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object-detection | 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. -->
# detr
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6906
## 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: 0.0001
- 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: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.6507 | 0.04 | 100 | 2.1677 |
| 1.8892 | 0.08 | 200 | 1.6329 |
| 1.638 | 0.12 | 300 | 1.5517 |
| 1.4457 | 0.16 | 400 | 1.4176 |
| 1.3489 | 0.2 | 500 | 1.2559 |
| 1.277 | 0.24 | 600 | 1.2726 |
| 1.2948 | 0.28 | 700 | 1.2169 |
| 1.1878 | 0.32 | 800 | 1.1680 |
| 1.1781 | 0.36 | 900 | 1.1291 |
| 1.1747 | 0.4 | 1000 | 1.1146 |
| 1.1966 | 0.44 | 1100 | 1.1399 |
| 1.1641 | 0.48 | 1200 | 1.0844 |
| 1.128 | 0.52 | 1300 | 1.1119 |
| 1.1191 | 0.56 | 1400 | 1.0528 |
| 1.1435 | 0.6 | 1500 | 1.0689 |
| 1.1657 | 0.64 | 1600 | 1.1484 |
| 1.1727 | 0.68 | 1700 | 1.0764 |
| 1.1085 | 0.72 | 1800 | 1.0391 |
| 1.0579 | 0.76 | 1900 | 1.0012 |
| 1.0935 | 0.8 | 2000 | 1.0160 |
| 1.054 | 0.84 | 2100 | 0.9796 |
| 1.0486 | 0.88 | 2200 | 0.9508 |
| 1.0472 | 0.92 | 2300 | 0.9869 |
| 1.032 | 0.96 | 2400 | 0.9655 |
| 1.0313 | 1.0 | 2500 | 0.9484 |
| 1.0096 | 1.04 | 2600 | 0.9676 |
| 1.0279 | 1.08 | 2700 | 1.0771 |
| 1.027 | 1.12 | 2800 | 0.9912 |
| 1.0415 | 1.16 | 2900 | 0.9882 |
| 1.003 | 1.2 | 3000 | 0.9579 |
| 1.0084 | 1.24 | 3100 | 0.9288 |
| 0.9353 | 1.28 | 3200 | 0.9278 |
| 0.9514 | 1.32 | 3300 | 0.8915 |
| 0.9452 | 1.36 | 3400 | 0.8904 |
| 0.9312 | 1.4 | 3500 | 0.8925 |
| 0.9256 | 1.44 | 3600 | 0.8729 |
| 0.8861 | 1.48 | 3700 | 0.8655 |
| 0.9043 | 1.52 | 3800 | 0.8977 |
| 0.8935 | 1.56 | 3900 | 0.8679 |
| 0.8974 | 1.6 | 4000 | 0.8908 |
| 0.9342 | 1.64 | 4100 | 0.8742 |
| 0.889 | 1.68 | 4200 | 0.8534 |
| 0.8998 | 1.72 | 4300 | 0.8409 |
| 0.8727 | 1.76 | 4400 | 0.8333 |
| 0.8728 | 1.8 | 4500 | 0.8386 |
| 0.8525 | 1.84 | 4600 | 0.8152 |
| 0.8709 | 1.88 | 4700 | 0.8146 |
| 0.8694 | 1.92 | 4800 | 0.8245 |
| 0.8663 | 1.96 | 4900 | 0.8216 |
| 0.8442 | 2.0 | 5000 | 0.8019 |
| 0.8256 | 2.04 | 5100 | 0.8022 |
| 0.8385 | 2.08 | 5200 | 0.7938 |
| 0.7995 | 2.12 | 5300 | 0.7958 |
| 0.8217 | 2.16 | 5400 | 0.7962 |
| 0.8432 | 2.2 | 5500 | 0.7772 |
| 0.8228 | 2.24 | 5600 | 0.7857 |
| 0.8283 | 2.28 | 5700 | 0.7982 |
| 0.772 | 2.32 | 5800 | 0.7969 |
| 0.8019 | 2.36 | 5900 | 0.7902 |
| 0.7805 | 2.4 | 6000 | 0.7782 |
| 0.802 | 2.44 | 6100 | 0.7681 |
| 0.8483 | 2.48 | 6200 | 0.7722 |
| 0.802 | 2.52 | 6300 | 0.7673 |
| 0.8064 | 2.56 | 6400 | 0.7603 |
| 0.7638 | 2.6 | 6500 | 0.7475 |
| 0.7727 | 2.64 | 6600 | 0.7515 |
| 0.801 | 2.68 | 6700 | 0.7523 |
| 0.8022 | 2.72 | 6800 | 0.7519 |
| 0.8074 | 2.76 | 6900 | 0.7555 |
| 0.7951 | 2.8 | 7000 | 0.7450 |
| 0.8125 | 2.84 | 7100 | 0.7476 |
| 0.8085 | 2.88 | 7200 | 0.7505 |
| 0.7959 | 2.92 | 7300 | 0.7432 |
| 0.7668 | 2.96 | 7400 | 0.7454 |
| 0.7666 | 3.0 | 7500 | 0.7419 |
| 0.7422 | 3.04 | 7600 | 0.7284 |
| 0.7713 | 3.08 | 7700 | 0.7418 |
| 0.7296 | 3.12 | 7800 | 0.7274 |
| 0.7468 | 3.16 | 7900 | 0.7224 |
| 0.7767 | 3.2 | 8000 | 0.7268 |
| 0.7526 | 3.24 | 8100 | 0.7210 |
