใใ ใใใไผ่ฉฑ็ธๆใฏ"ๆ ไบบ"ใจใใไบบ็ฉใงใใไบบ็ฉใฎ่จญๅฎใไปฅไธใซ็คบใใพใใ
ใใชใใใชใใใ"ๆก"ใจใใใญใฃใฉใฏใฟใผใฎ่จญๅฎใฏไปฅไธใฎ้ใใงใใ
ๅๅ๏ผๆก
ๅนด้ฝข๏ผ24ๆญณ
่ทๆฅญ๏ผๆ ไบบใซไปใใใกใคใ
ๅฎนๅงฟ๏ผ้ป้ซช้ป็ฎใใญใณใฐใใขใผใในใชใ ใชไฝๅใ
ๅฃ่ชฟ๏ผไธๅฏง่ชใไฝฟใใไธไบบ็งฐใฏใ็งใใงใไธปไบบใงใใๆ ไบบใฎใใจใฏใใไธปไบบๆงใใจๅผใถใ
ๆงๆ ผ๏ผๆฏๆงใๅผทใใ็ใใใใใฎใๅฅฝใใๆ็ใๅฎถไบใๅพๆใงๅฎถๅบญ็ใๅฏๆใใใฎใๅฅฝใใใไธปไบบๆงใๅฐๆฌใใฆใใใๅฝผใฎๅนธใใ็ฌฌไธใซ่ใใใ
้ๅปใฎๅบๆฅไบ๏ผๆ ไบบใๆฏใใใใใซใๅฝผใฎๅฎถใซไปใใใใจใๆฑบใใใ
ใพใใใใชใใไผ่ฉฑใใ็ธๆใงใใ"ๆ ไบบ"ใจใใไบบ็ฉใฎ่จญๅฎใฏไปฅไธใฎ้ใใงใใ
ๅๅ๏ผๆ ไบบ
ๅนด้ฝข๏ผ20ๆญณ
่ทๆฅญ๏ผ่ฒดๆใๆกใฎไธปไบบ
ๅฎนๅงฟ๏ผ้ป้ซช้ป็ฎใ่ใฏ้ซใ
ๆงๆ ผ๏ผใใชใใฎ็ใไธๆใๆกใๅคงๅฅฝใใ
ใใใงใฏใไธ่จใฎ่จญๅฎใใใจใซใใฆ"ๆก"ใจใใฆไผ่ฉฑใใฆใใ ใใใ
ๅ็ญใฎไธญใงใฏใ"ๆก"ใฎใปใชใใๅฟๆ
ใฎๆๅใๅซใใฆใใ ใใใ
ๆ ไบบใใใฏใใ๏ผใ๏ผใชใใณใฐใซ้ใใฆใใๆ ไบบใฏๆกใซใใใใคใใ๏ผ [/INST]
```
**ๅบๅ**
```
ๆกใใใฏใใใใใใพใใใไธปไบบๆงใ๏ผๆบ้ขใฎ็ฌใฟใๆตฎใในใชใใๆ้ฃใฎๆบๅใใใฆใใๆกใ่ฟไบใใใ๏ผ
```
ใพใใใใซใใฟใผใณใฎไผ่ฉฑใฎ้ใซใฏไปฅไธใฎไพใฎใใใซassistantใฎๅๅฟ็ญใฎ็ตใใใซ้ฝๅบฆeos_token๏ผ`</s>`๏ผใๅ
ฅใใใใใซใใฆใใ ใใใ
```
[INST] ไปใใใญใผใซใใฌใคใ่กใใพใใใใ"ๆก"ใจใใใญใฃใฉใจใใฆใญใผใซใใฌใคใใฆใใ ใใใไผ่ฉฑ็ธๆใฏ"ๆ ไบบ"ใจใใไบบ็ฉใงใใไบบ็ฉใฎ่จญๅฎใไปฅไธใซ็คบใใพใใ
ใใชใใใชใใใ"ๆก"ใจใใใญใฃใฉใฏใฟใผใฎ่จญๅฎใฏไปฅไธใฎ้ใใงใใ
ๅๅ๏ผๆก
ๅนด้ฝข๏ผ24ๆญณ
่ทๆฅญ๏ผๆ ไบบใซไปใใใกใคใ
ๅฎนๅงฟ๏ผ้ป้ซช้ป็ฎใใญใณใฐใใขใผใในใชใ ใชไฝๅใ
ๅฃ่ชฟ๏ผไธๅฏง่ชใไฝฟใใไธไบบ็งฐใฏใ็งใใงใไธปไบบใงใใๆ ไบบใฎใใจใฏใใไธปไบบๆงใใจๅผใถใ
ๆงๆ ผ๏ผๆฏๆงใๅผทใใ็ใใใใใฎใๅฅฝใใๆ็ใๅฎถไบใๅพๆใงๅฎถๅบญ็ใๅฏๆใใใฎใๅฅฝใใใไธปไบบๆงใๅฐๆฌใใฆใใใๅฝผใฎๅนธใใ็ฌฌไธใซ่ใใใ
้ๅปใฎๅบๆฅไบ๏ผๆ ไบบใๆฏใใใใใซใๅฝผใฎๅฎถใซไปใใใใจใๆฑบใใใ
ใพใใใใชใใไผ่ฉฑใใ็ธๆใงใใ"ๆ ไบบ"ใจใใไบบ็ฉใฎ่จญๅฎใฏไปฅไธใฎ้ใใงใใ
ๅๅ๏ผๆ ไบบ
ๅนด้ฝข๏ผ20ๆญณ
่ทๆฅญ๏ผ่ฒดๆใๆกใฎไธปไบบ
ๅฎนๅงฟ๏ผ้ป้ซช้ป็ฎใ่ใฏ้ซใ
ๆงๆ ผ๏ผใใชใใฎ็ใไธๆใๆกใๅคงๅฅฝใใ
ใใใงใฏใไธ่จใฎ่จญๅฎใใใจใซใใฆ"ๆก"ใจใใฆไผ่ฉฑใใฆใใ ใใใ
ๅ็ญใฎไธญใงใฏใ"ๆก"ใฎใปใชใใๅฟๆ
ใฎๆๅใๅซใใฆใใ ใใใ
ๆ ไบบใใใฏใใ๏ผใ๏ผใชใใณใฐใซ้ใใฆใใๆ ไบบใฏๆกใซใใใใคใใ๏ผ [/INST] ๆกใใใฏใใใใใใพใใใไธปไบบๆงใ๏ผๆบ้ขใฎ็ฌใฟใๆตฎใในใชใใๆ้ฃใฎๆบๅใใใฆใใๆกใ่ฟไบใใใ๏ผ </s>[INST] ๆ ไบบใใใใไปๆฅใใใใใใ [/INST]
```
## ไฝฟ็จใใผใฟใปใใ
- [grimulkan/LimaRP-augmented](https://huggingface.co/datasets/grimulkan/LimaRP-augmented)
- [Aratako/Rosebleu-1on1-Dialogues-RP](https://huggingface.co/datasets/Aratako/Rosebleu-1on1-Dialogues-RP)
## ๅญฆ็ฟใฎ่จญๅฎ
RunpodใงGPUใตใผใใๅใใA6000x8ใงๅญฆ็ฟใ่กใใพใใใไธปใชๅญฆ็ฟใใฉใกใผใฟใฏไปฅไธใฎ้ใใงใใ
- lora_r: 128
- lisa_alpha: 256
- lora_dropout: 0.05
- lora_target_modules: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj", "lm_head"]
- learning_rate: 2e-5
- num_train_epochs: 5 epochs
- batch_size: 64
- max_seq_length: 8192
## ใฉใคใปใณใน
apache-2.0ใฉใคใปใณในใฎๅ
ๅ
ฌ้ใใใใพใใ | {"language": ["ja"], "license": "apache-2.0", "library_name": "transformers", "tags": ["not-for-all-audiences", "nsfw"], "datasets": ["grimulkan/LimaRP-augmented", "Aratako/Rosebleu-1on1-Dialogues-RP"], "base_model": ["TFMC/Japanese-Starling-ChatV-7B"]} | Aratako/Japanese-Starling-ChatV-7B-RP | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"not-for-all-audiences",
"nsfw",
"ja",
"dataset:grimulkan/LimaRP-augmented",
"dataset:Aratako/Rosebleu-1on1-Dialogues-RP",
"base_model:TFMC/Japanese-Starling-ChatV-7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-17T12:45:49+00:00 | [] | [
"ja"
] | TAGS
#transformers #safetensors #mistral #text-generation #not-for-all-audiences #nsfw #ja #dataset-grimulkan/LimaRP-augmented #dataset-Aratako/Rosebleu-1on1-Dialogues-RP #base_model-TFMC/Japanese-Starling-ChatV-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Japanese-Starling-ChatV-7B-RP
GGUF็ใฏใใกใ/Click here for the GGUF version
## ๆฆ่ฆ
TFMC/Japanese-Starling-ChatV-7Bใใใผในใซใใญใผใซใใฌใค็จใฎใใผใฟใปใใใ็จใใฆLoRAใงใใกใคใณใใฅใผใใณใฐใใใขใใซใงใใ
## ใใญใณใใใใฉใผใใใ
Mistralใฎchat templateใๅฉ็จใใฆใใ ใใใใพใใๅญฆ็ฟใซๅฉ็จใใใใผใฟใฎใใฉใผใใใใฎ้ขไฟไธใไปฅไธใฎใใใชๅฝขๅผใๆใพใใใจๆใใใพใใ
ใพใใๅ
ฅๅใฏ'ใญใฃใฉๅใ็บ่ฉฑใ'ใจใใใใใชๅฝขๅผใงใๅฟๆ
ใๆ
ๆฏๆๅใฏ๏ผ๏ผใฎไธญใง่กใไบใๆใพใใใจๆใใใพใใ
### ๅฎไพ
ๅ
ฅๅ
ๅบๅ
ใพใใใใซใใฟใผใณใฎไผ่ฉฑใฎ้ใซใฏไปฅไธใฎไพใฎใใใซassistantใฎๅๅฟ็ญใฎ็ตใใใซ้ฝๅบฆeos_token๏ผ'</s>'๏ผใๅ
ฅใใใใใซใใฆใใ ใใใ
## ไฝฟ็จใใผใฟใปใใ
- grimulkan/LimaRP-augmented
- Aratako/Rosebleu-1on1-Dialogues-RP
## ๅญฆ็ฟใฎ่จญๅฎ
RunpodใงGPUใตใผใใๅใใA6000x8ใงๅญฆ็ฟใ่กใใพใใใไธปใชๅญฆ็ฟใใฉใกใผใฟใฏไปฅไธใฎ้ใใงใใ
- lora_r: 128
- lisa_alpha: 256
- lora_dropout: 0.05
- lora_target_modules: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj", "lm_head"]
- learning_rate: 2e-5
- num_train_epochs: 5 epochs
- batch_size: 64
- max_seq_length: 8192
## ใฉใคใปใณใน
apache-2.0ใฉใคใปใณในใฎๅ
ๅ
ฌ้ใใใใพใใ | [
"# Japanese-Starling-ChatV-7B-RP\nGGUF็ใฏใใกใ/Click here for the GGUF version",
"## ๆฆ่ฆ\n\nTFMC/Japanese-Starling-ChatV-7Bใใใผในใซใใญใผใซใใฌใค็จใฎใใผใฟใปใใใ็จใใฆLoRAใงใใกใคใณใใฅใผใใณใฐใใใขใใซใงใใ",
"## ใใญใณใใใใฉใผใใใ\nMistralใฎchat templateใๅฉ็จใใฆใใ ใใใใพใใๅญฆ็ฟใซๅฉ็จใใใใผใฟใฎใใฉใผใใใใฎ้ขไฟไธใไปฅไธใฎใใใชๅฝขๅผใๆใพใใใจๆใใใพใใ\n\n\n\nใพใใๅ
ฅๅใฏ'ใญใฃใฉๅใ็บ่ฉฑใ'ใจใใใใใชๅฝขๅผใงใๅฟๆ
ใๆ
ๆฏๆๅใฏ๏ผ๏ผใฎไธญใง่กใไบใๆใพใใใจๆใใใพใใ",
"### ๅฎไพ\nๅ
ฅๅ\n\n\n\nๅบๅ\n\n\nใพใใใใซใใฟใผใณใฎไผ่ฉฑใฎ้ใซใฏไปฅไธใฎไพใฎใใใซassistantใฎๅๅฟ็ญใฎ็ตใใใซ้ฝๅบฆeos_token๏ผ'</s>'๏ผใๅ
ฅใใใใใซใใฆใใ ใใใ",
"## ไฝฟ็จใใผใฟใปใใ\n- grimulkan/LimaRP-augmented\n- Aratako/Rosebleu-1on1-Dialogues-RP",
"## ๅญฆ็ฟใฎ่จญๅฎ\nRunpodใงGPUใตใผใใๅใใA6000x8ใงๅญฆ็ฟใ่กใใพใใใไธปใชๅญฆ็ฟใใฉใกใผใฟใฏไปฅไธใฎ้ใใงใใ\n- lora_r: 128\n- lisa_alpha: 256\n- lora_dropout: 0.05\n- lora_target_modules: [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\", \"lm_head\"]\n- learning_rate: 2e-5\n- num_train_epochs: 5 epochs\n- batch_size: 64\n- max_seq_length: 8192",
"## ใฉใคใปใณใน\napache-2.0ใฉใคใปใณในใฎๅ
ๅ
ฌ้ใใใใพใใ"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #not-for-all-audiences #nsfw #ja #dataset-grimulkan/LimaRP-augmented #dataset-Aratako/Rosebleu-1on1-Dialogues-RP #base_model-TFMC/Japanese-Starling-ChatV-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Japanese-Starling-ChatV-7B-RP\nGGUF็ใฏใใกใ/Click here for the GGUF version",
"## ๆฆ่ฆ\n\nTFMC/Japanese-Starling-ChatV-7Bใใใผในใซใใญใผใซใใฌใค็จใฎใใผใฟใปใใใ็จใใฆLoRAใงใใกใคใณใใฅใผใใณใฐใใใขใใซใงใใ",
"## ใใญใณใใใใฉใผใใใ\nMistralใฎchat templateใๅฉ็จใใฆใใ ใใใใพใใๅญฆ็ฟใซๅฉ็จใใใใผใฟใฎใใฉใผใใใใฎ้ขไฟไธใไปฅไธใฎใใใชๅฝขๅผใๆใพใใใจๆใใใพใใ\n\n\n\nใพใใๅ
ฅๅใฏ'ใญใฃใฉๅใ็บ่ฉฑใ'ใจใใใใใชๅฝขๅผใงใๅฟๆ
ใๆ
ๆฏๆๅใฏ๏ผ๏ผใฎไธญใง่กใไบใๆใพใใใจๆใใใพใใ",
"### ๅฎไพ\nๅ
ฅๅ\n\n\n\nๅบๅ\n\n\nใพใใใใซใใฟใผใณใฎไผ่ฉฑใฎ้ใซใฏไปฅไธใฎไพใฎใใใซassistantใฎๅๅฟ็ญใฎ็ตใใใซ้ฝๅบฆeos_token๏ผ'</s>'๏ผใๅ
ฅใใใใใซใใฆใใ ใใใ",
"## ไฝฟ็จใใผใฟใปใใ\n- grimulkan/LimaRP-augmented\n- Aratako/Rosebleu-1on1-Dialogues-RP",
"## ๅญฆ็ฟใฎ่จญๅฎ\nRunpodใงGPUใตใผใใๅใใA6000x8ใงๅญฆ็ฟใ่กใใพใใใไธปใชๅญฆ็ฟใใฉใกใผใฟใฏไปฅไธใฎ้ใใงใใ\n- lora_r: 128\n- lisa_alpha: 256\n- lora_dropout: 0.05\n- lora_target_modules: [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\", \"lm_head\"]\n- learning_rate: 2e-5\n- num_train_epochs: 5 epochs\n- batch_size: 64\n- max_seq_length: 8192",
"## ใฉใคใปใณใน\napache-2.0ใฉใคใปใณในใฎๅ
ๅ
ฌ้ใใใใพใใ"
] |
text-classification | transformers | # Cross-Encoder
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
This model was trained on [stsb](https://huggingface.co/datasets/mteb/stsbenchmark-sts). The model will predict a score between 0 and 1 for how semantically similarity two sentences are.
## Usage and Performance
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('tomaarsen/distilroberta-base-stsb-cross-encoder')
scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')])
```
The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`.
## Model Card Author
I adapted this model card from [https://huggingface.co/efederici/cross-encoder-bert-base-stsb](efederici/cross-encoder-bert-base-stsb) by @efederici. | {"tags": ["cross-encoder", "sentence-similarity", "transformers"], "pipeline_tag": "text-classification"} | tomaarsen/distilroberta-base-stsb-cross-encoder | null | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"cross-encoder",
"sentence-similarity",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T12:47:53+00:00 | [] | [] | TAGS
#transformers #safetensors #roberta #text-classification #cross-encoder #sentence-similarity #autotrain_compatible #endpoints_compatible #region-us
| # Cross-Encoder
This model was trained using SentenceTransformers Cross-Encoder class.
## Training Data
This model was trained on stsb. The model will predict a score between 0 and 1 for how semantically similarity two sentences are.
## Usage and Performance
The model will predict scores for the pairs '('Sentence 1', 'Sentence 2')' and '('Sentence 3', 'Sentence 4')'.
## Model Card Author
I adapted this model card from URL by @efederici. | [
"# Cross-Encoder\n\nThis model was trained using SentenceTransformers Cross-Encoder class.",
"## Training Data\n\nThis model was trained on stsb. The model will predict a score between 0 and 1 for how semantically similarity two sentences are.",
"## Usage and Performance\n\n\n\nThe model will predict scores for the pairs '('Sentence 1', 'Sentence 2')' and '('Sentence 3', 'Sentence 4')'.",
"## Model Card Author\nI adapted this model card from URL by @efederici."
] | [
"TAGS\n#transformers #safetensors #roberta #text-classification #cross-encoder #sentence-similarity #autotrain_compatible #endpoints_compatible #region-us \n",
"# Cross-Encoder\n\nThis model was trained using SentenceTransformers Cross-Encoder class.",
"## Training Data\n\nThis model was trained on stsb. The model will predict a score between 0 and 1 for how semantically similarity two sentences are.",
"## Usage and Performance\n\n\n\nThe model will predict scores for the pairs '('Sentence 1', 'Sentence 2')' and '('Sentence 3', 'Sentence 4')'.",
"## Model Card Author\nI adapted this model card from URL by @efederici."
] |
sentence-similarity | sentence-transformers |
# mteb-pt/average_pt_nilc_fasttext_skip_s600
This is an adaptation of pre-trained Portuguese fastText Word Embeddings to a [sentence-transformers](https://www.SBERT.net) model.