| 0.7328 | 3.28 | 8200 | 0.7139 |
| 0.7626 | 3.32 | 8300 | 0.7142 |
| 0.7515 | 3.36 | 8400 | 0.7102 |
| 0.7141 | 3.4 | 8500 | 0.7100 |
| 0.7068 | 3.44 | 8600 | 0.7097 |
| 0.7274 | 3.48 | 8700 | 0.7018 |
| 0.7458 | 3.52 | 8800 | 0.7041 |
| 0.7205 | 3.56 | 8900 | 0.7065 |
| 0.7643 | 3.6 | 9000 | 0.6985 |
| 0.6968 | 3.64 | 9100 | 0.6983 |
| 0.7111 | 3.68 | 9200 | 0.6982 |
| 0.7229 | 3.72 | 9300 | 0.6920 |
| 0.7466 | 3.76 | 9400 | 0.6959 |
| 0.7126 | 3.8 | 9500 | 0.6925 |
| 0.739 | 3.84 | 9600 | 0.6869 |
| 0.7449 | 3.88 | 9700 | 0.6939 |
| 0.7139 | 3.92 | 9800 | 0.6893 |
| 0.7216 | 3.96 | 9900 | 0.6895 |
| 0.6942 | 4.0 | 10000 | 0.6906 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "facebook/detr-resnet-50", "model-index": [{"name": "detr", "results": []}]} | Yaroslava270602/detr | null | [
"transformers",
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"object-detection",
"generated_from_trainer",
"base_model:facebook/detr-resnet-50",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T18:53:16+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #detr #object-detection #generated_from_trainer #base_model-facebook/detr-resnet-50 #license-apache-2.0 #endpoints_compatible #region-us
| detr
====
This model is a fine-tuned version of facebook/detr-resnet-50 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6906
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: 0.0001
* 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: 4
* 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. -->
# T5Model_for_Ecommerce
This model is a fine-tuned version of [Praveen76/FinetunedT5Model](https://huggingface.co/Praveen76/FinetunedT5Model) 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: 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: 15
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.2+cpu
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "Praveen76/FinetunedT5Model", "model-index": [{"name": "T5Model_for_Ecommerce", "results": []}]} | BalaSubrahmanyam/T5Model_for_Ecommerce | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:Praveen76/FinetunedT5Model",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-19T18:53:22+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-Praveen76/FinetunedT5Model #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# T5Model_for_Ecommerce
This model is a fine-tuned version of Praveen76/FinetunedT5Model 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: 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: 15
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.2+cpu
- Datasets 2.19.0
- Tokenizers 0.19.1
| [
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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",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-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- num_epochs: 15",
"### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.2.2+cpu\n- Datasets 2.19.0\n- Tokenizers 0.19.1"
] |
null | null | GGUF-IQ-Imatrix experimental quants for **dreamgen/opus-v1.2-llama-3-8b**.
> [!NOTE]
> This will have to uploaded again later.
> [!WARNING]
> Using a different testing config to avoid some reported issues so far and to get through the imatrix data generation. <br>
> This is experimental. Proper support and fixes should be coming in the respective projects in due time. | {"license": "cc-by-4.0"} | Lewdiculous/opus-v1.2-llama-3-8b-GGUF-IQ-Imatrix | null | [
"gguf",
"license:cc-by-4.0",
"region:us"
] | null | 2024-04-19T18:56:06+00:00 | [] | [] | TAGS
#gguf #license-cc-by-4.0 #region-us
| GGUF-IQ-Imatrix experimental quants for dreamgen/opus-v1.2-llama-3-8b.
> [!NOTE]
> This will have to uploaded again later.
> [!WARNING]
> Using a different testing config to avoid some reported issues so far and to get through the imatrix data generation. <br>
> This is experimental. Proper support and fixes should be coming in the respective projects in due time. | [] | [
"TAGS\n#gguf #license-cc-by-4.0 #region-us \n"
] |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | hi000000/insta_upnormal-llama3_80 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T18:58:39+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"## Technical Specifications [optional]",
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"### Compute Infrastructure",
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"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
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"# Model Card for Model ID",
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"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"## Training Details",
"### Training Data",
"### Training Procedure",
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"#### 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: -->
static quants of https://huggingface.co/vicgalle/Roleplay-Llama-3-8B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Roleplay-Llama-3-8B-GGUF/resolve/main/Roleplay-Llama-3-8B.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Roleplay-Llama-3-8B-GGUF/resolve/main/Roleplay-Llama-3-8B.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Roleplay-Llama-3-8B-GGUF/resolve/main/Roleplay-Llama-3-8B.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Roleplay-Llama-3-8B-GGUF/resolve/main/Roleplay-Llama-3-8B.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Roleplay-Llama-3-8B-GGUF/resolve/main/Roleplay-Llama-3-8B.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Roleplay-Llama-3-8B-GGUF/resolve/main/Roleplay-Llama-3-8B.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Roleplay-Llama-3-8B-GGUF/resolve/main/Roleplay-Llama-3-8B.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Roleplay-Llama-3-8B-GGUF/resolve/main/Roleplay-Llama-3-8B.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Roleplay-Llama-3-8B-GGUF/resolve/main/Roleplay-Llama-3-8B.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Roleplay-Llama-3-8B-GGUF/resolve/main/Roleplay-Llama-3-8B.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Roleplay-Llama-3-8B-GGUF/resolve/main/Roleplay-Llama-3-8B.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Roleplay-Llama-3-8B-GGUF/resolve/main/Roleplay-Llama-3-8B.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Roleplay-Llama-3-8B-GGUF/resolve/main/Roleplay-Llama-3-8B.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Roleplay-Llama-3-8B-GGUF/resolve/main/Roleplay-Llama-3-8B.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["roleplay", "rp", "role"], "datasets": ["ResplendentAI/NSFW_RP_Format_DPO"], "base_model": "vicgalle/Roleplay-Llama-3-8B", "quantized_by": "mradermacher"} | mradermacher/Roleplay-Llama-3-8B-GGUF | null | [
"transformers",
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"dataset:ResplendentAI/NSFW_RP_Format_DPO",
"base_model:vicgalle/Roleplay-Llama-3-8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T19:03:29+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #roleplay #rp #role #en #dataset-ResplendentAI/NSFW_RP_Format_DPO #base_model-vicgalle/Roleplay-Llama-3-8B #license-apache-2.0 #endpoints_compatible #region-us
| About
-----
static quants of URL
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
Usage
-----
If you are unsure how to use GGUF files, refer to one of TheBloke's
READMEs for
more details, including on how to concatenate multi-part files.
Provided Quants
---------------
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
!URL
And here are Artefact2's thoughts on the matter:
URL
FAQ / Model Request
-------------------
See URL for some answers to
questions you might have and/or if you want some other model quantized.