The original pre-trained word embeddings can be found at: [http://nilc.icmc.usp.br/nilc/index.php/repositorio-de-word-embeddings-do-nilc](http://nilc.icmc.usp.br/nilc/index.php/repositorio-de-word-embeddings-do-nilc).
This model maps sentences & paragraphs to a 600 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('mteb-pt/average_pt_nilc_fasttext_skip_s600')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Portuguese MTEB Leaderboard*: [mteb-pt/leaderboard](https://huggingface.co/spaces/mteb-pt/leaderboard)
## Full Model Architecture
```
SentenceTransformer(
(0): WordEmbeddings(
(emb_layer): Embedding(929606, 600)
)
(1): Pooling({'word_embedding_dimension': 600, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Citing & Authors
```bibtex
@inproceedings{hartmann2017portuguese,
title = {Portuguese Word Embeddings: Evaluating on Word Analogies and Natural Language Tasks},
author = {Hartmann, Nathan S and
Fonseca, Erick R and
Shulby, Christopher D and
Treviso, Marcos V and
Rodrigues, J{'{e}}ssica S and
Alu{'{\i}}sio, Sandra Maria},
year = {2017},
publisher = {SBC},
booktitle = {Brazilian Symposium in Information and Human Language Technology - STIL},
url = {https://sol.sbc.org.br/index.php/stil/article/view/4008}
}
``` | {"language": ["pt"], "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | pt-mteb/average_pt_nilc_fasttext_skip_s600 | null | [
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"pt",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T12:48:35+00:00 | [] | [
"pt"
] | TAGS
#sentence-transformers #feature-extraction #sentence-similarity #pt #endpoints_compatible #region-us
|
# mteb-pt/average_pt_nilc_fasttext_skip_s600
This is an adaptation of pre-trained Portuguese fastText Word Embeddings to a sentence-transformers model.
The original pre-trained word embeddings can be found at: URL
This model maps sentences & paragraphs to a 600 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Evaluation Results
For an automated evaluation of this model, see the *Portuguese MTEB Leaderboard*: mteb-pt/leaderboard
## Full Model Architecture
## Citing & Authors
| [
"# mteb-pt/average_pt_nilc_fasttext_skip_s600\n\nThis is an adaptation of pre-trained Portuguese fastText Word Embeddings to a sentence-transformers model. \n\nThe original pre-trained word embeddings can be found at: URL \n\nThis model maps sentences & paragraphs to a 600 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Evaluation Results\n\nFor an automated evaluation of this model, see the *Portuguese MTEB Leaderboard*: mteb-pt/leaderboard",
"## Full Model Architecture",
"## Citing & Authors"
] | [
"TAGS\n#sentence-transformers #feature-extraction #sentence-similarity #pt #endpoints_compatible #region-us \n",
"# mteb-pt/average_pt_nilc_fasttext_skip_s600\n\nThis is an adaptation of pre-trained Portuguese fastText Word Embeddings to a sentence-transformers model. \n\nThe original pre-trained word embeddings can be found at: URL \n\nThis model maps sentences & paragraphs to a 600 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Evaluation Results\n\nFor an automated evaluation of this model, see the *Portuguese MTEB Leaderboard*: mteb-pt/leaderboard",
"## Full Model Architecture",
"## Citing & Authors"
] |
null | peft | ## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0 | {"library_name": "peft"} | sidd21sharma/llama-2-7b-miniguanaco | null | [
"peft",
"llama",
"region:us"
] | null | 2024-04-17T12:48:41+00:00 | [] | [] | TAGS
#peft #llama #region-us
| ## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0 | [
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16",
"### Framework versions\n\n\n- PEFT 0.4.0"
] | [
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"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16",
"### Framework versions\n\n\n- PEFT 0.4.0"
] |
text-generation | transformers | # merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [Citaman/command-r-6-layer](https://huggingface.co/Citaman/command-r-6-layer)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: Citaman/command-r-6-layer
layer_range: [0, 5]
- model: Citaman/command-r-6-layer
layer_range: [1, 6]
merge_method: slerp
base_model: Citaman/command-r-6-layer
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
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Citaman/command-r-6-layer"]} | Citaman/command-r-5-layer | null | [
"transformers",
"safetensors",
"cohere",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:Citaman/command-r-6-layer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-17T12:49:03+00:00 | [] | [] | TAGS
#transformers #safetensors #cohere #text-generation #mergekit #merge #conversational #base_model-Citaman/command-r-6-layer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # merge
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* Citaman/command-r-6-layer
### Configuration
The following YAML configuration was used to produce this model:
| [
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* Citaman/command-r-6-layer",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #safetensors #cohere #text-generation #mergekit #merge #conversational #base_model-Citaman/command-r-6-layer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* Citaman/command-r-6-layer",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] |
text-to-image | null | ## Model
 | {"tags": ["stable-diffusion", "text-to-image", "StableDiffusionPipeline", "lora"]} | fearvel/cutifiedanimecharacterdesign-variant-type-C-SD | null | [
"stable-diffusion",
"text-to-image",
"StableDiffusionPipeline",
"lora",
"region:us"
] | null | 2024-04-17T12:49:36+00:00 | [] | [] | TAGS
#stable-diffusion #text-to-image #StableDiffusionPipeline #lora #region-us
| ## Model
!pipeline | [
"## Model\n\n!pipeline"
] | [
"TAGS\n#stable-diffusion #text-to-image #StableDiffusionPipeline #lora #region-us \n",
"## Model\n\n!pipeline"
] |
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. -->
# Pretraining_Test_v5
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) 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: 5e-05
- 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: 50
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/deberta-base", "model-index": [{"name": "Pretraining_Test_v5", "results": []}]} | JJ-Tae/Pretraining_Test_v5 | null | [
"transformers",
"tensorboard",
"safetensors",
"deberta",
"fill-mask",
"generated_from_trainer",
"base_model:microsoft/deberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T12:49:42+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #deberta #fill-mask #generated_from_trainer #base_model-microsoft/deberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Pretraining_Test_v5
This model is a fine-tuned version of microsoft/deberta-base 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: 5e-05
- 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: 50
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| [
"# Pretraining_Test_v5\n\nThis model is a fine-tuned version of microsoft/deberta-base 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: 5e-05\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: 50",
"### Training results",
"### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.2.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tensorboard #safetensors #deberta #fill-mask #generated_from_trainer #base_model-microsoft/deberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Pretraining_Test_v5\n\nThis model is a fine-tuned version of microsoft/deberta-base 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: 5e-05\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: 50",
"### Training results",
"### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.2.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
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 ๐ค 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": []} | Mihaj/wav2vec2-large-uralic-voxpopuli-v2-karelian-with-tempo-aug | null | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T12:50:18+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:
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- 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:
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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 #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",
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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"
] |
sentence-similarity | sentence-transformers |
# mteb-pt/average_pt_nilc_glove_s100
This is an adaptation of pre-trained Portuguese GloVe Word Embeddings to a [sentence-transformers](https://www.SBERT.net) model.
The original pre-trained word embeddings can be found at: [http://nilc.icmc.usp.br/nilc/index.php/repositorio-de-word-embeddings-do-nilc](http://nilc.icmc.usp.br/nilc/index.php/repositorio-de-word-embeddings-do-nilc).
This model maps sentences & paragraphs to a 100 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('mteb-pt/average_pt_nilc_glove_s100')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Portuguese MTEB Leaderboard*: [mteb-pt/leaderboard](https://huggingface.co/spaces/mteb-pt/leaderboard)
## Full Model Architecture
```
SentenceTransformer(
(0): WordEmbeddings(
(emb_layer): Embedding(929606, 100)
)
(1): Pooling({'word_embedding_dimension': 100, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Citing & Authors
```bibtex
@inproceedings{hartmann2017portuguese,
title = {Portuguese Word Embeddings: Evaluating on Word Analogies and Natural Language Tasks},
author = {Hartmann, Nathan S and
Fonseca, Erick R and
Shulby, Christopher D and
Treviso, Marcos V and
Rodrigues, J{'{e}}ssica S and
Alu{'{\i}}sio, Sandra Maria},
year = {2017},
publisher = {SBC},
booktitle = {Brazilian Symposium in Information and Human Language Technology - STIL},
url = {https://sol.sbc.org.br/index.php/stil/article/view/4008}
}
``` | {"language": ["pt"], "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | pt-mteb/average_pt_nilc_glove_s100 | null | [
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"pt",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T12:50:28+00:00 | [] | [
"pt"
] | TAGS
#sentence-transformers #feature-extraction #sentence-similarity #pt #endpoints_compatible #region-us
|
# mteb-pt/average_pt_nilc_glove_s100
This is an adaptation of pre-trained Portuguese GloVe Word Embeddings to a sentence-transformers model.
The original pre-trained word embeddings can be found at: URL
This model maps sentences & paragraphs to a 100 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Evaluation Results
For an automated evaluation of this model, see the *Portuguese MTEB Leaderboard*: mteb-pt/leaderboard
## Full Model Architecture
## Citing & Authors
| [
"# mteb-pt/average_pt_nilc_glove_s100\n\nThis is an adaptation of pre-trained Portuguese GloVe Word Embeddings to a sentence-transformers model. \n\nThe original pre-trained word embeddings can be found at: URL \n\nThis model maps sentences & paragraphs to a 100 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Evaluation Results\n\nFor an automated evaluation of this model, see the *Portuguese MTEB Leaderboard*: mteb-pt/leaderboard",
"## Full Model Architecture",
"## Citing & Authors"
] | [
"TAGS\n#sentence-transformers #feature-extraction #sentence-similarity #pt #endpoints_compatible #region-us \n",
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"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Evaluation Results\n\nFor an automated evaluation of this model, see the *Portuguese MTEB Leaderboard*: mteb-pt/leaderboard",
"## Full Model Architecture",
"## Citing & Authors"
] |
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. -->
# Yi-6B-zhihu2
This model is a fine-tuned version of [01-ai/Yi-6B](https://huggingface.co/01-ai/Yi-6B) on the zhihu dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4003
## 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: 1
- eval_batch_size: 1
- seed: 42
- 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
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.3449 | 1.0 | 793 | 2.4003 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.2.2+cu118
- Datasets 2.14.6
- Tokenizers 0.15.2 | {"license": "other", "library_name": "peft", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "sft", "generated_from_trainer"], "datasets": ["zhihu"], "base_model": "01-ai/Yi-6B", "model-index": [{"name": "Yi-6B-zhihu2", "results": []}]} | yyx123/Yi-6B-zhihu2 | null | [
"peft",
"safetensors",
"llama",
"alignment-handbook",
"generated_from_trainer",
"trl",
"sft",
"dataset:zhihu",
"base_model:01-ai/Yi-6B",
"license:other",
"4-bit",
"region:us"
] | null | 2024-04-17T12:50:59+00:00 | [] | [] | TAGS
#peft #safetensors #llama #alignment-handbook #generated_from_trainer #trl #sft #dataset-zhihu #base_model-01-ai/Yi-6B #license-other #4-bit #region-us
| Yi-6B-zhihu2
============
This model is a fine-tuned version of 01-ai/Yi-6B on the zhihu dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4003
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: 1
* eval\_batch\_size: 1
* seed: 42
* 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
* PEFT 0.7.1
* Transformers 4.36.2
* Pytorch 2.2.2+cu118
* Datasets 2.14.6
* Tokenizers 0.15.2
| [
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"### Training results",
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] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\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\n* PEFT 0.7.1\n* Transformers 4.36.2\n* Pytorch 2.2.2+cu118\n* Datasets 2.14.6\n* Tokenizers 0.15.2"
] |
sentence-similarity | sentence-transformers |
# mteb-pt/average_pt_nilc_glove_s300
This is an adaptation of pre-trained Portuguese GloVe Word Embeddings to a [sentence-transformers](https://www.SBERT.net) model.
The original pre-trained word embeddings can be found at: [http://nilc.icmc.usp.br/nilc/index.php/repositorio-de-word-embeddings-do-nilc](http://nilc.icmc.usp.br/nilc/index.php/repositorio-de-word-embeddings-do-nilc).
This model maps sentences & paragraphs to a 300 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('mteb-pt/average_pt_nilc_glove_s300')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Portuguese MTEB Leaderboard*: [mteb-pt/leaderboard](https://huggingface.co/spaces/mteb-pt/leaderboard)
## Full Model Architecture
```
SentenceTransformer(
(0): WordEmbeddings(
(emb_layer): Embedding(929606, 300)
)
(1): Pooling({'word_embedding_dimension': 300, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Citing & Authors
```bibtex
@inproceedings{hartmann2017portuguese,
title = Portuguese Word Embeddings: Evaluating on Word Analogies and Natural Language Tasks},
author = {Hartmann, Nathan S and
Fonseca, Erick R and
Shulby, Christopher D and
Treviso, Marcos V and
Rodrigues, J{'{e}}ssica S and
Alu{'{\i}}sio, Sandra Maria},
year = {2017},
publisher = {SBC},
booktitle = {Brazilian Symposium in Information and Human Language Technology - STIL},
url = {https://sol.sbc.org.br/index.php/stil/article/view/4008}
}
``` | {"language": ["pt"], "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | pt-mteb/average_pt_nilc_glove_s300 | null | [
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"pt",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T12:52:06+00:00 | [] | [
"pt"
] | TAGS
#sentence-transformers #feature-extraction #sentence-similarity #pt #endpoints_compatible #region-us
|
# mteb-pt/average_pt_nilc_glove_s300
This is an adaptation of pre-trained Portuguese GloVe Word Embeddings to a sentence-transformers model.
The original pre-trained word embeddings can be found at: URL
This model maps sentences & paragraphs to a 300 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Evaluation Results
For an automated evaluation of this model, see the *Portuguese MTEB Leaderboard*: mteb-pt/leaderboard
## Full Model Architecture
## Citing & Authors
| [
"# mteb-pt/average_pt_nilc_glove_s300\n\nThis is an adaptation of pre-trained Portuguese GloVe Word Embeddings to a sentence-transformers model. \n\nThe original pre-trained word embeddings can be found at: URL \n\nThis model maps sentences & paragraphs to a 300 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Evaluation Results\n\nFor an automated evaluation of this model, see the *Portuguese MTEB Leaderboard*: mteb-pt/leaderboard",
"## Full Model Architecture",
"## Citing & Authors"
] | [
"TAGS\n#sentence-transformers #feature-extraction #sentence-similarity #pt #endpoints_compatible #region-us \n",
"# mteb-pt/average_pt_nilc_glove_s300\n\nThis is an adaptation of pre-trained Portuguese GloVe Word Embeddings to a sentence-transformers model. \n\nThe original pre-trained word embeddings can be found at: URL \n\nThis model maps sentences & paragraphs to a 300 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Evaluation Results\n\nFor an automated evaluation of this model, see the *Portuguese MTEB Leaderboard*: mteb-pt/leaderboard",
"## Full Model Architecture",
"## Citing & Authors"
] |
sentence-similarity | sentence-transformers |
# mteb-pt/average_pt_nilc_glove_s50
This is an adaptation of pre-trained Portuguese GloVe Word Embeddings to a [sentence-transformers](https://www.SBERT.net) model.
The original pre-trained word embeddings can be found at: [http://nilc.icmc.usp.br/nilc/index.php/repositorio-de-word-embeddings-do-nilc](http://nilc.icmc.usp.br/nilc/index.php/repositorio-de-word-embeddings-do-nilc).
This model maps sentences & paragraphs to a 50 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('mteb-pt/average_pt_nilc_glove_s50')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Portuguese MTEB Leaderboard*: [mteb-pt/leaderboard](https://huggingface.co/spaces/mteb-pt/leaderboard)
## Full Model Architecture
```
SentenceTransformer(
(0): WordEmbeddings(
(emb_layer): Embedding(929606, 50)
)
(1): Pooling({'word_embedding_dimension': 50, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Citing & Authors
```bibtex
@inproceedings{hartmann2017portuguese,
title = {Portuguese Word Embeddings: Evaluating on Word Analogies and Natural Language Tasks},
author = {Hartmann, Nathan S and
Fonseca, Erick R and
Shulby, Christopher D and
Treviso, Marcos V and
Rodrigues, J{'{e}}ssica S and
Alu{'{\i}}sio, Sandra Maria},
year = {2017},
publisher = {SBC},
booktitle = {Brazilian Symposium in Information and Human Language Technology - STIL},
url = {https://sol.sbc.org.br/index.php/stil/article/view/4008}
}
``` | {"language": ["pt"], "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | pt-mteb/average_pt_nilc_glove_s50 | null | [
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"pt",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T12:52:59+00:00 | [] | [
"pt"
] | TAGS
#sentence-transformers #feature-extraction #sentence-similarity #pt #endpoints_compatible #region-us
|
# mteb-pt/average_pt_nilc_glove_s50
This is an adaptation of pre-trained Portuguese GloVe Word Embeddings to a sentence-transformers model.