Thanks
------
I thank my company, nethype GmbH, for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
| [] | [
"TAGS\n#transformers #gguf #roleplay #rp #role #en #dataset-ResplendentAI/NSFW_RP_Format_DPO #base_model-vicgalle/Roleplay-Llama-3-8B #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Uploaded model
- **Developed by:** martyyz
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama 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", "llama", "trl", "sft"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | martyyz/llama3-8b-oig-unsloth-merged_4bit | null | [
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"license:apache-2.0",
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"endpoints_compatible",
"4-bit",
"region:us"
] | null | 2024-04-19T19:04:43+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #llama #text-generation #text-generation-inference #unsloth #trl #sft #en #base_model-unsloth/llama-3-8b-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #4-bit #region-us
|
# Uploaded model
- Developed by: martyyz
- License: apache-2.0
- Finetuned from model : unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
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] |
text-generation | transformers |
<br>
<br>
# MoMA Model Card
## Model details
**Model type:**
MoMA is an open-source image personalization model. It has new attention layers and a multi-modal large language model fine-tuned from LLaVA-7B.
**Paper or resources for more information:**
+ Project page: https://moma-adapter.github.io/
+ Github: https://github.com/bytedance/MoMA/tree/main
+ Paper: https://arxiv.org/abs/2404.05674
**Where to send questions or comments about the model:**
https://github.com/bytedance/MoMA/tree/main
## Intended use
**Primary intended uses:**
The primary use is research on personalized image generation tasks.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
| {"inference": false} | KunpengSong/MoMA_llava_7b | null | [
"transformers",
"pytorch",
"llava",
"text-generation",
"arxiv:2404.05674",
"autotrain_compatible",
"region:us"
] | null | 2024-04-19T19:06:34+00:00 | [
"2404.05674"
] | [] | TAGS
#transformers #pytorch #llava #text-generation #arxiv-2404.05674 #autotrain_compatible #region-us
|
<br>
<br>
# MoMA Model Card
## Model details
Model type:
MoMA is an open-source image personalization model. It has new attention layers and a multi-modal large language model fine-tuned from LLaVA-7B.
Paper or resources for more information:
+ Project page: URL
+ Github: URL
+ Paper: URL
Where to send questions or comments about the model:
URL
## Intended use
Primary intended uses:
The primary use is research on personalized image generation tasks.
Primary intended users:
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
| [
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"## Intended use\nPrimary intended uses:\nThe primary use is research on personalized image generation tasks.\n\nPrimary intended users:\nThe primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence."
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"## Model details\n\nModel type:\nMoMA is an open-source image personalization model. It has new attention layers and a multi-modal large language model fine-tuned from LLaVA-7B.\n\nPaper or resources for more information:\n+ Project page: URL\n+ Github: URL\n+ Paper: URL\n\nWhere to send questions or comments about the model:\nURL",
"## Intended use\nPrimary intended uses:\nThe primary use is research on personalized image generation tasks.\n\nPrimary intended users:\nThe primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence."
] |
automatic-speech-recognition | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | ajassal/whisper-small-llm-transcript | null | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T19:06:46+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #whisper #automatic-speech-recognition #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
| [
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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[More Information Needed] | {"library_name": "transformers", "tags": []} | ajassal/whisper-small-llm-transcript-adapters | null | [
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"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
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] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
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null | transformers |