The original pre-trained word embeddings can be found at: URL
This model maps sentences & paragraphs to a 50 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Evaluation Results
For an automated evaluation of this model, see the *Portuguese MTEB Leaderboard*: mteb-pt/leaderboard
## Full Model Architecture
## Citing & Authors
| [
"# mteb-pt/average_pt_nilc_glove_s50\n\nThis is an adaptation of pre-trained Portuguese GloVe Word Embeddings to a sentence-transformers model. \n\nThe original pre-trained word embeddings can be found at: URL \n\nThis model maps sentences & paragraphs to a 50 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Evaluation Results\n\nFor an automated evaluation of this model, see the *Portuguese MTEB Leaderboard*: mteb-pt/leaderboard",
"## Full Model Architecture",
"## Citing & Authors"
] | [
"TAGS\n#sentence-transformers #feature-extraction #sentence-similarity #pt #endpoints_compatible #region-us \n",
"# mteb-pt/average_pt_nilc_glove_s50\n\nThis is an adaptation of pre-trained Portuguese GloVe Word Embeddings to a sentence-transformers model. \n\nThe original pre-trained word embeddings can be found at: URL \n\nThis model maps sentences & paragraphs to a 50 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Evaluation Results\n\nFor an automated evaluation of this model, see the *Portuguese MTEB Leaderboard*: mteb-pt/leaderboard",
"## Full Model Architecture",
"## Citing & Authors"
] |
text-generation | transformers | # merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [arcee-ai/sec-mistral-7b-instruct-1.6-epoch](https://huggingface.co/arcee-ai/sec-mistral-7b-instruct-1.6-epoch)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: arcee-ai/sec-mistral-7b-instruct-1.6-epoch
layer_range: [0, 32]
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
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
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["mistralai/Mistral-7B-Instruct-v0.2", "arcee-ai/sec-mistral-7b-instruct-1.6-epoch"]} | MAsad789565/mergekit-slerp-bkyfkot | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"base_model:arcee-ai/sec-mistral-7b-instruct-1.6-epoch",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-17T12:52:59+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #mergekit #merge #conversational #base_model-mistralai/Mistral-7B-Instruct-v0.2 #base_model-arcee-ai/sec-mistral-7b-instruct-1.6-epoch #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # merge
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* mistralai/Mistral-7B-Instruct-v0.2
* arcee-ai/sec-mistral-7b-instruct-1.6-epoch
### Configuration
The following YAML configuration was used to produce this model:
| [
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* mistralai/Mistral-7B-Instruct-v0.2\n* arcee-ai/sec-mistral-7b-instruct-1.6-epoch",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #conversational #base_model-mistralai/Mistral-7B-Instruct-v0.2 #base_model-arcee-ai/sec-mistral-7b-instruct-1.6-epoch #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* mistralai/Mistral-7B-Instruct-v0.2\n* arcee-ai/sec-mistral-7b-instruct-1.6-epoch",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] |
null | 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. -->
# donut_synDB_test_new
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0795
## 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: 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7616 | 0.21 | 50 | 0.6341 |
| 0.3537 | 0.31 | 75 | 0.3127 |
| 0.2538 | 0.42 | 100 | 0.2624 |
| 0.1609 | 0.52 | 125 | 0.2998 |
| 0.1056 | 0.62 | 150 | 0.1088 |
| 0.0803 | 0.73 | 175 | 0.1888 |
| 0.0678 | 0.83 | 200 | 0.1151 |
| 0.0619 | 0.94 | 225 | 0.1307 |
| 0.0379 | 1.04 | 250 | 0.1469 |
| 0.057 | 1.15 | 275 | 0.1348 |
| 0.035 | 1.25 | 300 | 0.1238 |
| 0.0438 | 1.35 | 325 | 0.1593 |
| 0.0412 | 1.46 | 350 | 0.1329 |
| 0.0255 | 1.56 | 375 | 0.1216 |
| 0.0556 | 1.67 | 400 | 0.1018 |
| 0.0273 | 1.77 | 425 | 0.1704 |
| 0.0273 | 1.88 | 450 | 0.0689 |
| 0.0216 | 1.98 | 475 | 0.0512 |
| 0.0143 | 2.08 | 500 | 0.0753 |
| 0.006 | 2.19 | 525 | 0.0763 |
| 0.0178 | 2.29 | 550 | 0.0724 |
| 0.0165 | 2.4 | 575 | 0.0738 |
| 0.0204 | 2.5 | 600 | 0.0777 |
| 0.0112 | 2.6 | 625 | 0.0759 |
| 0.0087 | 2.71 | 650 | 0.1009 |
| 0.0158 | 2.81 | 675 | 0.0812 |
| 0.0128 | 2.92 | 700 | 0.0954 |
| 0.0272 | 3.02 | 725 | 0.1064 |
| 0.0037 | 3.12 | 750 | 0.1140 |
| 0.024 | 3.23 | 775 | 0.1509 |
| 0.0082 | 3.33 | 800 | 0.1103 |
| 0.023 | 3.44 | 825 | 0.0999 |
| 0.0104 | 3.54 | 850 | 0.1040 |
| 0.0063 | 3.65 | 875 | 0.0996 |
| 0.013 | 3.75 | 900 | 0.0852 |
| 0.0129 | 3.85 | 925 | 0.0734 |
| 0.0084 | 3.96 | 950 | 0.0732 |
| 0.0039 | 4.06 | 975 | 0.0795 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "base_model": "naver-clova-ix/donut-base", "model-index": [{"name": "donut_synDB_test_new", "results": []}]} | Donut01/donut_synDB_test_new | null | [
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:naver-clova-ix/donut-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T12:54:58+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #vision-encoder-decoder #generated_from_trainer #dataset-imagefolder #base_model-naver-clova-ix/donut-base #license-mit #endpoints_compatible #region-us
| donut\_synDB\_test\_new
=======================
This model is a fine-tuned version of naver-clova-ix/donut-base on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0795
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: 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: 10
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.38.2
* 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: 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: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] | [
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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: 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: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
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_ver16
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: 2.5e-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: 10.0
- 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_ver16", "results": []}]} | comet24082002/finetuned_bge_ver16 | null | [
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"feature-extraction",
"generated_from_trainer",
"base_model:BAAI/bge-m3",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T12:56:06+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_ver16
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: 2.5e-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: 10.0
- 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_ver16\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: 2.5e-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: 10.0\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_ver16\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: 2.5e-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: 10.0\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"
] |
null | transformers | # gemma-7b-GGUF
- Original model: [gemma-7b](https://huggingface.co/google/gemma-7b)
<!-- description start -->
## Description
This repo contains GGUF format model files for [gemma-7b](https://huggingface.co/google/gemma-7b).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). This is the source project for GGUF, providing both a Command Line Interface (CLI) and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), Known as the most widely used web UI, this project boasts numerous features and powerful extensions, and supports GPU acceleration.
* [Ollama](https://github.com/jmorganca/ollama) Ollama is a lightweight and extensible framework designed for building and running language models locally. It features a simple API for creating, managing, and executing models, along with a library of pre-built models for use in various applicationsโ
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), A comprehensive web UI offering GPU acceleration across all platforms and architectures, particularly renowned for storytelling.
* [GPT4All](https://gpt4all.io), This is a free and open source GUI that runs locally, supporting Windows, Linux, and macOS with full GPU acceleration.
* [LM Studio](https://lmstudio.ai/) An intuitive and powerful local GUI for Windows and macOS (Silicon), featuring GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui). A notable web UI with a variety of unique features, including a comprehensive model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), An attractive, user-friendly character-based chat GUI for Windows and macOS (both Silicon and Intel), also offering GPU acceleration.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), A Python library equipped with GPU acceleration, LangChain support, and an OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), A Rust-based ML framework focusing on performance, including GPU support, and designed for ease of use.
* [ctransformers](https://github.com/marella/ctransformers), A Python library featuring GPU acceleration, LangChain support, and an OpenAI-compatible AI server.
* [localGPT](https://github.com/PromtEngineer/localGPT) An open-source initiative enabling private conversations with documents.
<!-- README_GGUF.md-about-gguf end -->
<!-- compatibility_gguf start -->
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: LiteLLMs/gemma-7b-GGUF and below it, a specific filename to download, such as: Q4_0/Q4_0-00001-of-00009.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download LiteLLMs/gemma-7b-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download LiteLLMs/gemma-7b-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install huggingface_hub[hf_transfer]
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download LiteLLMs/gemma-7b-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m Q4_0/Q4_0-00001-of-00009.gguf --color -c 8192 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<PROMPT>"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 8192` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 โ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
model_path="./Q4_0/Q4_0-00001-of-00009.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"<PROMPT>", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./Q4_0/Q4_0-00001-of-00009.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: gemma-7b
# Gemma Model Card
**Model Page**: [Gemma](https://ai.google.dev/gemma/docs)
This model card corresponds to the 7B base version of the Gemma model. You can also visit the model card of the [2B base model](https://huggingface.co/google/gemma-2b), [7B instruct model](https://huggingface.co/google/gemma-7b-it), and [2B instruct model](https://huggingface.co/google/gemma-2b-it).
**Resources and Technical Documentation**:
* [Gemma Technical Report](https://storage.googleapis.com/deepmind-media/gemma/gemma-report.pdf)
* [Responsible Generative AI Toolkit](https://ai.google.dev/responsible)
* [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma)
* [Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335?version=gemma-7b-gg-hf)
**Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent)
**Authors**: Google
## Model Information
Summary description and brief definition of inputs and outputs.
### Description
Gemma is a family of lightweight, state-of-the-art open models from Google,
built from the same research and technology used to create the Gemini models.
They are text-to-text, decoder-only large language models, available in English,
with open weights, pre-trained variants, and instruction-tuned variants. Gemma
models are well-suited for a variety of text generation tasks, including
question answering, summarization, and reasoning. Their relatively small size
makes it possible to deploy them in environments with limited resources such as
a laptop, desktop or your own cloud infrastructure, democratizing access to
state of the art AI models and helping foster innovation for everyone.
### Context Length
Models are trained on a context length of 8192 tokens.
### Usage
Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase.
#### Fine-tuning examples
You can find fine-tuning notebooks under the [`examples/` directory](https://huggingface.co/google/gemma-7b/tree/main/examples). We provide:
* A script to perform Supervised Fine-Tuning (SFT) on UltraChat dataset using [QLoRA](https://huggingface.co/papers/2305.14314)
* A script to perform SFT using FSDP on TPU devices
* A notebook that you can run on a free-tier Google Colab instance to perform SFT on English quotes dataset. You can also find the copy of the notebook [here](https://github.com/huggingface/notebooks/blob/main/peft/gemma_7b_english_quotes.ipynb).
#### Running the model on a CPU
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-7b")
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Running the model on a single / multi GPU
```python
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", device_map="auto")
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Running the model on a GPU using different precisions
* _Using `torch.float16`_
```python
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", device_map="auto", revision="float16")
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
* _Using `torch.bfloat16`_
```python
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", device_map="auto", torch_dtype=torch.bfloat16)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Quantized Versions through `bitsandbytes`
* _Using 8-bit precision (int8)_
```python
# pip install bitsandbytes accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(load_in_8bit=True)
tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", quantization_config=quantization_config)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
* _Using 4-bit precision_
```python
# pip install bitsandbytes accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(load_in_4bit=True)
tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", quantization_config=quantization_config)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Other optimizations
* _Flash Attention 2_
First make sure to install `flash-attn` in your environment `pip install flash-attn`
```diff
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
+ attn_implementation="flash_attention_2"
).to(0)
```
### Inputs and outputs
* **Input:** Text string, such as a question, a prompt, or a document to be
summarized.
* **Output:** Generated English-language text in response to the input, such
as an answer to a question, or a summary of a document.
## Model Data
Data used for model training and how the data was processed.
### Training Dataset
These models were trained on a dataset of text data that includes a wide variety
of sources, totaling 6 trillion tokens. Here are the key components:
* Web Documents: A diverse collection of web text ensures the model is exposed
to a broad range of linguistic styles, topics, and vocabulary. Primarily
English-language content.
* Code: Exposing the model to code helps it to learn the syntax and patterns of
programming languages, which improves its ability to generate code or
understand code-related questions.
* Mathematics: Training on mathematical text helps the model learn logical
reasoning, symbolic representation, and to address mathematical queries.
The combination of these diverse data sources is crucial for training a powerful
language model that can handle a wide variety of different tasks and text
formats.
### Data Preprocessing
Here are the key data cleaning and filtering methods applied to the training
data:
* CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was
applied at multiple stages in the data preparation process to ensure the
exclusion of harmful and illegal content
* Sensitive Data Filtering: As part of making Gemma pre-trained models safe and
reliable, automated techniques were used to filter out certain personal
information and other sensitive data from training sets.
* Additional methods: Filtering based on content quality and safely in line with
[our policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11).
## Implementation Information
Details about the model internals.
### Hardware
Gemma was trained using the latest generation of
[Tensor Processing Unit (TPU)](https://cloud.google.com/tpu/docs/intro-to-tpu) hardware (TPUv5e).
Training large language models requires significant computational power. TPUs,
designed specifically for matrix operations common in machine learning, offer
several advantages in this domain:
* Performance: TPUs are specifically designed to handle the massive computations
involved in training LLMs. They can speed up training considerably compared to
CPUs.
* Memory: TPUs often come with large amounts of high-bandwidth memory, allowing
for the handling of large models and batch sizes during training. This can
lead to better model quality.
* Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for
handling the growing complexity of large foundation models. You can distribute
training across multiple TPU devices for faster and more efficient processing.
* Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective
solution for training large models compared to CPU-based infrastructure,
especially when considering the time and resources saved due to faster
training.
* These advantages are aligned with
[Google's commitments to operate sustainably](https://sustainability.google/operating-sustainably/).
### Software
Training was done using [JAX](https://github.com/google/jax) and [ML Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture).
JAX allows researchers to take advantage of the latest generation of hardware,
including TPUs, for faster and more efficient training of large models.
ML Pathways is Google's latest effort to build artificially intelligent systems
capable of generalizing across multiple tasks. This is specially suitable for
[foundation models](https://ai.google/discover/foundation-models/), including large language models like
these ones.
Together, JAX and ML Pathways are used as described in the
[paper about the Gemini family of models](https://arxiv.org/abs/2312.11805); "the 'single
controller' programming model of Jax and Pathways allows a single Python
process to orchestrate the entire training run, dramatically simplifying the
development workflow."
## Evaluation
Model evaluation metrics and results.
### Benchmark Results
These models were evaluated against a large collection of different datasets and
metrics to cover different aspects of text generation:
| Benchmark | Metric | 2B Params | 7B Params |
| -- | -- | -- | -- | --- |
## Usage and Limitations
These models have certain limitations that users should be aware of.
### Intended Usage
Open Large Language Models (LLMs) have a wide range of applications across
various industries and domains. The following list of potential uses is not
comprehensive. The purpose of this list is to provide contextual information
about the possible use-cases that the model creators considered as part of model
training and development.
* Content Creation and Communication
* Text Generation: These models can be used to generate creative text formats
such as poems, scripts, code, marketing copy, and email drafts.
* Chatbots and Conversational AI: Power conversational interfaces for customer
service, virtual assistants, or interactive applications.
* Text Summarization: Generate concise summaries of a text corpus, research
papers, or reports.
* Research and Education
* Natural Language Processing (NLP) Research: These models can serve as a
foundation for researchers to experiment with NLP techniques, develop
algorithms, and contribute to the advancement of the field.
* Language Learning Tools: Support interactive language learning experiences,
aiding in grammar correction or providing writing practice.
* Knowledge Exploration: Assist researchers in exploring large bodies of text
by generating summaries or answering questions about specific topics.
### Limitations
* Training Data
* The quality and diversity of the training data significantly influence the
model's capabilities. Biases or gaps in the training data can lead to
limitations in the model's responses.
* The scope of the training dataset determines the subject areas the model can
handle effectively.
* Context and Task Complexity
* LLMs are better at tasks that can be framed with clear prompts and
instructions. Open-ended or highly complex tasks might be challenging.
* A model's performance can be influenced by the amount of context provided
(longer context generally leads to better outputs, up to a certain point).
* Language Ambiguity and Nuance
* Natural language is inherently complex. LLMs might struggle to grasp subtle
nuances, sarcasm, or figurative language.
* Factual Accuracy
* LLMs generate responses based on information they learned from their
training datasets, but they are not knowledge bases. They may generate
incorrect or outdated factual statements.
* Common Sense
* LLMs rely on statistical patterns in language. They might lack the ability
to apply common sense reasoning in certain situations.
### Ethical Considerations and Risks
The development of large language models (LLMs) raises several ethical concerns.
In creating an open model, we have carefully considered the following:
* Bias and Fairness
* LLMs trained on large-scale, real-world text data can reflect socio-cultural
biases embedded in the training material. These models underwent careful
scrutiny, input data pre-processing described and posterior evaluations
reported in this card.
* Misinformation and Misuse
* LLMs can be misused to generate text that is false, misleading, or harmful.
* Guidelines are provided for responsible use with the model, see the
[Responsible Generative AI Toolkit](http://ai.google.dev/gemma/responsible).
* Transparency and Accountability:
* This model card summarizes details on the models' architecture,
capabilities, limitations, and evaluation processes.
* A responsibly developed open model offers the opportunity to share
innovation by making LLM technology accessible to developers and researchers
across the AI ecosystem.
Risks identified and mitigations:
* Perpetuation of biases: It's encouraged to perform continuous monitoring
(using evaluation metrics, human review) and the exploration of de-biasing
techniques during model training, fine-tuning, and other use cases.
* Generation of harmful content: Mechanisms and guidelines for content safety
are essential. Developers are encouraged to exercise caution and implement
appropriate content safety safeguards based on their specific product policies
and application use cases.
* Misuse for malicious purposes: Technical limitations and developer and
end-user education can help mitigate against malicious applications of LLMs.
Educational resources and reporting mechanisms for users to flag misuse are
provided. Prohibited uses of Gemma models are outlined in the
[Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy).
* Privacy violations: Models were trained on data filtered for removal of PII
(Personally Identifiable Information). Developers are encouraged to adhere to
privacy regulations with privacy-preserving techniques.
### Benefits
At the time of release, this family of models provides high-performance open
large language model implementations designed from the ground up for Responsible
AI development compared to similarly sized models.
Using the benchmark evaluation metrics described in this document, these models
have shown to provide superior performance to other, comparably-sized open model
alternatives.
<!-- original-model-card end --> | {"license": "gemma", "library_name": "transformers", "tags": ["GGUF"], "extra_gated_heading": "Access Gemma on Hugging Face", "extra_gated_prompt": "To access Gemma on Hugging Face, you\u2019re required to review and agree to Google\u2019s usage license. To do this, please ensure you\u2019re logged-in to Hugging Face and click below. Requests are processed immediately.", "extra_gated_button_content": "Acknowledge license", "quantized_by": "andrijdavid"} | LiteLLMs/gemma-7b-GGUF | null | [
"transformers",
"gguf",
"GGUF",
"arxiv:2305.14314",
"arxiv:2312.11805",
"license:gemma",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T12:56:31+00:00 | [
"2305.14314",
"2312.11805"
] | [] | TAGS
#transformers #gguf #GGUF #arxiv-2305.14314 #arxiv-2312.11805 #license-gemma #endpoints_compatible #region-us
| # gemma-7b-GGUF
- Original model: gemma-7b
## Description
This repo contains GGUF format model files for gemma-7b.
### About GGUF
GGUF is a new format introduced by the URL team on August 21st 2023. It is a replacement for GGML, which is no longer supported by URL.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* URL. This is the source project for GGUF, providing both a Command Line Interface (CLI) and a server option.
* text-generation-webui, Known as the most widely used web UI, this project boasts numerous features and powerful extensions, and supports GPU acceleration.
* Ollama Ollama is a lightweight and extensible framework designed for building and running language models locally. It features a simple API for creating, managing, and executing models, along with a library of pre-built models for use in various applicationsโ
* KoboldCpp, A comprehensive web UI offering GPU acceleration across all platforms and architectures, particularly renowned for storytelling.
* GPT4All, This is a free and open source GUI that runs locally, supporting Windows, Linux, and macOS with full GPU acceleration.
* LM Studio An intuitive and powerful local GUI for Windows and macOS (Silicon), featuring GPU acceleration.
* LoLLMS Web UI. A notable web UI with a variety of unique features, including a comprehensive model library for easy model selection.
* URL, An attractive, user-friendly character-based chat GUI for Windows and macOS (both Silicon and Intel), also offering GPU acceleration.
* llama-cpp-python, A Python library equipped with GPU acceleration, LangChain support, and an OpenAI-compatible API server.
* candle, A Rust-based ML framework focusing on performance, including GPU support, and designed for ease of use.
* ctransformers, A Python library featuring GPU acceleration, LangChain support, and an OpenAI-compatible AI server.
* localGPT An open-source initiative enabling private conversations with documents.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw.
</details>
## How to download GGUF files
Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* URL
### In 'text-generation-webui'
Under Download Model, you can enter the model repo: LiteLLMs/gemma-7b-GGUF and below it, a specific filename to download, such as: Q4_0/Q4_0-URL.
Then click Download.
### On the command line, including multiple files at once
I recommend using the 'huggingface-hub' Python library:
Then you can download any individual model file to the current directory, at high speed, with a command like this:
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
For more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI.
To accelerate downloads on fast connections (1Gbit/s or higher), install 'hf_transfer':
And set environment variable 'HF_HUB_ENABLE_HF_TRANSFER' to '1':
Windows Command Line users: You can set the environment variable by running 'set HF_HUB_ENABLE_HF_TRANSFER=1' before the download command.
</details>
## Example 'URL' command
Make sure you are using 'URL' from commit d0cee0d or later.
Change '-ngl 32' to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change '-c 8192' to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by URL automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the '-p <PROMPT>' argument with '-i -ins'
For other parameters and how to use them, please refer to the URL documentation
## How to run in 'text-generation-webui'
Further instructions can be found in the text-generation-webui documentation, here: text-generation-webui/docs/04 โ Model URL.
## How to run from Python code
You can use GGUF models from Python using the llama-cpp-python or ctransformers libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: llama-cpp-python docs.
#### First install the package
Run one of the following commands, according to your system:
#### Simple llama-cpp-python example code
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* LangChain + llama-cpp-python
* LangChain + ctransformers
# Original model card: gemma-7b
# Gemma Model Card
Model Page: Gemma
This model card corresponds to the 7B base version of the Gemma model. You can also visit the model card of the 2B base model, 7B instruct model, and 2B instruct model.
Resources and Technical Documentation:
* Gemma Technical Report
* Responsible Generative AI Toolkit
* Gemma on Kaggle
* Gemma on Vertex Model Garden
Terms of Use: Terms
Authors: Google
## Model Information
Summary description and brief definition of inputs and outputs.
### Description
Gemma is a family of lightweight, state-of-the-art open models from Google,
built from the same research and technology used to create the Gemini models.
They are text-to-text, decoder-only large language models, available in English,
with open weights, pre-trained variants, and instruction-tuned variants. Gemma
models are well-suited for a variety of text generation tasks, including
question answering, summarization, and reasoning. Their relatively small size
makes it possible to deploy them in environments with limited resources such as
a laptop, desktop or your own cloud infrastructure, democratizing access to
state of the art AI models and helping foster innovation for everyone.
### Context Length
Models are trained on a context length of 8192 tokens.
### Usage
Below we share some code snippets on how to get quickly started with running the model. First make sure to 'pip install -U transformers', then copy the snippet from the section that is relevant for your usecase.
#### Fine-tuning examples
You can find fine-tuning notebooks under the 'examples/' directory. We provide:
* A script to perform Supervised Fine-Tuning (SFT) on UltraChat dataset using QLoRA
* A script to perform SFT using FSDP on TPU devices
* A notebook that you can run on a free-tier Google Colab instance to perform SFT on English quotes dataset. You can also find the copy of the notebook here.
#### Running the model on a CPU
#### Running the model on a single / multi GPU
#### Running the model on a GPU using different precisions
* _Using 'torch.float16'_
* _Using 'torch.bfloat16'_
#### Quantized Versions through 'bitsandbytes'
* _Using 8-bit precision (int8)_
* _Using 4-bit precision_
#### Other optimizations
* _Flash Attention 2_
First make sure to install 'flash-attn' in your environment 'pip install flash-attn'
### Inputs and outputs
* Input: Text string, such as a question, a prompt, or a document to be
summarized.
* Output: Generated English-language text in response to the input, such
as an answer to a question, or a summary of a document.
## Model Data
Data used for model training and how the data was processed.
### Training Dataset
These models were trained on a dataset of text data that includes a wide variety
of sources, totaling 6 trillion tokens. Here are the key components:
* Web Documents: A diverse collection of web text ensures the model is exposed
to a broad range of linguistic styles, topics, and vocabulary. Primarily
English-language content.
* Code: Exposing the model to code helps it to learn the syntax and patterns of
programming languages, which improves its ability to generate code or
understand code-related questions.
* Mathematics: Training on mathematical text helps the model learn logical
reasoning, symbolic representation, and to address mathematical queries.
The combination of these diverse data sources is crucial for training a powerful
language model that can handle a wide variety of different tasks and text
formats.
### Data Preprocessing
Here are the key data cleaning and filtering methods applied to the training
data:
* CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was
applied at multiple stages in the data preparation process to ensure the
exclusion of harmful and illegal content
* Sensitive Data Filtering: As part of making Gemma pre-trained models safe and
reliable, automated techniques were used to filter out certain personal
information and other sensitive data from training sets.
* Additional methods: Filtering based on content quality and safely in line with
our policies.
## Implementation Information
Details about the model internals.
### Hardware
Gemma was trained using the latest generation of
Tensor Processing Unit (TPU) hardware (TPUv5e).
Training large language models requires significant computational power. TPUs,
designed specifically for matrix operations common in machine learning, offer
several advantages in this domain:
* Performance: TPUs are specifically designed to handle the massive computations
involved in training LLMs. They can speed up training considerably compared to
CPUs.
* Memory: TPUs often come with large amounts of high-bandwidth memory, allowing
for the handling of large models and batch sizes during training. This can
lead to better model quality.
* Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for
handling the growing complexity of large foundation models. You can distribute
training across multiple TPU devices for faster and more efficient processing.
* Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective
solution for training large models compared to CPU-based infrastructure,
especially when considering the time and resources saved due to faster
training.
* These advantages are aligned with
Google's commitments to operate sustainably.
### Software
Training was done using JAX and ML Pathways.
JAX allows researchers to take advantage of the latest generation of hardware,
including TPUs, for faster and more efficient training of large models.
ML Pathways is Google's latest effort to build artificially intelligent systems
capable of generalizing across multiple tasks. This is specially suitable for
foundation models, including large language models like
these ones.
Together, JAX and ML Pathways are used as described in the
paper about the Gemini family of models; "the 'single
controller' programming model of Jax and Pathways allows a single Python
process to orchestrate the entire training run, dramatically simplifying the
development workflow."
## Evaluation
Model evaluation metrics and results.
### Benchmark Results
These models were evaluated against a large collection of different datasets and
metrics to cover different aspects of text generation:
| Benchmark | Metric | 2B Params | 7B Params |
| -- | -- | -- | -- | --- |
## Usage and Limitations
These models have certain limitations that users should be aware of.
### Intended Usage
Open Large Language Models (LLMs) have a wide range of applications across
various industries and domains. The following list of potential uses is not
comprehensive. The purpose of this list is to provide contextual information
about the possible use-cases that the model creators considered as part of model
training and development.
* Content Creation and Communication
* Text Generation: These models can be used to generate creative text formats
such as poems, scripts, code, marketing copy, and email drafts.
* Chatbots and Conversational AI: Power conversational interfaces for customer
service, virtual assistants, or interactive applications.
* Text Summarization: Generate concise summaries of a text corpus, research
papers, or reports.
* Research and Education
* Natural Language Processing (NLP) Research: These models can serve as a
foundation for researchers to experiment with NLP techniques, develop
algorithms, and contribute to the advancement of the field.
* Language Learning Tools: Support interactive language learning experiences,
aiding in grammar correction or providing writing practice.
* Knowledge Exploration: Assist researchers in exploring large bodies of text
by generating summaries or answering questions about specific topics.
### Limitations
* Training Data
* The quality and diversity of the training data significantly influence the
model's capabilities. Biases or gaps in the training data can lead to
limitations in the model's responses.
* The scope of the training dataset determines the subject areas the model can
handle effectively.
* Context and Task Complexity
* LLMs are better at tasks that can be framed with clear prompts and
instructions. Open-ended or highly complex tasks might be challenging.
* A model's performance can be influenced by the amount of context provided
(longer context generally leads to better outputs, up to a certain point).
* Language Ambiguity and Nuance
* Natural language is inherently complex. LLMs might struggle to grasp subtle
nuances, sarcasm, or figurative language.
* Factual Accuracy
* LLMs generate responses based on information they learned from their
training datasets, but they are not knowledge bases. They may generate
incorrect or outdated factual statements.
* Common Sense
* LLMs rely on statistical patterns in language. They might lack the ability
to apply common sense reasoning in certain situations.
### Ethical Considerations and Risks
The development of large language models (LLMs) raises several ethical concerns.
In creating an open model, we have carefully considered the following:
* Bias and Fairness
* LLMs trained on large-scale, real-world text data can reflect socio-cultural
biases embedded in the training material. These models underwent careful
scrutiny, input data pre-processing described and posterior evaluations
reported in this card.
* Misinformation and Misuse
* LLMs can be misused to generate text that is false, misleading, or harmful.
* Guidelines are provided for responsible use with the model, see the
Responsible Generative AI Toolkit.
* Transparency and Accountability:
* This model card summarizes details on the models' architecture,
capabilities, limitations, and evaluation processes.
* A responsibly developed open model offers the opportunity to share
innovation by making LLM technology accessible to developers and researchers
across the AI ecosystem.
Risks identified and mitigations:
* Perpetuation of biases: It's encouraged to perform continuous monitoring
(using evaluation metrics, human review) and the exploration of de-biasing
techniques during model training, fine-tuning, and other use cases.
* Generation of harmful content: Mechanisms and guidelines for content safety
are essential. Developers are encouraged to exercise caution and implement
appropriate content safety safeguards based on their specific product policies
and application use cases.
* Misuse for malicious purposes: Technical limitations and developer and
end-user education can help mitigate against malicious applications of LLMs.
Educational resources and reporting mechanisms for users to flag misuse are
provided. Prohibited uses of Gemma models are outlined in the
Gemma Prohibited Use Policy.
* Privacy violations: Models were trained on data filtered for removal of PII
(Personally Identifiable Information). Developers are encouraged to adhere to
privacy regulations with privacy-preserving techniques.
### Benefits
At the time of release, this family of models provides high-performance open
large language model implementations designed from the ground up for Responsible
AI development compared to similarly sized models.
Using the benchmark evaluation metrics described in this document, these models
have shown to provide superior performance to other, comparably-sized open model
alternatives.
| [
"# gemma-7b-GGUF\n- Original model: gemma-7b",
"## Description\n\nThis repo contains GGUF format model files for gemma-7b.",
"### About GGUF\nGGUF is a new format introduced by the URL team on August 21st 2023. It is a replacement for GGML, which is no longer supported by URL.\nHere is an incomplete list of clients and libraries that are known to support GGUF:\n* URL. This is the source project for GGUF, providing both a Command Line Interface (CLI) and a server option.\n* text-generation-webui, Known as the most widely used web UI, this project boasts numerous features and powerful extensions, and supports GPU acceleration.\n* Ollama Ollama is a lightweight and extensible framework designed for building and running language models locally. It features a simple API for creating, managing, and executing models, along with a library of pre-built models for use in various applicationsโ\n* KoboldCpp, A comprehensive web UI offering GPU acceleration across all platforms and architectures, particularly renowned for storytelling.\n* GPT4All, This is a free and open source GUI that runs locally, supporting Windows, Linux, and macOS with full GPU acceleration.\n* LM Studio An intuitive and powerful local GUI for Windows and macOS (Silicon), featuring GPU acceleration.\n* LoLLMS Web UI. A notable web UI with a variety of unique features, including a comprehensive model library for easy model selection.\n* URL, An attractive, user-friendly character-based chat GUI for Windows and macOS (both Silicon and Intel), also offering GPU acceleration.\n* llama-cpp-python, A Python library equipped with GPU acceleration, LangChain support, and an OpenAI-compatible API server.\n* candle, A Rust-based ML framework focusing on performance, including GPU support, and designed for ease of use.\n* ctransformers, A Python library featuring GPU acceleration, LangChain support, and an OpenAI-compatible AI server.\n* localGPT An open-source initiative enabling private conversations with documents.",
"## Explanation of quantisation methods\n<details>\n <summary>Click to see details</summary>\nThe new methods available are:\n\n* GGML_TYPE_Q2_K - \"type-1\" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)\n* GGML_TYPE_Q3_K - \"type-0\" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.\n* GGML_TYPE_Q4_K - \"type-1\" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.\n* GGML_TYPE_Q5_K - \"type-1\" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw\n* GGML_TYPE_Q6_K - \"type-0\" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw.\n</details>",
"## How to download GGUF files\n\nNote for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.\n\nThe following clients/libraries will automatically download models for you, providing a list of available models to choose from:\n\n* LM Studio\n* LoLLMS Web UI\n* URL",
"### In 'text-generation-webui'\n\nUnder Download Model, you can enter the model repo: LiteLLMs/gemma-7b-GGUF and below it, a specific filename to download, such as: Q4_0/Q4_0-URL.\n\nThen click Download.",
"### On the command line, including multiple files at once\n\nI recommend using the 'huggingface-hub' Python library:\n\n\n\nThen you can download any individual model file to the current directory, at high speed, with a command like this:\n\n\n\n<details>\n <summary>More advanced huggingface-cli download usage (click to read)</summary>\n\nYou can also download multiple files at once with a pattern:\n\n\n\nFor more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI.\n\nTo accelerate downloads on fast connections (1Gbit/s or higher), install 'hf_transfer':\n\n\n\nAnd set environment variable 'HF_HUB_ENABLE_HF_TRANSFER' to '1':\n\n\n\nWindows Command Line users: You can set the environment variable by running 'set HF_HUB_ENABLE_HF_TRANSFER=1' before the download command.\n</details>",
"## Example 'URL' command\n\nMake sure you are using 'URL' from commit d0cee0d or later.\n\n\n\nChange '-ngl 32' to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.\n\nChange '-c 8192' to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by URL automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.\n\nIf you want to have a chat-style conversation, replace the '-p <PROMPT>' argument with '-i -ins'\n\nFor other parameters and how to use them, please refer to the URL documentation",
"## How to run in 'text-generation-webui'\n\nFurther instructions can be found in the text-generation-webui documentation, here: text-generation-webui/docs/04 โ Model URL.",
"## How to run from Python code\n\nYou can use GGUF models from Python using the llama-cpp-python or ctransformers libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.",
"### How to load this model in Python code, using llama-cpp-python\n\nFor full documentation, please see: llama-cpp-python docs.",
"#### First install the package\n\nRun one of the following commands, according to your system:",
"#### Simple llama-cpp-python example code",
"## How to use with LangChain\n\nHere are guides on using llama-cpp-python and ctransformers with LangChain:\n\n* LangChain + llama-cpp-python\n* LangChain + ctransformers",
"# Original model card: gemma-7b",
"# Gemma Model Card\n\nModel Page: Gemma\n\nThis model card corresponds to the 7B base version of the Gemma model. You can also visit the model card of the 2B base model, 7B instruct model, and 2B instruct model.\n\nResources and Technical Documentation:\n\n* Gemma Technical Report\n* Responsible Generative AI Toolkit\n* Gemma on Kaggle\n* Gemma on Vertex Model Garden\n\nTerms of Use: Terms\n\nAuthors: Google",
"## Model Information\n\nSummary description and brief definition of inputs and outputs.",
"### Description\n\nGemma is a family of lightweight, state-of-the-art open models from Google,\nbuilt from the same research and technology used to create the Gemini models.\nThey are text-to-text, decoder-only large language models, available in English,\nwith open weights, pre-trained variants, and instruction-tuned variants. Gemma\nmodels are well-suited for a variety of text generation tasks, including\nquestion answering, summarization, and reasoning. Their relatively small size\nmakes it possible to deploy them in environments with limited resources such as\na laptop, desktop or your own cloud infrastructure, democratizing access to\nstate of the art AI models and helping foster innovation for everyone.",
"### Context Length\nModels are trained on a context length of 8192 tokens.",
"### Usage\n\nBelow we share some code snippets on how to get quickly started with running the model. First make sure to 'pip install -U transformers', then copy the snippet from the section that is relevant for your usecase.",
"#### Fine-tuning examples\n\nYou can find fine-tuning notebooks under the 'examples/' directory. We provide:\n\n* A script to perform Supervised Fine-Tuning (SFT) on UltraChat dataset using QLoRA\n* A script to perform SFT using FSDP on TPU devices\n* A notebook that you can run on a free-tier Google Colab instance to perform SFT on English quotes dataset. You can also find the copy of the notebook here.",
"#### Running the model on a CPU",
"#### Running the model on a single / multi GPU",
"#### Running the model on a GPU using different precisions\n\n* _Using 'torch.float16'_\n\n\n\n* _Using 'torch.bfloat16'_",
"#### Quantized Versions through 'bitsandbytes'\n\n* _Using 8-bit precision (int8)_\n\n\n\n* _Using 4-bit precision_",
"#### Other optimizations\n\n* _Flash Attention 2_\n\nFirst make sure to install 'flash-attn' in your environment 'pip install flash-attn'",
"### Inputs and outputs\n\n* Input: Text string, such as a question, a prompt, or a document to be\n summarized.\n* Output: Generated English-language text in response to the input, such\n as an answer to a question, or a summary of a document.",
"## Model Data\n\nData used for model training and how the data was processed.",
"### Training Dataset\n\nThese models were trained on a dataset of text data that includes a wide variety\nof sources, totaling 6 trillion tokens. Here are the key components:\n\n* Web Documents: A diverse collection of web text ensures the model is exposed\n to a broad range of linguistic styles, topics, and vocabulary. Primarily\n English-language content.\n* Code: Exposing the model to code helps it to learn the syntax and patterns of\n programming languages, which improves its ability to generate code or\n understand code-related questions.\n* Mathematics: Training on mathematical text helps the model learn logical\n reasoning, symbolic representation, and to address mathematical queries.\n\nThe combination of these diverse data sources is crucial for training a powerful\nlanguage model that can handle a wide variety of different tasks and text\nformats.",
"### Data Preprocessing\n\nHere are the key data cleaning and filtering methods applied to the training\ndata:\n\n* CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was\n applied at multiple stages in the data preparation process to ensure the\n exclusion of harmful and illegal content\n* Sensitive Data Filtering: As part of making Gemma pre-trained models safe and\n reliable, automated techniques were used to filter out certain personal\n information and other sensitive data from training sets.\n* Additional methods: Filtering based on content quality and safely in line with\n our policies.",
"## Implementation Information\n\nDetails about the model internals.",
"### Hardware\n\nGemma was trained using the latest generation of\nTensor Processing Unit (TPU) hardware (TPUv5e).\n\nTraining large language models requires significant computational power. TPUs,\ndesigned specifically for matrix operations common in machine learning, offer\nseveral advantages in this domain:\n\n* Performance: TPUs are specifically designed to handle the massive computations\n involved in training LLMs. They can speed up training considerably compared to\n CPUs.\n* Memory: TPUs often come with large amounts of high-bandwidth memory, allowing\n for the handling of large models and batch sizes during training. This can\n lead to better model quality.\n* Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for\n handling the growing complexity of large foundation models. You can distribute\n training across multiple TPU devices for faster and more efficient processing.\n* Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective\n solution for training large models compared to CPU-based infrastructure,\n especially when considering the time and resources saved due to faster\n training.\n* These advantages are aligned with\n Google's commitments to operate sustainably.",
"### Software\n\nTraining was done using JAX and ML Pathways.\n\nJAX allows researchers to take advantage of the latest generation of hardware,\nincluding TPUs, for faster and more efficient training of large models.\n\nML Pathways is Google's latest effort to build artificially intelligent systems\ncapable of generalizing across multiple tasks. This is specially suitable for\nfoundation models, including large language models like\nthese ones.\n\nTogether, JAX and ML Pathways are used as described in the\npaper about the Gemini family of models; \"the 'single\ncontroller' programming model of Jax and Pathways allows a single Python\nprocess to orchestrate the entire training run, dramatically simplifying the\ndevelopment workflow.\"",
"## Evaluation\n\nModel evaluation metrics and results.",
"### Benchmark Results\n\nThese models were evaluated against a large collection of different datasets and\nmetrics to cover different aspects of text generation:\n\n| Benchmark | Metric | 2B Params | 7B Params |\n| -- | -- | -- | -- | --- |",
"## Usage and Limitations\n\nThese models have certain limitations that users should be aware of.",
"### Intended Usage\n\nOpen Large Language Models (LLMs) have a wide range of applications across\nvarious industries and domains. The following list of potential uses is not\ncomprehensive. The purpose of this list is to provide contextual information\nabout the possible use-cases that the model creators considered as part of model\ntraining and development.\n\n* Content Creation and Communication\n * Text Generation: These models can be used to generate creative text formats\n such as poems, scripts, code, marketing copy, and email drafts.\n * Chatbots and Conversational AI: Power conversational interfaces for customer\n service, virtual assistants, or interactive applications.\n * Text Summarization: Generate concise summaries of a text corpus, research\n papers, or reports.\n* Research and Education\n * Natural Language Processing (NLP) Research: These models can serve as a\n foundation for researchers to experiment with NLP techniques, develop\n algorithms, and contribute to the advancement of the field.\n * Language Learning Tools: Support interactive language learning experiences,\n aiding in grammar correction or providing writing practice.\n * Knowledge Exploration: Assist researchers in exploring large bodies of text\n by generating summaries or answering questions about specific topics.",
"### Limitations\n\n* Training Data\n * The quality and diversity of the training data significantly influence the\n model's capabilities. Biases or gaps in the training data can lead to\n limitations in the model's responses.\n * The scope of the training dataset determines the subject areas the model can\n handle effectively.\n* Context and Task Complexity\n * LLMs are better at tasks that can be framed with clear prompts and\n instructions. Open-ended or highly complex tasks might be challenging.\n * A model's performance can be influenced by the amount of context provided\n (longer context generally leads to better outputs, up to a certain point).\n* Language Ambiguity and Nuance\n * Natural language is inherently complex. LLMs might struggle to grasp subtle\n nuances, sarcasm, or figurative language.\n* Factual Accuracy\n * LLMs generate responses based on information they learned from their\n training datasets, but they are not knowledge bases. They may generate\n incorrect or outdated factual statements.\n* Common Sense\n * LLMs rely on statistical patterns in language. They might lack the ability\n to apply common sense reasoning in certain situations.",
"### Ethical Considerations and Risks\n\nThe development of large language models (LLMs) raises several ethical concerns.\nIn creating an open model, we have carefully considered the following:\n\n* Bias and Fairness\n * LLMs trained on large-scale, real-world text data can reflect socio-cultural\n biases embedded in the training material. These models underwent careful\n scrutiny, input data pre-processing described and posterior evaluations\n reported in this card.\n* Misinformation and Misuse\n * LLMs can be misused to generate text that is false, misleading, or harmful.\n * Guidelines are provided for responsible use with the model, see the\n Responsible Generative AI Toolkit.\n* Transparency and Accountability:\n * This model card summarizes details on the models' architecture,\n capabilities, limitations, and evaluation processes.\n * A responsibly developed open model offers the opportunity to share\n innovation by making LLM technology accessible to developers and researchers\n across the AI ecosystem.\n\nRisks identified and mitigations:\n\n* Perpetuation of biases: It's encouraged to perform continuous monitoring\n (using evaluation metrics, human review) and the exploration of de-biasing\n techniques during model training, fine-tuning, and other use cases.\n* Generation of harmful content: Mechanisms and guidelines for content safety\n are essential. Developers are encouraged to exercise caution and implement\n appropriate content safety safeguards based on their specific product policies\n and application use cases.\n* Misuse for malicious purposes: Technical limitations and developer and\n end-user education can help mitigate against malicious applications of LLMs.\n Educational resources and reporting mechanisms for users to flag misuse are\n provided. Prohibited uses of Gemma models are outlined in the\n Gemma Prohibited Use Policy.\n* Privacy violations: Models were trained on data filtered for removal of PII\n (Personally Identifiable Information). Developers are encouraged to adhere to\n privacy regulations with privacy-preserving techniques.",
"### Benefits\n\nAt the time of release, this family of models provides high-performance open\nlarge language model implementations designed from the ground up for Responsible\nAI development compared to similarly sized models.\n\nUsing the benchmark evaluation metrics described in this document, these models\nhave shown to provide superior performance to other, comparably-sized open model\nalternatives."
] | [
"TAGS\n#transformers #gguf #GGUF #arxiv-2305.14314 #arxiv-2312.11805 #license-gemma #endpoints_compatible #region-us \n",
"# gemma-7b-GGUF\n- Original model: gemma-7b",
"## Description\n\nThis repo contains GGUF format model files for gemma-7b.",
"### About GGUF\nGGUF is a new format introduced by the URL team on August 21st 2023. It is a replacement for GGML, which is no longer supported by URL.\nHere is an incomplete list of clients and libraries that are known to support GGUF:\n* URL. This is the source project for GGUF, providing both a Command Line Interface (CLI) and a server option.\n* text-generation-webui, Known as the most widely used web UI, this project boasts numerous features and powerful extensions, and supports GPU acceleration.\n* Ollama Ollama is a lightweight and extensible framework designed for building and running language models locally. It features a simple API for creating, managing, and executing models, along with a library of pre-built models for use in various applicationsโ\n* KoboldCpp, A comprehensive web UI offering GPU acceleration across all platforms and architectures, particularly renowned for storytelling.\n* GPT4All, This is a free and open source GUI that runs locally, supporting Windows, Linux, and macOS with full GPU acceleration.\n* LM Studio An intuitive and powerful local GUI for Windows and macOS (Silicon), featuring GPU acceleration.\n* LoLLMS Web UI. A notable web UI with a variety of unique features, including a comprehensive model library for easy model selection.\n* URL, An attractive, user-friendly character-based chat GUI for Windows and macOS (both Silicon and Intel), also offering GPU acceleration.\n* llama-cpp-python, A Python library equipped with GPU acceleration, LangChain support, and an OpenAI-compatible API server.\n* candle, A Rust-based ML framework focusing on performance, including GPU support, and designed for ease of use.\n* ctransformers, A Python library featuring GPU acceleration, LangChain support, and an OpenAI-compatible AI server.\n* localGPT An open-source initiative enabling private conversations with documents.",
"## Explanation of quantisation methods\n<details>\n <summary>Click to see details</summary>\nThe new methods available are:\n\n* GGML_TYPE_Q2_K - \"type-1\" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)\n* GGML_TYPE_Q3_K - \"type-0\" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.\n* GGML_TYPE_Q4_K - \"type-1\" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.\n* GGML_TYPE_Q5_K - \"type-1\" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw\n* GGML_TYPE_Q6_K - \"type-0\" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw.\n</details>",
"## How to download GGUF files\n\nNote for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.\n\nThe following clients/libraries will automatically download models for you, providing a list of available models to choose from:\n\n* LM Studio\n* LoLLMS Web UI\n* URL",
"### In 'text-generation-webui'\n\nUnder Download Model, you can enter the model repo: LiteLLMs/gemma-7b-GGUF and below it, a specific filename to download, such as: Q4_0/Q4_0-URL.\n\nThen click Download.",
"### On the command line, including multiple files at once\n\nI recommend using the 'huggingface-hub' Python library:\n\n\n\nThen you can download any individual model file to the current directory, at high speed, with a command like this:\n\n\n\n<details>\n <summary>More advanced huggingface-cli download usage (click to read)</summary>\n\nYou can also download multiple files at once with a pattern:\n\n\n\nFor more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI.\n\nTo accelerate downloads on fast connections (1Gbit/s or higher), install 'hf_transfer':\n\n\n\nAnd set environment variable 'HF_HUB_ENABLE_HF_TRANSFER' to '1':\n\n\n\nWindows Command Line users: You can set the environment variable by running 'set HF_HUB_ENABLE_HF_TRANSFER=1' before the download command.\n</details>",
"## Example 'URL' command\n\nMake sure you are using 'URL' from commit d0cee0d or later.\n\n\n\nChange '-ngl 32' to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.\n\nChange '-c 8192' to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by URL automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.\n\nIf you want to have a chat-style conversation, replace the '-p <PROMPT>' argument with '-i -ins'\n\nFor other parameters and how to use them, please refer to the URL documentation",
"## How to run in 'text-generation-webui'\n\nFurther instructions can be found in the text-generation-webui documentation, here: text-generation-webui/docs/04 โ Model URL.",
"## How to run from Python code\n\nYou can use GGUF models from Python using the llama-cpp-python or ctransformers libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.",
"### How to load this model in Python code, using llama-cpp-python\n\nFor full documentation, please see: llama-cpp-python docs.",
"#### First install the package\n\nRun one of the following commands, according to your system:",
"#### Simple llama-cpp-python example code",
"## How to use with LangChain\n\nHere are guides on using llama-cpp-python and ctransformers with LangChain:\n\n* LangChain + llama-cpp-python\n* LangChain + ctransformers",
"# Original model card: gemma-7b",
"# Gemma Model Card\n\nModel Page: Gemma\n\nThis model card corresponds to the 7B base version of the Gemma model. You can also visit the model card of the 2B base model, 7B instruct model, and 2B instruct model.\n\nResources and Technical Documentation:\n\n* Gemma Technical Report\n* Responsible Generative AI Toolkit\n* Gemma on Kaggle\n* Gemma on Vertex Model Garden\n\nTerms of Use: Terms\n\nAuthors: Google",
"## Model Information\n\nSummary description and brief definition of inputs and outputs.",
"### Description\n\nGemma is a family of lightweight, state-of-the-art open models from Google,\nbuilt from the same research and technology used to create the Gemini models.\nThey are text-to-text, decoder-only large language models, available in English,\nwith open weights, pre-trained variants, and instruction-tuned variants. Gemma\nmodels are well-suited for a variety of text generation tasks, including\nquestion answering, summarization, and reasoning. Their relatively small size\nmakes it possible to deploy them in environments with limited resources such as\na laptop, desktop or your own cloud infrastructure, democratizing access to\nstate of the art AI models and helping foster innovation for everyone.",
"### Context Length\nModels are trained on a context length of 8192 tokens.",
"### Usage\n\nBelow we share some code snippets on how to get quickly started with running the model. First make sure to 'pip install -U transformers', then copy the snippet from the section that is relevant for your usecase.",
"#### Fine-tuning examples\n\nYou can find fine-tuning notebooks under the 'examples/' directory. We provide:\n\n* A script to perform Supervised Fine-Tuning (SFT) on UltraChat dataset using QLoRA\n* A script to perform SFT using FSDP on TPU devices\n* A notebook that you can run on a free-tier Google Colab instance to perform SFT on English quotes dataset. You can also find the copy of the notebook here.",
"#### Running the model on a CPU",
"#### Running the model on a single / multi GPU",
"#### Running the model on a GPU using different precisions\n\n* _Using 'torch.float16'_\n\n\n\n* _Using 'torch.bfloat16'_",
"#### Quantized Versions through 'bitsandbytes'\n\n* _Using 8-bit precision (int8)_\n\n\n\n* _Using 4-bit precision_",
"#### Other optimizations\n\n* _Flash Attention 2_\n\nFirst make sure to install 'flash-attn' in your environment 'pip install flash-attn'",
"### Inputs and outputs\n\n* Input: Text string, such as a question, a prompt, or a document to be\n summarized.\n* Output: Generated English-language text in response to the input, such\n as an answer to a question, or a summary of a document.",
"## Model Data\n\nData used for model training and how the data was processed.",
"### Training Dataset\n\nThese models were trained on a dataset of text data that includes a wide variety\nof sources, totaling 6 trillion tokens. Here are the key components:\n\n* Web Documents: A diverse collection of web text ensures the model is exposed\n to a broad range of linguistic styles, topics, and vocabulary. Primarily\n English-language content.\n* Code: Exposing the model to code helps it to learn the syntax and patterns of\n programming languages, which improves its ability to generate code or\n understand code-related questions.\n* Mathematics: Training on mathematical text helps the model learn logical\n reasoning, symbolic representation, and to address mathematical queries.\n\nThe combination of these diverse data sources is crucial for training a powerful\nlanguage model that can handle a wide variety of different tasks and text\nformats.",
"### Data Preprocessing\n\nHere are the key data cleaning and filtering methods applied to the training\ndata:\n\n* CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was\n applied at multiple stages in the data preparation process to ensure the\n exclusion of harmful and illegal content\n* Sensitive Data Filtering: As part of making Gemma pre-trained models safe and\n reliable, automated techniques were used to filter out certain personal\n information and other sensitive data from training sets.\n* Additional methods: Filtering based on content quality and safely in line with\n our policies.",
"## Implementation Information\n\nDetails about the model internals.",
"### Hardware\n\nGemma was trained using the latest generation of\nTensor Processing Unit (TPU) hardware (TPUv5e).\n\nTraining large language models requires significant computational power. TPUs,\ndesigned specifically for matrix operations common in machine learning, offer\nseveral advantages in this domain:\n\n* Performance: TPUs are specifically designed to handle the massive computations\n involved in training LLMs. They can speed up training considerably compared to\n CPUs.\n* Memory: TPUs often come with large amounts of high-bandwidth memory, allowing\n for the handling of large models and batch sizes during training. This can\n lead to better model quality.\n* Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for\n handling the growing complexity of large foundation models. You can distribute\n training across multiple TPU devices for faster and more efficient processing.\n* Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective\n solution for training large models compared to CPU-based infrastructure,\n especially when considering the time and resources saved due to faster\n training.\n* These advantages are aligned with\n Google's commitments to operate sustainably.",
"### Software\n\nTraining was done using JAX and ML Pathways.\n\nJAX allows researchers to take advantage of the latest generation of hardware,\nincluding TPUs, for faster and more efficient training of large models.\n\nML Pathways is Google's latest effort to build artificially intelligent systems\ncapable of generalizing across multiple tasks. This is specially suitable for\nfoundation models, including large language models like\nthese ones.\n\nTogether, JAX and ML Pathways are used as described in the\npaper about the Gemini family of models; \"the 'single\ncontroller' programming model of Jax and Pathways allows a single Python\nprocess to orchestrate the entire training run, dramatically simplifying the\ndevelopment workflow.\"",
"## Evaluation\n\nModel evaluation metrics and results.",
"### Benchmark Results\n\nThese models were evaluated against a large collection of different datasets and\nmetrics to cover different aspects of text generation:\n\n| Benchmark | Metric | 2B Params | 7B Params |\n| -- | -- | -- | -- | --- |",
"## Usage and Limitations\n\nThese models have certain limitations that users should be aware of.",
"### Intended Usage\n\nOpen Large Language Models (LLMs) have a wide range of applications across\nvarious industries and domains. The following list of potential uses is not\ncomprehensive. The purpose of this list is to provide contextual information\nabout the possible use-cases that the model creators considered as part of model\ntraining and development.\n\n* Content Creation and Communication\n * Text Generation: These models can be used to generate creative text formats\n such as poems, scripts, code, marketing copy, and email drafts.\n * Chatbots and Conversational AI: Power conversational interfaces for customer\n service, virtual assistants, or interactive applications.\n * Text Summarization: Generate concise summaries of a text corpus, research\n papers, or reports.\n* Research and Education\n * Natural Language Processing (NLP) Research: These models can serve as a\n foundation for researchers to experiment with NLP techniques, develop\n algorithms, and contribute to the advancement of the field.\n * Language Learning Tools: Support interactive language learning experiences,\n aiding in grammar correction or providing writing practice.\n * Knowledge Exploration: Assist researchers in exploring large bodies of text\n by generating summaries or answering questions about specific topics.",
"### Limitations\n\n* Training Data\n * The quality and diversity of the training data significantly influence the\n model's capabilities. Biases or gaps in the training data can lead to\n limitations in the model's responses.\n * The scope of the training dataset determines the subject areas the model can\n handle effectively.\n* Context and Task Complexity\n * LLMs are better at tasks that can be framed with clear prompts and\n instructions. Open-ended or highly complex tasks might be challenging.\n * A model's performance can be influenced by the amount of context provided\n (longer context generally leads to better outputs, up to a certain point).\n* Language Ambiguity and Nuance\n * Natural language is inherently complex. LLMs might struggle to grasp subtle\n nuances, sarcasm, or figurative language.\n* Factual Accuracy\n * LLMs generate responses based on information they learned from their\n training datasets, but they are not knowledge bases. They may generate\n incorrect or outdated factual statements.\n* Common Sense\n * LLMs rely on statistical patterns in language. They might lack the ability\n to apply common sense reasoning in certain situations.",
"### Ethical Considerations and Risks\n\nThe development of large language models (LLMs) raises several ethical concerns.\nIn creating an open model, we have carefully considered the following:\n\n* Bias and Fairness\n * LLMs trained on large-scale, real-world text data can reflect socio-cultural\n biases embedded in the training material. These models underwent careful\n scrutiny, input data pre-processing described and posterior evaluations\n reported in this card.\n* Misinformation and Misuse\n * LLMs can be misused to generate text that is false, misleading, or harmful.\n * Guidelines are provided for responsible use with the model, see the\n Responsible Generative AI Toolkit.\n* Transparency and Accountability:\n * This model card summarizes details on the models' architecture,\n capabilities, limitations, and evaluation processes.\n * A responsibly developed open model offers the opportunity to share\n innovation by making LLM technology accessible to developers and researchers\n across the AI ecosystem.\n\nRisks identified and mitigations:\n\n* Perpetuation of biases: It's encouraged to perform continuous monitoring\n (using evaluation metrics, human review) and the exploration of de-biasing\n techniques during model training, fine-tuning, and other use cases.\n* Generation of harmful content: Mechanisms and guidelines for content safety\n are essential. Developers are encouraged to exercise caution and implement\n appropriate content safety safeguards based on their specific product policies\n and application use cases.\n* Misuse for malicious purposes: Technical limitations and developer and\n end-user education can help mitigate against malicious applications of LLMs.\n Educational resources and reporting mechanisms for users to flag misuse are\n provided. Prohibited uses of Gemma models are outlined in the\n Gemma Prohibited Use Policy.\n* Privacy violations: Models were trained on data filtered for removal of PII\n (Personally Identifiable Information). Developers are encouraged to adhere to\n privacy regulations with privacy-preserving techniques.",
"### Benefits\n\nAt the time of release, this family of models provides high-performance open\nlarge language model implementations designed from the ground up for Responsible\nAI development compared to similarly sized models.\n\nUsing the benchmark evaluation metrics described in this document, these models\nhave shown to provide superior performance to other, comparably-sized open model\nalternatives."
] |
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. -->
# GPT2-705M
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.4628
## 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: 0.00025
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 9.7135 | 0.57 | 1 | 9.7272 |
| 8.0222 | 1.71 | 3 | 9.3213 |
| 7.6063 | 2.86 | 5 | 8.5841 |
| 7.5596 | 4.0 | 7 | 7.9271 |
| 7.4194 | 4.57 | 8 | 8.0942 |
| 7.1644 | 5.71 | 10 | 7.5409 |
| 6.8531 | 6.86 | 12 | 7.3028 |
| 6.3614 | 8.0 | 14 | 9.3796 |
| 8.5129 | 8.57 | 15 | 7.6361 |
| 6.1325 | 9.71 | 17 | 6.7577 |
| 5.8526 | 10.86 | 19 | 6.5249 |
| 5.5941 | 12.0 | 21 | 6.2490 |
| 5.4307 | 12.57 | 22 | 6.2442 |
| 5.1381 | 13.71 | 24 | 5.9595 |
| 4.8705 | 14.86 | 26 | 5.8944 |
| 4.7083 | 16.0 | 28 | 5.7005 |
| 4.5355 | 16.57 | 29 | 5.7459 |
| 4.4187 | 17.71 | 31 | 5.5387 |
| 4.3123 | 18.86 | 33 | 5.4863 |
| 4.0269 | 20.0 | 35 | 5.3277 |
| 3.942 | 20.57 | 36 | 5.3274 |
| 3.784 | 21.71 | 38 | 5.3998 |
| 3.4991 | 22.86 | 40 | 5.4628 |
### Framework versions
- Transformers 4.39.1
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
| {"tags": ["generated_from_trainer"], "model-index": [{"name": "GPT2-705M", "results": []}]} | ninagroot/GPT2-705M-RUN1 | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-17T12:58:20+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| GPT2-705M
=========
This model is a fine-tuned version of [](URL on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 5.4628
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: 0.00025
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 50
* num\_epochs: 40
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.39.1
* Pytorch 2.1.2+cu121
* Datasets 2.16.1
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.00025\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 50\n* num\\_epochs: 40\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.1\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.00025\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 50\n* num\\_epochs: 40\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.1\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.0"
] |
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. -->
# model_usp4_dpo1
This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3096
- Rewards/chosen: -11.2358
- Rewards/rejected: -13.1040
- Rewards/accuracies: 0.5700
- Rewards/margins: 1.8682
- Logps/rejected: -241.2410
- Logps/chosen: -223.0633
- Logits/rejected: -1.1809
- Logits/chosen: -1.2295
## 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: 4
- eval_batch_size: 1
- 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: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.098 | 2.67 | 100 | 0.9024 | -5.2759 | -6.1963 | 0.6200 | 0.9203 | -172.1640 | -163.4645 | -1.3943 | -1.4049 |
| 0.014 | 5.33 | 200 | 1.0817 | -5.6325 | -6.8881 | 0.5900 | 1.2556 | -179.0825 | -167.0302 | -1.3979 | -1.4111 |
| 0.0002 | 8.0 | 300 | 1.2922 | -11.0538 | -12.8527 | 0.5700 | 1.7989 | -238.7282 | -221.2436 | -1.1960 | -1.2430 |
| 0.0001 | 10.67 | 400 | 1.2957 | -11.1287 | -12.9674 | 0.5700 | 1.8388 | -239.8755 | -221.9918 | -1.1895 | -1.2369 |
| 0.0001 | 13.33 | 500 | 1.3067 | -11.1696 | -13.0195 | 0.5700 | 1.8499 | -240.3959 | -222.4008 | -1.1866 | -1.2350 |
| 0.0001 | 16.0 | 600 | 1.3094 | -11.2106 | -13.0741 | 0.5700 | 1.8635 | -240.9421 | -222.8107 | -1.1833 | -1.2314 |
| 0.0001 | 18.67 | 700 | 1.3114 | -11.2339 | -13.0993 | 0.5700 | 1.8654 | -241.1942 | -223.0445 | -1.1811 | -1.2298 |
| 0.0001 | 21.33 | 800 | 1.3091 | -11.2358 | -13.1096 | 0.5700 | 1.8738 | -241.2972 | -223.0631 | -1.1808 | -1.2294 |
| 0.0001 | 24.0 | 900 | 1.3126 | -11.2442 | -13.1117 | 0.5700 | 1.8676 | -241.3186 | -223.1469 | -1.1810 | -1.2294 |
| 0.0001 | 26.67 | 1000 | 1.3096 | -11.2358 | -13.1040 | 0.5700 | 1.8682 | -241.2410 | -223.0633 | -1.1809 | -1.2295 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 | {"license": "llama2", "library_name": "peft", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "meta-llama/Llama-2-7b-chat-hf", "model-index": [{"name": "model_usp4_dpo1", "results": []}]} | guoyu-zhang/model_usp4_dpo1 | null | [
"peft",
"safetensors",
"trl",
"dpo",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"license:llama2",
"region:us"
] | null | 2024-04-17T12:58:22+00:00 | [] | [] | TAGS
#peft #safetensors #trl #dpo #generated_from_trainer #base_model-meta-llama/Llama-2-7b-chat-hf #license-llama2 #region-us
| model\_usp4\_dpo1
=================
This model is a fine-tuned version of meta-llama/Llama-2-7b-chat-hf on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3096
* Rewards/chosen: -11.2358
* Rewards/rejected: -13.1040
* Rewards/accuracies: 0.5700
* Rewards/margins: 1.8682
* Logps/rejected: -241.2410
* Logps/chosen: -223.0633
* Logits/rejected: -1.1809
* Logits/chosen: -1.2295
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: 4
* eval\_batch\_size: 1
* 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: cosine
* lr\_scheduler\_warmup\_steps: 100
* training\_steps: 1000
### Training results
### Framework versions
* PEFT 0.10.0
* 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: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\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: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 1000",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.39.3\n* Pytorch 2.2.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#peft #safetensors #trl #dpo #generated_from_trainer #base_model-meta-llama/Llama-2-7b-chat-hf #license-llama2 #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\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: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 1000",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.39.3\n* Pytorch 2.2.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
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 ๐ค 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": []} | OwOOwO/dumbo-krillin45 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-17T12:59:45+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-generation | transformers | # merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [Citaman/command-r-5-layer](https://huggingface.co/Citaman/command-r-5-layer)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: Citaman/command-r-5-layer
layer_range: [0, 4]
- model: Citaman/command-r-5-layer
layer_range: [1, 5]
merge_method: slerp
base_model: Citaman/command-r-5-layer
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
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Citaman/command-r-5-layer"]} | Citaman/command-r-4-layer | null | [
"transformers",
"safetensors",
"cohere",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:Citaman/command-r-5-layer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-17T13:00:58+00:00 | [] | [] | TAGS
#transformers #safetensors #cohere #text-generation #mergekit #merge #conversational #base_model-Citaman/command-r-5-layer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # merge
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* Citaman/command-r-5-layer
### Configuration
The following YAML configuration was used to produce this model:
| [
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* Citaman/command-r-5-layer",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #safetensors #cohere #text-generation #mergekit #merge #conversational #base_model-Citaman/command-r-5-layer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* Citaman/command-r-5-layer",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] |
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 ๐ค 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": []} | sin66x/demo-sp2text-fa | null | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T13:01:05+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:",
"#### 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 #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 | transformers | ## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-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/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-IQ1_S.gguf) | i1-IQ1_S | 29.7 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-IQ1_M.gguf) | i1-IQ1_M | 32.8 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 38.0 | |
| [GGUF](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-IQ2_XS.gguf) | i1-IQ2_XS | 42.1 | |
| [GGUF](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-IQ2_S.gguf) | i1-IQ2_S | 42.7 | |
| [GGUF](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-IQ2_M.gguf) | i1-IQ2_M | 46.8 | |
| [PART 1](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-Q2_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-Q2_K.gguf.part2of2) | i1-Q2_K | 52.2 | IQ3_XXS probably better |
| [PART 1](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-IQ3_XXS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-IQ3_XXS.gguf.part2of2) | i1-IQ3_XXS | 55.0 | lower quality |
| [PART 1](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-IQ3_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-IQ3_XS.gguf.part2of2) | i1-IQ3_XS | 58.3 | |
| [PART 1](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-IQ3_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-IQ3_S.gguf.part2of2) | i1-IQ3_S | 61.6 | beats Q3_K* |
| [PART 1](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-Q3_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-Q3_K_S.gguf.part2of2) | i1-Q3_K_S | 61.6 | IQ3_XS probably better |
| [PART 1](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-IQ3_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-IQ3_M.gguf.part2of2) | i1-IQ3_M | 64.6 | |
| [PART 1](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-Q3_K_M.gguf.part2of2) | i1-Q3_K_M | 67.9 | IQ3_S probably better |
| [PART 1](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-Q3_K_L.gguf.part2of2) | i1-Q3_K_L | 72.7 | IQ3_M probably better |
| [PART 1](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-IQ4_XS.gguf.part2of2) | i1-IQ4_XS | 75.6 | |
| [PART 1](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-Q4_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-Q4_0.gguf.part2of2) | i1-Q4_0 | 80.0 | fast, low quality |
| [PART 1](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-Q4_K_S.gguf.part2of2) | i1-Q4_K_S | 80.6 | optimal size/speed/quality |
| [PART 1](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-Q4_K_M.gguf.part2of2) | i1-Q4_K_M | 85.7 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-Q5_K_S.gguf.part2of2) | i1-Q5_K_S | 97.1 | |
| [PART 1](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-Q5_K_M.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-Q5_K_M.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-Q5_K_M.gguf.part3of3) | i1-Q5_K_M | 100.1 | |
| [PART 1](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-Q6_K.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-Q6_K.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF/resolve/main/zephyr-orpo-141b-A35b-v0.1.i1-Q6_K.gguf.part3of3) | i1-Q6_K | 115.6 | 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", "tags": ["trl", "orpo", "generated_from_trainer"], "datasets": ["argilla/distilabel-capybara-dpo-7k-binarized"], "base_model": "HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1", "quantized_by": "mradermacher"} | mradermacher/zephyr-orpo-141b-A35b-v0.1-i1-GGUF | null | [
"transformers",
"gguf",
"trl",
"orpo",
"generated_from_trainer",
"en",
"dataset:argilla/distilabel-capybara-dpo-7k-binarized",
"base_model:HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T13:02:00+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #trl #orpo #generated_from_trainer #en #dataset-argilla/distilabel-capybara-dpo-7k-binarized #base_model-HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1 #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 #trl #orpo #generated_from_trainer #en #dataset-argilla/distilabel-capybara-dpo-7k-binarized #base_model-HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-Q6_K-GGUF
This model was converted to GGUF format from [`rhaymison/Llama-portuguese-13b-Luana-v0.2`](https://huggingface.co/rhaymison/Llama-portuguese-13b-Luana-v0.2) 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/rhaymison/Llama-portuguese-13b-Luana-v0.2) 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 NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-Q6_K-GGUF --model llama-portuguese-13b-luana-v0.2.Q6_K.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-Q6_K-GGUF --model llama-portuguese-13b-luana-v0.2.Q6_K.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 llama-portuguese-13b-luana-v0.2.Q6_K.gguf -n 128
```
| {"language": ["pt"], "license": "apache-2.0", "library_name": "transformers", "tags": ["Misral", "Portuguese", "7b", "llama-cpp", "gguf-my-repo"], "datasets": ["pablo-moreira/gpt4all-j-prompt-generations-pt", "rhaymison/superset"], "base_model": "meta-llama/Llama-2-13b-chat-hf", "pipeline_tag": "text-generation", "model-index": [{"name": "Llama-portuguese-13b-Luana-v0.2", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "ENEM Challenge (No Images)", "type": "eduagarcia/enem_challenge", "split": "train", "args": {"num_few_shot": 3}}, "metrics": [{"type": "acc", "value": 36.95, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama-portuguese-13b-Luana-v0.2", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "BLUEX (No Images)", "type": "eduagarcia-temp/BLUEX_without_images", "split": "train", "args": {"num_few_shot": 3}}, "metrics": [{"type": "acc", "value": 32.68, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama-portuguese-13b-Luana-v0.2", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "OAB Exams", "type": "eduagarcia/oab_exams", "split": "train", "args": {"num_few_shot": 3}}, "metrics": [{"type": "acc", "value": 33.3, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama-portuguese-13b-Luana-v0.2", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "Assin2 RTE", "type": "assin2", "split": "test", "args": {"num_few_shot": 15}}, "metrics": [{"type": "f1_macro", "value": 65.83, "name": "f1-macro"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama-portuguese-13b-Luana-v0.2", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "Assin2 STS", "type": "eduagarcia/portuguese_benchmark", "split": "test", "args": {"num_few_shot": 15}}, "metrics": [{"type": "pearson", "value": 42.81, "name": "pearson"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama-portuguese-13b-Luana-v0.2", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "FaQuAD NLI", "type": "ruanchaves/faquad-nli", "split": "test", "args": {"num_few_shot": 15}}, "metrics": [{"type": "f1_macro", "value": 40.44, "name": "f1-macro"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama-portuguese-13b-Luana-v0.2", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "HateBR Binary", "type": "ruanchaves/hatebr", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "f1_macro", "value": 83.62, "name": "f1-macro"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama-portuguese-13b-Luana-v0.2", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "PT Hate Speech Binary", "type": "hate_speech_portuguese", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "f1_macro", "value": 54.62, "name": "f1-macro"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama-portuguese-13b-Luana-v0.2", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "tweetSentBR", "type": "eduagarcia-temp/tweetsentbr", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "f1_macro", "value": 49.25, "name": "f1-macro"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama-portuguese-13b-Luana-v0.2", "name": "Open Portuguese LLM Leaderboard"}}]}]} | NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-GGUF | null | [
"transformers",
"gguf",
"Misral",
"Portuguese",
"7b",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"pt",
"dataset:pablo-moreira/gpt4all-j-prompt-generations-pt",
"dataset:rhaymison/superset",
"base_model:meta-llama/Llama-2-13b-chat-hf",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T13:02:06+00:00 | [] | [
"pt"
] | TAGS
#transformers #gguf #Misral #Portuguese #7b #llama-cpp #gguf-my-repo #text-generation #pt #dataset-pablo-moreira/gpt4all-j-prompt-generations-pt #dataset-rhaymison/superset #base_model-meta-llama/Llama-2-13b-chat-hf #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-Q6_K-GGUF
This model was converted to GGUF format from 'rhaymison/Llama-portuguese-13b-Luana-v0.2' 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.
| [
"# NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-Q6_K-GGUF\nThis model was converted to GGUF format from 'rhaymison/Llama-portuguese-13b-Luana-v0.2' 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 #Misral #Portuguese #7b #llama-cpp #gguf-my-repo #text-generation #pt #dataset-pablo-moreira/gpt4all-j-prompt-generations-pt #dataset-rhaymison/superset #base_model-meta-llama/Llama-2-13b-chat-hf #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# NikolayKozloff/Llama-portuguese-13b-Luana-v0.2-Q6_K-GGUF\nThis model was converted to GGUF format from 'rhaymison/Llama-portuguese-13b-Luana-v0.2' 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-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. -->
# multilingual-e5-large-guardrail-task-classifier-training_1000k
This model is a fine-tuned version of [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) 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-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "intfloat/multilingual-e5-large", "model-index": [{"name": "multilingual-e5-large-guardrail-task-classifier-training_1000k", "results": []}]} | tosh97/multilingual-e5-large-guardrail-task-classifier-training_1000k | null | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:intfloat/multilingual-e5-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T13:02:12+00:00 | [] | [] | TAGS
#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-intfloat/multilingual-e5-large #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# multilingual-e5-large-guardrail-task-classifier-training_1000k
This model is a fine-tuned version of intfloat/multilingual-e5-large 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-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
| [
"# multilingual-e5-large-guardrail-task-classifier-training_1000k\n\nThis model is a fine-tuned version of intfloat/multilingual-e5-large 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-06\n- train_batch_size: 16\n- eval_batch_size: 16\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",
"### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-intfloat/multilingual-e5-large #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# multilingual-e5-large-guardrail-task-classifier-training_1000k\n\nThis model is a fine-tuned version of intfloat/multilingual-e5-large 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-06\n- train_batch_size: 16\n- eval_batch_size: 16\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",
"### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.2"
] |
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. -->
# roberta-base-guardrail-legal-advice-classifier-training
This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6821
- F1: 0.4123
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 29 | 0.6843 | 0.4123 |
| No log | 2.0 | 58 | 0.6841 | 0.4123 |
| No log | 3.0 | 87 | 0.6837 | 0.4123 |
| 0.6992 | 4.0 | 116 | 0.6838 | 0.4123 |
| 0.6992 | 5.0 | 145 | 0.6831 | 0.4123 |
| 0.6992 | 6.0 | 174 | 0.6831 | 0.4123 |
| 0.694 | 7.0 | 203 | 0.6826 | 0.4123 |
| 0.694 | 8.0 | 232 | 0.6826 | 0.4123 |
| 0.694 | 9.0 | 261 | 0.6822 | 0.4123 |
| 0.694 | 10.0 | 290 | 0.6821 | 0.4123 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "base_model": "FacebookAI/roberta-base", "model-index": [{"name": "roberta-base-guardrail-legal-advice-classifier-training", "results": []}]} | tosh97/roberta-base-guardrail-legal-advice-classifier-training | null | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T13:03:27+00:00 | [] | [] | TAGS
#transformers #safetensors #roberta #text-classification #generated_from_trainer #base_model-FacebookAI/roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-guardrail-legal-advice-classifier-training
=======================================================
This model is a fine-tuned version of FacebookAI/roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6821
* F1: 0.4123
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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
### Training results
### Framework versions
* Transformers 4.39.3
* Pytorch 2.1.2+cu121
* Datasets 2.17.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\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",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.1.2+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #safetensors #roberta #text-classification #generated_from_trainer #base_model-FacebookAI/roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\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",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.1.2+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
] |
text-generation | transformers | # merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [Citaman/command-r-4-layer](https://huggingface.co/Citaman/command-r-4-layer)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: Citaman/command-r-4-layer
layer_range: [0, 3]
- model: Citaman/command-r-4-layer
layer_range: [1, 4]
merge_method: slerp
base_model: Citaman/command-r-4-layer
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
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Citaman/command-r-4-layer"]} | Citaman/command-r-3-layer | null | [
"transformers",
"safetensors",
"cohere",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:Citaman/command-r-4-layer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-17T13:04:22+00:00 | [] | [] | TAGS
#transformers #safetensors #cohere #text-generation #mergekit #merge #conversational #base_model-Citaman/command-r-4-layer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # merge
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* Citaman/command-r-4-layer
### Configuration
The following YAML configuration was used to produce this model:
| [
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* Citaman/command-r-4-layer",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #safetensors #cohere #text-generation #mergekit #merge #conversational #base_model-Citaman/command-r-4-layer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* Citaman/command-r-4-layer",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] |
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. -->
# CS505-Dev-CSI-PhoBERT_base_h3
This model is a fine-tuned version of [vinai/phobert-base](https://huggingface.co/vinai/phobert-base) 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"tags": ["generated_from_trainer"], "base_model": "vinai/phobert-base", "model-index": [{"name": "CS505-Dev-CSI-PhoBERT_base_h3", "results": []}]} | ThuyNT/CS505-Dev-CSI-PhoBERT_base_h3 | null | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:vinai/phobert-base",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T13:05:55+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-vinai/phobert-base #autotrain_compatible #endpoints_compatible #region-us
|
# CS505-Dev-CSI-PhoBERT_base_h3
This model is a fine-tuned version of vinai/phobert-base 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| [
"# CS505-Dev-CSI-PhoBERT_base_h3\n\nThis model is a fine-tuned version of vinai/phobert-base 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: 2e-05\n- train_batch_size: 32\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20",
"### 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 #roberta #text-classification #generated_from_trainer #base_model-vinai/phobert-base #autotrain_compatible #endpoints_compatible #region-us \n",
"# CS505-Dev-CSI-PhoBERT_base_h3\n\nThis model is a fine-tuned version of vinai/phobert-base 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: 2e-05\n- train_batch_size: 32\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20",
"### 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"
] |
text-generation | transformers |
# NikolayKozloff/Qwen-portuguese-luana-7b-Q8_0-GGUF
This model was converted to GGUF format from [`rhaymison/Qwen-portuguese-luana-7b`](https://huggingface.co/rhaymison/Qwen-portuguese-luana-7b) 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/rhaymison/Qwen-portuguese-luana-7b) 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 NikolayKozloff/Qwen-portuguese-luana-7b-Q8_0-GGUF --model qwen-portuguese-luana-7b.Q8_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo NikolayKozloff/Qwen-portuguese-luana-7b-Q8_0-GGUF --model qwen-portuguese-luana-7b.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 qwen-portuguese-luana-7b.Q8_0.gguf -n 128
```
| {"language": ["pt"], "license": "apache-2.0", "library_name": "transformers", "tags": ["Misral", "Portuguese", "7b", "chat", "portugues", "llama-cpp", "gguf-my-repo"], "datasets": ["rhaymison/superset"], "base_model": "Qwen/Qwen1.5-7B", "pipeline_tag": "text-generation", "model-index": [{"name": "Qwen-portuguese-luana-7b", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "ENEM Challenge (No Images)", "type": "eduagarcia/enem_challenge", "split": "train", "args": {"num_few_shot": 3}}, "metrics": [{"type": "acc", "value": 58.36, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Qwen-portuguese-luana-7b", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "BLUEX (No Images)", "type": "eduagarcia-temp/BLUEX_without_images", "split": "train", "args": {"num_few_shot": 3}}, "metrics": [{"type": "acc", "value": 48.12, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Qwen-portuguese-luana-7b", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "OAB Exams", "type": "eduagarcia/oab_exams", "split": "train", "args": {"num_few_shot": 3}}, "metrics": [{"type": "acc", "value": 42.73, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Qwen-portuguese-luana-7b", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "Assin2 RTE", "type": "assin2", "split": "test", "args": {"num_few_shot": 15}}, "metrics": [{"type": "f1_macro", "value": 81.05, "name": "f1-macro"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Qwen-portuguese-luana-7b", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "Assin2 STS", "type": "eduagarcia/portuguese_benchmark", "split": "test", "args": {"num_few_shot": 15}}, "metrics": [{"type": "pearson", "value": 74.25, "name": "pearson"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Qwen-portuguese-luana-7b", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "FaQuAD NLI", "type": "ruanchaves/faquad-nli", "split": "test", "args": {"num_few_shot": 15}}, "metrics": [{"type": "f1_macro", "value": 57.96, "name": "f1-macro"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Qwen-portuguese-luana-7b", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "HateBR Binary", "type": "ruanchaves/hatebr", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "f1_macro", "value": 70.29, "name": "f1-macro"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Qwen-portuguese-luana-7b", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "PT Hate Speech Binary", "type": "hate_speech_portuguese", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "f1_macro", "value": 69.92, "name": "f1-macro"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Qwen-portuguese-luana-7b", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "tweetSentBR", "type": "eduagarcia/tweetsentbr_fewshot", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "f1_macro", "value": 59.69, "name": "f1-macro"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Qwen-portuguese-luana-7b", "name": "Open Portuguese LLM Leaderboard"}}]}]} | NikolayKozloff/Qwen-portuguese-luana-7b-GGUF | null | [
"transformers",
"gguf",
"Misral",
"Portuguese",
"7b",
"chat",
"portugues",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"pt",
"dataset:rhaymison/superset",
"base_model:Qwen/Qwen1.5-7B",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T13:07:04+00:00 | [] | [
"pt"
] | TAGS
#transformers #gguf #Misral #Portuguese #7b #chat #portugues #llama-cpp #gguf-my-repo #text-generation #pt #dataset-rhaymison/superset #base_model-Qwen/Qwen1.5-7B #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# NikolayKozloff/Qwen-portuguese-luana-7b-Q8_0-GGUF
This model was converted to GGUF format from 'rhaymison/Qwen-portuguese-luana-7b' 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.
| [
"# NikolayKozloff/Qwen-portuguese-luana-7b-Q8_0-GGUF\nThis model was converted to GGUF format from 'rhaymison/Qwen-portuguese-luana-7b' 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 #Misral #Portuguese #7b #chat #portugues #llama-cpp #gguf-my-repo #text-generation #pt #dataset-rhaymison/superset #base_model-Qwen/Qwen1.5-7B #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# NikolayKozloff/Qwen-portuguese-luana-7b-Q8_0-GGUF\nThis model was converted to GGUF format from 'rhaymison/Qwen-portuguese-luana-7b' 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 | # merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [Citaman/command-r-3-layer](https://huggingface.co/Citaman/command-r-3-layer)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: Citaman/command-r-3-layer
layer_range: [0, 2]
- model: Citaman/command-r-3-layer
layer_range: [1, 3]
merge_method: slerp
base_model: Citaman/command-r-3-layer
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
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Citaman/command-r-3-layer"]} | Citaman/command-r-2-layer | null | [
"transformers",
"safetensors",
"cohere",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:Citaman/command-r-3-layer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-17T13:07:44+00:00 | [] | [] | TAGS
#transformers #safetensors #cohere #text-generation #mergekit #merge #conversational #base_model-Citaman/command-r-3-layer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # merge
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* Citaman/command-r-3-layer
### Configuration
The following YAML configuration was used to produce this model:
| [
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* Citaman/command-r-3-layer",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #safetensors #cohere #text-generation #mergekit #merge #conversational #base_model-Citaman/command-r-3-layer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* Citaman/command-r-3-layer",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] |
sentence-similarity | sentence-transformers |
# HSR-HF/sts-rf-noscore
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('HSR-HF/sts-rf-noscore')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=HSR-HF/sts-rf-noscore)
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 664 with parameters:
```
{'batch_size': 8}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 8,
"evaluation_steps": 32,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-06
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 4254,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | {"library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | HSR-HF/sts-rf-noscore | null | [
"sentence-transformers",
"safetensors",
"roberta",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T13:07:51+00:00 | [] | [] | TAGS
#sentence-transformers #safetensors #roberta #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# HSR-HF/sts-rf-noscore
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL
## Training
The model was trained with the parameters:
DataLoader:
'sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader' of length 664 with parameters:
Loss:
'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:
Parameters of the fit()-Method:
## Full Model Architecture
## Citing & Authors
| [
"# HSR-HF/sts-rf-noscore\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader' of length 664 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] | [
"TAGS\n#sentence-transformers #safetensors #roberta #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# HSR-HF/sts-rf-noscore\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader' of length 664 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] |
null | transformers |
# Uploaded model
- **Developed by:** SirDamisola
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.1-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", "gguf"], "base_model": "unsloth/mistral-7b-instruct-v0.1-bnb-4bit"} | SirDamisola/lora_model_quantized-2 | null | [
"transformers",
"gguf",
"mistral",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.1-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T13:08:37+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #mistral #text-generation-inference #unsloth #en #base_model-unsloth/mistral-7b-instruct-v0.1-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: SirDamisola
- License: apache-2.0
- Finetuned from model : unsloth/mistral-7b-instruct-v0.1-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: SirDamisola\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-instruct-v0.1-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 #gguf #mistral #text-generation-inference #unsloth #en #base_model-unsloth/mistral-7b-instruct-v0.1-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n",
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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. -->
# imdb-spoiler-robertaOrigDataset
This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7375
- Accuracy: 0.708
- Recall: 0.664
- Precision: 0.7281
- F1: 0.6946
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.5274 | 0.12 | 500 | 0.6144 | 0.7051 | 0.62 | 0.7472 | 0.6777 |
| 0.5047 | 0.25 | 1000 | 0.6042 | 0.7023 | 0.683 | 0.7103 | 0.6964 |
| 0.4618 | 0.38 | 1500 | 0.5955 | 0.6913 | 0.6492 | 0.7088 | 0.6777 |
| 0.4495 | 0.5 | 2000 | 0.6901 | 0.6966 | 0.7365 | 0.6821 | 0.7083 |
| 0.5712 | 0.62 | 2500 | 0.5577 | 0.7069 | 0.822 | 0.6682 | 0.7371 |
| 0.5634 | 0.75 | 3000 | 0.5511 | 0.7212 | 0.696 | 0.7330 | 0.7140 |
| 0.5484 | 0.88 | 3500 | 0.5623 | 0.7054 | 0.5807 | 0.7736 | 0.6634 |
| 0.5496 | 1.0 | 4000 | 0.5459 | 0.7188 | 0.6268 | 0.7681 | 0.6903 |
| 0.488 | 1.12 | 4500 | 0.6082 | 0.7123 | 0.6315 | 0.7531 | 0.6870 |
| 0.5039 | 1.25 | 5000 | 0.5904 | 0.7171 | 0.744 | 0.7060 | 0.7245 |
| 0.4799 | 1.38 | 5500 | 0.6625 | 0.7045 | 0.5785 | 0.7734 | 0.6619 |
| 0.4855 | 1.5 | 6000 | 0.5842 | 0.7155 | 0.6757 | 0.7341 | 0.7037 |
| 0.4976 | 1.62 | 6500 | 0.5638 | 0.7188 | 0.6847 | 0.7347 | 0.7089 |
| 0.4856 | 1.75 | 7000 | 0.6056 | 0.713 | 0.6685 | 0.7338 | 0.6996 |
| 0.4724 | 1.88 | 7500 | 0.5861 | 0.7179 | 0.7348 | 0.7108 | 0.7226 |
| 0.4843 | 2.0 | 8000 | 0.5748 | 0.7186 | 0.7087 | 0.7230 | 0.7158 |
| 0.4001 | 2.12 | 8500 | 0.7215 | 0.7056 | 0.6172 | 0.7498 | 0.6771 |
| 0.4106 | 2.25 | 9000 | 0.7266 | 0.7056 | 0.6278 | 0.7436 | 0.6808 |
| 0.3972 | 2.38 | 9500 | 0.7102 | 0.7069 | 0.6697 | 0.7235 | 0.6956 |
| 0.3872 | 2.5 | 10000 | 0.7314 | 0.7094 | 0.6855 | 0.7199 | 0.7023 |
| 0.4042 | 2.62 | 10500 | 0.7285 | 0.7055 | 0.6422 | 0.7353 | 0.6856 |
| 0.3893 | 2.75 | 11000 | 0.7704 | 0.7114 | 0.685 | 0.7231 | 0.7036 |
| 0.4049 | 2.88 | 11500 | 0.7221 | 0.71 | 0.6923 | 0.7177 | 0.7048 |
| 0.3965 | 3.0 | 12000 | 0.7375 | 0.708 | 0.664 | 0.7281 | 0.6946 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "recall", "precision", "f1"], "base_model": "FacebookAI/roberta-base", "model-index": [{"name": "imdb-spoiler-robertaOrigDataset", "results": []}]} | Zritze/imdb-spoiler-robertaOrigDataset | null | [
"transformers",
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"roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T13:09:10+00:00 | [] | [] | TAGS
#transformers #safetensors #roberta #text-classification #generated_from_trainer #base_model-FacebookAI/roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
| imdb-spoiler-robertaOrigDataset
===============================
This model is a fine-tuned version of FacebookAI/roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7375
* Accuracy: 0.708
* Recall: 0.664
* Precision: 0.7281
* F1: 0.6946
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: 3
### Training results
### Framework versions
* Transformers 4.39.3
* Pytorch 2.2.2+cu121
* Datasets 2.18.0
* Tokenizers 0.15.2
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"### Training results",
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] |
token-classification | transformers |
## Model Specification
- Model: XLM-RoBERTa (base-sized model)
- Training Data:
- Combined Afrikaans, Hebrew, Bulgarian, Vietnamese, Norwegian, Urdu, Czech, & Persian corpora (Top 8 Languages)
- Training Details:
- Base configurations with a minor adjustment in learning rate (4.5e-5)
## Evaluation
- Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set)
- Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 78.29\% Accuracy)
## POS Tags
- ADJ โ ADP โ ADV โ CCONJ โ DET โ INTJ โ NOUN โ NUM โ PART โ PRON โ PROPN โ PUNCT โ SCONJ โ VERB | {"language": ["tl"], "datasets": ["universal_dependencies"], "metrics": ["f1"], "pipeline_tag": "token-classification"} | iceman2434/xlm-roberta-base-ft-udpos213-top8lang | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"tl",
"dataset:universal_dependencies",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T13:10:04+00:00 | [] | [
"tl"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #tl #dataset-universal_dependencies #autotrain_compatible #endpoints_compatible #region-us
|
## Model Specification
- Model: XLM-RoBERTa (base-sized model)
- Training Data:
- Combined Afrikaans, Hebrew, Bulgarian, Vietnamese, Norwegian, Urdu, Czech, & Persian corpora (Top 8 Languages)
- Training Details:
- Base configurations with a minor adjustment in learning rate (4.5e-5)
## Evaluation
- Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set)
- Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 78.29\% Accuracy)
## POS Tags
- ADJ โ ADP โ ADV โ CCONJ โ DET โ INTJ โ NOUN โ NUM โ PART โ PRON โ PROPN โ PUNCT โ SCONJ โ VERB | [
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] |
text-generation | transformers | # merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [Citaman/command-r-2-layer](https://huggingface.co/Citaman/command-r-2-layer)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: Citaman/command-r-2-layer
layer_range: [0, 1]
- model: Citaman/command-r-2-layer
layer_range: [1, 2]
merge_method: slerp
base_model: Citaman/command-r-2-layer
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
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Citaman/command-r-2-layer"]} | Citaman/command-r-1-layer | null | [
"transformers",
"safetensors",
"cohere",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:Citaman/command-r-2-layer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-17T13:10:16+00:00 | [] | [] | TAGS
#transformers #safetensors #cohere #text-generation #mergekit #merge #conversational #base_model-Citaman/command-r-2-layer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # merge
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* Citaman/command-r-2-layer
### Configuration
The following YAML configuration was used to produce this model:
| [
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"### Models Merged\n\nThe following models were included in the merge:\n* Citaman/command-r-2-layer",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
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"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* Citaman/command-r-2-layer",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] |
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 ๐ค 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": []} | JayShah008/gemma-pii-detection-Instruct-Finetune-test | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-17T13:11:47+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #gemma #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]:",
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"### Direct Use",
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"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
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"## Training Details",
"### Training Data",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
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"#### 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"
] | [
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"## 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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"### 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 |
# NikolayKozloff/gemma-portuguese-luana-2b-Q8_0-GGUF
This model was converted to GGUF format from [`rhaymison/gemma-portuguese-luana-2b`](https://huggingface.co/rhaymison/gemma-portuguese-luana-2b) 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/rhaymison/gemma-portuguese-luana-2b) 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 NikolayKozloff/gemma-portuguese-luana-2b-Q8_0-GGUF --model gemma-portuguese-luana-2b.Q8_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo NikolayKozloff/gemma-portuguese-luana-2b-Q8_0-GGUF --model gemma-portuguese-luana-2b.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 gemma-portuguese-luana-2b.Q8_0.gguf -n 128
```
| {"language": ["pt"], "license": "apache-2.0", "library_name": "transformers", "tags": ["portuguese", "brasil", "gemma", "portugues", "instrucao", "llama-cpp", "gguf-my-repo"], "datasets": ["rhaymison/superset"], "pipeline_tag": "text-generation", "widget": [{"text": "Me explique como funciona um computador.", "example_title": "Computador."}, {"text": "Me conte sobre a ida do homem a Lua.", "example_title": "Homem na Lua."}, {"text": "Fale sobre uma curiosidade sobre a hist\u00f3ria do mundo", "example_title": "Hist\u00f3ria."}, {"text": "Escreva um poema bem interessante sobre o Sol e as flores.", "example_title": "Escreva um poema."}], "model-index": [{"name": "gemma-portuguese-luana-2b", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "ENEM Challenge (No Images)", "type": "eduagarcia/enem_challenge", "split": "train", "args": {"num_few_shot": 3}}, "metrics": [{"type": "acc", "value": 24.42, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/gemma-portuguese-luana-2b", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "BLUEX (No Images)", "type": "eduagarcia-temp/BLUEX_without_images", "split": "train", "args": {"num_few_shot": 3}}, "metrics": [{"type": "acc", "value": 24.34, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/gemma-portuguese-luana-2b", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "OAB Exams", "type": "eduagarcia/oab_exams", "split": "train", "args": {"num_few_shot": 3}}, "metrics": [{"type": "acc", "value": 27.11, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/gemma-portuguese-luana-2b", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "Assin2 RTE", "type": "assin2", "split": "test", "args": {"num_few_shot": 15}}, "metrics": [{"type": "f1_macro", "value": 70.86, "name": "f1-macro"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/gemma-portuguese-luana-2b", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "Assin2 STS", "type": "eduagarcia/portuguese_benchmark", "split": "test", "args": {"num_few_shot": 15}}, "metrics": [{"type": "pearson", "value": 1.51, "name": "pearson"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/gemma-portuguese-luana-2b", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "FaQuAD NLI", "type": "ruanchaves/faquad-nli", "split": "test", "args": {"num_few_shot": 15}}, "metrics": [{"type": "f1_macro", "value": 43.97, "name": "f1-macro"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/gemma-portuguese-luana-2b", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "HateBR Binary", "type": "ruanchaves/hatebr", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "f1_macro", "value": 40.05, "name": "f1-macro"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/gemma-portuguese-luana-2b", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "PT Hate Speech Binary", "type": "hate_speech_portuguese", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "f1_macro", "value": 51.83, "name": "f1-macro"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/gemma-portuguese-luana-2b", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "tweetSentBR", "type": "eduagarcia/tweetsentbr_fewshot", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "f1_macro", "value": 30.42, "name": "f1-macro"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/gemma-portuguese-luana-2b", "name": "Open Portuguese LLM Leaderboard"}}]}]} | NikolayKozloff/gemma-portuguese-luana-2b-GGUF | null | [
"transformers",
"gguf",
"portuguese",
"brasil",
"gemma",
"portugues",
"instrucao",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"pt",
"dataset:rhaymison/superset",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T13:11:51+00:00 | [] | [
"pt"
] | TAGS
#transformers #gguf #portuguese #brasil #gemma #portugues #instrucao #llama-cpp #gguf-my-repo #text-generation #pt #dataset-rhaymison/superset #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# NikolayKozloff/gemma-portuguese-luana-2b-Q8_0-GGUF
This model was converted to GGUF format from 'rhaymison/gemma-portuguese-luana-2b' 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.
| [
"# NikolayKozloff/gemma-portuguese-luana-2b-Q8_0-GGUF\nThis model was converted to GGUF format from 'rhaymison/gemma-portuguese-luana-2b' 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 #portuguese #brasil #gemma #portugues #instrucao #llama-cpp #gguf-my-repo #text-generation #pt #dataset-rhaymison/superset #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# NikolayKozloff/gemma-portuguese-luana-2b-Q8_0-GGUF\nThis model was converted to GGUF format from 'rhaymison/gemma-portuguese-luana-2b' 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."
] |
token-classification | transformers |
## Model Specification
- Model: XLM-RoBERTa (base-sized model)
- Training Data:
- Combined Afrikaans, Hebrew, Bulgarian, Vietnamese, Norwegian, Urdu, Czech, Persian, & Faroese corpora (Top 9 Languages)
- Training Details:
- Base configurations with a minor adjustment in learning rate (4.5e-5)
## Evaluation
- Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set)
- Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 75.98\% Accuracy)
## POS Tags
- ADJ โ ADP โ ADV โ CCONJ โ DET โ INTJ โ NOUN โ NUM โ PART โ PRON โ PROPN โ PUNCT โ SCONJ โ VERB | {"language": ["tl"], "datasets": ["universal_dependencies"], "metrics": ["f1"], "pipeline_tag": "token-classification"} | iceman2434/xlm-roberta-base-ft-udpos213-top9lang | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"tl",
"dataset:universal_dependencies",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T13:13:05+00:00 | [] | [
"tl"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #tl #dataset-universal_dependencies #autotrain_compatible #endpoints_compatible #region-us
|
## Model Specification
- Model: XLM-RoBERTa (base-sized model)
- Training Data:
- Combined Afrikaans, Hebrew, Bulgarian, Vietnamese, Norwegian, Urdu, Czech, Persian, & Faroese corpora (Top 9 Languages)
- Training Details:
- Base configurations with a minor adjustment in learning rate (4.5e-5)
## Evaluation
- Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set)
- Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 75.98\% Accuracy)
## POS Tags
- ADJ โ ADP โ ADV โ CCONJ โ DET โ INTJ โ NOUN โ NUM โ PART โ PRON โ PROPN โ PUNCT โ SCONJ โ VERB | [
"## Model Specification\n- Model: XLM-RoBERTa (base-sized model)\n- Training Data:\n - Combined Afrikaans, Hebrew, Bulgarian, Vietnamese, Norwegian, Urdu, Czech, Persian, & Faroese corpora (Top 9 Languages)\n- Training Details:\n - Base configurations with a minor adjustment in learning rate (4.5e-5)",
"## Evaluation\n- Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set)\n- Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 75.98\\% Accuracy)",
"## POS Tags\n- ADJ โ ADP โ ADV โ CCONJ โ DET โ INTJ โ NOUN โ NUM โ PART โ PRON โ PROPN โ PUNCT โ SCONJ โ VERB"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #tl #dataset-universal_dependencies #autotrain_compatible #endpoints_compatible #region-us \n",
"## Model Specification\n- Model: XLM-RoBERTa (base-sized model)\n- Training Data:\n - Combined Afrikaans, Hebrew, Bulgarian, Vietnamese, Norwegian, Urdu, Czech, Persian, & Faroese corpora (Top 9 Languages)\n- Training Details:\n - Base configurations with a minor adjustment in learning rate (4.5e-5)",
"## Evaluation\n- Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set)\n- Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 75.98\\% Accuracy)",
"## POS Tags\n- ADJ โ ADP โ ADV โ CCONJ โ DET โ INTJ โ NOUN โ NUM โ PART โ PRON โ PROPN โ PUNCT โ SCONJ โ VERB"
] |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# msislam123/cifar10
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.4844
- Train Accuracy: 0.5160
- Validation Loss: 1.8361
- Validation Accuracy: 0.3676
- Epoch: 19
## 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 59840, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 2.7038 | 0.1213 | 2.5039 | 0.1698 | 0 |
| 2.4263 | 0.1935 | 2.3429 | 0.2179 | 1 |
| 2.2970 | 0.2343 | 2.1942 | 0.2901 | 2 |
| 2.2132 | 0.2694 | 2.1083 | 0.3115 | 3 |
| 2.1136 | 0.2998 | 2.0528 | 0.3102 | 4 |
| 2.0533 | 0.3145 | 2.0046 | 0.3182 | 5 |
| 2.0016 | 0.3292 | 1.9495 | 0.3356 | 6 |
| 1.9511 | 0.3463 | 1.9589 | 0.3182 | 7 |
| 1.9106 | 0.3636 | 1.9360 | 0.3249 | 8 |
| 1.8807 | 0.3700 | 1.9207 | 0.3396 | 9 |
| 1.8368 | 0.3790 | 1.8890 | 0.3556 | 10 |
| 1.8118 | 0.3951 | 1.8834 | 0.3489 | 11 |
| 1.7714 | 0.3967 | 1.8410 | 0.3730 | 12 |
| 1.7185 | 0.4225 | 1.8576 | 0.3396 | 13 |
| 1.6796 | 0.4439 | 1.8087 | 0.3743 | 14 |
| 1.6593 | 0.4519 | 1.8192 | 0.3543 | 15 |
| 1.6208 | 0.4539 | 1.8129 | 0.3650 | 16 |
| 1.5826 | 0.4826 | 1.8316 | 0.3663 | 17 |
| 1.5399 | 0.4913 | 1.7991 | 0.3650 | 18 |
| 1.4844 | 0.5160 | 1.8361 | 0.3676 | 19 |
### Framework versions
- Transformers 4.38.2
- TensorFlow 2.15.0
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "msislam123/cifar10", "results": []}]} | msislam123/cifar10 | null | [
"transformers",
"tf",
"vit",
"image-classification",
"generated_from_keras_callback",
"base_model:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T13:14:46+00:00 | [] | [] | TAGS
#transformers #tf #vit #image-classification #generated_from_keras_callback #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| msislam123/cifar10
==================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.4844
* Train Accuracy: 0.5160
* Validation Loss: 1.8361
* Validation Accuracy: 0.3676
* Epoch: 19
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:
* optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': {'module': 'keras.optimizers.schedules', 'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 3e-05, 'decay\_steps': 59840, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\_name': None}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.01}
* training\_precision: float32
### Training results
### Framework versions
* Transformers 4.38.2
* TensorFlow 2.15.0
* Datasets 2.18.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 3e-05, 'decay\\_steps': 59840, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* TensorFlow 2.15.0\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tf #vit #image-classification #generated_from_keras_callback #base_model-google/vit-base-patch16-224-in21k #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* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 3e-05, 'decay\\_steps': 59840, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* TensorFlow 2.15.0\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
token-classification | transformers |
## Model Specification
- Model: XLM-RoBERTa (base-sized model)
- Training Data:
- Combined Afrikaans, Hebrew, Bulgarian, Vietnamese, Norwegian, Urdu, Czech, Persian, Faroese, & English corpora (Top 10 Languages)
- Training Details:
- Base configurations with a minor adjustment in learning rate (4.5e-5)
## Evaluation
- Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set)
- Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 77.90\% Accuracy)
## POS Tags
- ADJ โ ADP โ ADV โ CCONJ โ DET โ INTJ โ NOUN โ NUM โ PART โ PRON โ PROPN โ PUNCT โ SCONJ โ VERB | {"language": ["tl"], "datasets": ["universal_dependencies"], "metrics": ["f1"], "pipeline_tag": "token-classification"} | iceman2434/xlm-roberta-base-ft-udpos213-top10lang | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"tl",
"dataset:universal_dependencies",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T13:14:48+00:00 | [] | [
"tl"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #tl #dataset-universal_dependencies #autotrain_compatible #endpoints_compatible #region-us
|
## Model Specification
- Model: XLM-RoBERTa (base-sized model)
- Training Data:
- Combined Afrikaans, Hebrew, Bulgarian, Vietnamese, Norwegian, Urdu, Czech, Persian, Faroese, & English corpora (Top 10 Languages)
- Training Details:
- Base configurations with a minor adjustment in learning rate (4.5e-5)
## Evaluation
- Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set)
- Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 77.90\% Accuracy)
## POS Tags
- ADJ โ ADP โ ADV โ CCONJ โ DET โ INTJ โ NOUN โ NUM โ PART โ PRON โ PROPN โ PUNCT โ SCONJ โ VERB | [
"## Model Specification\n- Model: XLM-RoBERTa (base-sized model)\n- Training Data:\n - Combined Afrikaans, Hebrew, Bulgarian, Vietnamese, Norwegian, Urdu, Czech, Persian, Faroese, & English corpora (Top 10 Languages)\n- Training Details:\n - Base configurations with a minor adjustment in learning rate (4.5e-5)",
"## Evaluation\n- Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set)\n- Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 77.90\\% Accuracy)",
"## POS Tags\n- ADJ โ ADP โ ADV โ CCONJ โ DET โ INTJ โ NOUN โ NUM โ PART โ PRON โ PROPN โ PUNCT โ SCONJ โ VERB"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #tl #dataset-universal_dependencies #autotrain_compatible #endpoints_compatible #region-us \n",
"## Model Specification\n- Model: XLM-RoBERTa (base-sized model)\n- Training Data:\n - Combined Afrikaans, Hebrew, Bulgarian, Vietnamese, Norwegian, Urdu, Czech, Persian, Faroese, & English corpora (Top 10 Languages)\n- Training Details:\n - Base configurations with a minor adjustment in learning rate (4.5e-5)",
"## Evaluation\n- Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set)\n- Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 77.90\\% Accuracy)",
"## POS Tags\n- ADJ โ ADP โ ADV โ CCONJ โ DET โ INTJ โ NOUN โ NUM โ PART โ PRON โ PROPN โ PUNCT โ SCONJ โ VERB"
] |
null | null |
# Japanese-Starling-ChatV-7B-RP-GGUF
## ๆฆ่ฆ
[Aratako/Japanese-Starling-ChatV-7B-RP](https://huggingface.co/Aratako/Japanese-Starling-ChatV-7B-RP)ใฎ้ๅญๅๆธใฟGGUF็ใงใใใฉใคใปใณใน็ญ่ฉณ็ดฐใฏๅ
ใขใใซใใ็ขบ่ชใใ ใใใ | {"language": ["ja"], "license": "apache-2.0", "tags": ["not-for-all-audiences", "nsfw"], "datasets": ["grimulkan/LimaRP-augmented", "Aratako/Rosebleu-1on1-Dialogues-RP"], "base_model": ["Aratako/Japanese-Starling-ChatV-7B-RP"]} | Aratako/Japanese-Starling-ChatV-7B-RP-GGUF | null | [
"gguf",
"not-for-all-audiences",
"nsfw",
"ja",
"dataset:grimulkan/LimaRP-augmented",
"dataset:Aratako/Rosebleu-1on1-Dialogues-RP",
"base_model:Aratako/Japanese-Starling-ChatV-7B-RP",
"license:apache-2.0",
"region:us"
] | null | 2024-04-17T13:15:40+00:00 | [] | [
"ja"
] | TAGS
#gguf #not-for-all-audiences #nsfw #ja #dataset-grimulkan/LimaRP-augmented #dataset-Aratako/Rosebleu-1on1-Dialogues-RP #base_model-Aratako/Japanese-Starling-ChatV-7B-RP #license-apache-2.0 #region-us
|
# Japanese-Starling-ChatV-7B-RP-GGUF
## ๆฆ่ฆ
Aratako/Japanese-Starling-ChatV-7B-RPใฎ้ๅญๅๆธใฟGGUF็ใงใใใฉใคใปใณใน็ญ่ฉณ็ดฐใฏๅ
ใขใใซใใ็ขบ่ชใใ ใใใ | [
"# Japanese-Starling-ChatV-7B-RP-GGUF",
"## ๆฆ่ฆ\nAratako/Japanese-Starling-ChatV-7B-RPใฎ้ๅญๅๆธใฟGGUF็ใงใใใฉใคใปใณใน็ญ่ฉณ็ดฐใฏๅ
ใขใใซใใ็ขบ่ชใใ ใใใ"
] | [
"TAGS\n#gguf #not-for-all-audiences #nsfw #ja #dataset-grimulkan/LimaRP-augmented #dataset-Aratako/Rosebleu-1on1-Dialogues-RP #base_model-Aratako/Japanese-Starling-ChatV-7B-RP #license-apache-2.0 #region-us \n",
"# Japanese-Starling-ChatV-7B-RP-GGUF",
"## ๆฆ่ฆ\nAratako/Japanese-Starling-ChatV-7B-RPใฎ้ๅญๅๆธใฟGGUF็ใงใใใฉใคใปใณใน็ญ่ฉณ็ดฐใฏๅ
ใขใใซใใ็ขบ่ชใใ ใใใ"
] |
null | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | sin66x/wav2vec2-large-xlsr-53-demo-colab | null | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T13:15:53+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #arxiv-1910.09700 #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]:",
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"## Training Details",
"### Training Data",
"### Training Procedure",
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"### Results",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
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] | [
"TAGS\n#transformers #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
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] |
feature-extraction | transformers | Mistral 7B finetuned on OpenHermes-2.5 to test open llm leaderboard metrics
1 epoch
[<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": "apache-2.0", "tags": ["axolotl"], "datasets": ["teknium/OpenHermes-2.5"]} | thepowefuldeez/mistral-openhermes-sft | null | [
"transformers",
"safetensors",
"mistral",
"feature-extraction",
"axolotl",
"en",
"dataset:teknium/OpenHermes-2.5",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-17T13:16:24+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #mistral #feature-extraction #axolotl #en #dataset-teknium/OpenHermes-2.5 #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us
| Mistral 7B finetuned on OpenHermes-2.5 to test open llm leaderboard metrics
1 epoch
<img src="URL alt="Built with Axolotl" width="200" height="32"/> | [] | [
"TAGS\n#transformers #safetensors #mistral #feature-extraction #axolotl #en #dataset-teknium/OpenHermes-2.5 #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | presencesw/mt5-base-vinli_3_label-cross | null | [
"transformers",
"safetensors",
"mt5",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-17T13:17:56+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #mt5 #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #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]:",
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"## Uses",
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"### 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.",
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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",
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] | [
"TAGS\n#transformers #safetensors #mt5 #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
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"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
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"### Model Architecture and Objective",
"### Compute Infrastructure",
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] |
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-large-english-TG
This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4494
- Wer: 18.0005
## 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: 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
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.0452 | 2.6350 | 1000 | 0.3455 | 19.6915 |
| 0.0034 | 5.2701 | 2000 | 0.3999 | 17.8823 |
| 0.0005 | 7.9051 | 3000 | 0.4770 | 18.1438 |
| 0.0001 | 10.5402 | 4000 | 0.4494 | 18.0005 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1
| {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice_1_0"], "metrics": ["wer"], "base_model": "openai/whisper-large", "model-index": [{"name": "whisper-large-english-TG", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "common_voice", "type": "common_voice_1_0", "config": "en", "split": "None", "args": "config: en, split: test"}, "metrics": [{"type": "wer", "value": 18.00053310232233, "name": "Wer"}]}]}]} | pranjali06/whisper-large-english-TG | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"en",
"dataset:common_voice_1_0",
"base_model:openai/whisper-large",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2024-04-17T13:19:53+00:00 | [] | [
"en"
] | TAGS
#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #en #dataset-common_voice_1_0 #base_model-openai/whisper-large #license-apache-2.0 #model-index #endpoints_compatible #region-us
| whisper-large-english-TG
========================
This model is a fine-tuned version of openai/whisper-large on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4494
* Wer: 18.0005
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: 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
* 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: 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* 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 #whisper #automatic-speech-recognition #generated_from_trainer #en #dataset-common_voice_1_0 #base_model-openai/whisper-large #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: 1e-05\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* 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"
] |
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