# Uploaded model
- **Developed by:** martyyz
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- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama 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", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | martyyz/llama3-8b-oig-unsloth_lora | null | [
"transformers",
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"unsloth",
"llama",
"trl",
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#transformers #safetensors #text-generation-inference #unsloth #llama #trl #en #base_model-unsloth/llama-3-8b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: martyyz
- License: apache-2.0
- Finetuned from model : unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
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] |
null | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | nuebaek/komt_mistral_mss_user_0_max_steps_200 | null | [
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] | [] | TAGS
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image-classification | transformers |
This model has been pushed to the Hub using ****:
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- Docs: [More Information Needed] | {"tags": ["pytorch_model_hub_mixin", "model_hub_mixin"]} | date3k2/dl-resnet50 | null | [
"transformers",
"safetensors",
"resnet",
"image-classification",
"pytorch_model_hub_mixin",
"model_hub_mixin",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T19:10:13+00:00 | [] | [] | TAGS
#transformers #safetensors #resnet #image-classification #pytorch_model_hub_mixin #model_hub_mixin #autotrain_compatible #endpoints_compatible #region-us
|
This model has been pushed to the Hub using :
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"TAGS\n#transformers #safetensors #resnet #image-classification #pytorch_model_hub_mixin #model_hub_mixin #autotrain_compatible #endpoints_compatible #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. -->
# deberta-v2-xlarge-otat
This model is a fine-tuned version of [microsoft/deberta-v2-xlarge](https://huggingface.co/microsoft/deberta-v2-xlarge) on the DandinPower/review_onlytitleandtext dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6316
- Accuracy: 0.2011
- Macro F1: 0.0670
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4.5e-05
- train_batch_size: 4
- eval_batch_size: 4
- 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: linear
- lr_scheduler_warmup_steps: 1500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 1.1994 | 0.14 | 500 | 1.6893 | 0.4029 | 0.3240 |
| 1.6344 | 0.29 | 1000 | 1.6403 | 0.2011 | 0.0670 |
| 1.6413 | 0.43 | 1500 | 1.6270 | 0.2 | 0.0667 |
| 1.6326 | 0.57 | 2000 | 1.6375 | 0.1971 | 0.0659 |
| 1.6128 | 0.71 | 2500 | 1.6604 | 0.2011 | 0.0670 |
| 1.6213 | 0.86 | 3000 | 1.6161 | 0.2 | 0.0667 |
| 1.6199 | 1.0 | 3500 | 1.6132 | 0.2017 | 0.0671 |
| 1.6177 | 1.14 | 4000 | 1.6142 | 0.2011 | 0.0670 |
| 1.6183 | 1.29 | 4500 | 1.6213 | 0.2 | 0.0667 |
| 1.6211 | 1.43 | 5000 | 1.6136 | 0.1971 | 0.0659 |
| 1.6145 | 1.57 | 5500 | 1.6169 | 0.1971 | 0.0659 |
| 1.6187 | 1.71 | 6000 | 1.6160 | 0.2011 | 0.0670 |
| 1.6174 | 1.86 | 6500 | 1.6146 | 0.2 | 0.0667 |
| 1.6164 | 2.0 | 7000 | 1.6181 | 0.2 | 0.0667 |
| 1.6184 | 2.14 | 7500 | 1.6109 | 0.1971 | 0.0659 |
| 1.6152 | 2.29 | 8000 | 1.6189 | 0.2 | 0.0667 |
| 1.6175 | 2.43 | 8500 | 1.6146 | 0.1971 | 0.0659 |
| 1.6134 | 2.57 | 9000 | 1.6160 | 0.1971 | 0.0659 |
| 1.6144 | 2.71 | 9500 | 1.6167 | 0.2011 | 0.0670 |
| 1.6141 | 2.86 | 10000 | 1.6106 | 0.2017 | 0.0671 |
| 1.6128 | 3.0 | 10500 | 1.6139 | 0.1971 | 0.0659 |
| 1.6179 | 3.14 | 11000 | 1.6112 | 0.2 | 0.0667 |
| 1.6096 | 3.29 | 11500 | 1.6127 | 0.2 | 0.0667 |
| 1.6132 | 3.43 | 12000 | 1.6135 | 0.2011 | 0.0670 |
| 1.6053 | 3.57 | 12500 | 1.6186 | 0.2 | 0.0667 |
| 1.6049 | 3.71 | 13000 | 1.6277 | 0.2011 | 0.0670 |
| 1.6044 | 3.86 | 13500 | 1.6271 | 0.2011 | 0.0670 |
| 1.6017 | 4.0 | 14000 | 1.6275 | 0.2011 | 0.0670 |
| 1.608 | 4.14 | 14500 | 1.6192 | 0.2011 | 0.0670 |
| 1.6075 | 4.29 | 15000 | 1.6259 | 0.2011 | 0.0670 |
| 1.601 | 4.43 | 15500 | 1.6267 | 0.2011 | 0.0670 |
| 1.6086 | 4.57 | 16000 | 1.6339 | 0.2011 | 0.0670 |
| 1.5955 | 4.71 | 16500 | 1.6340 | 0.2011 | 0.0670 |
| 1.6013 | 4.86 | 17000 | 1.6322 | 0.2011 | 0.0670 |
| 1.5976 | 5.0 | 17500 | 1.6316 | 0.2011 | 0.0670 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"language": ["en"], "license": "mit", "tags": ["nycu-112-2-datamining-hw2", "generated_from_trainer"], "datasets": ["DandinPower/review_onlytitleandtext"], "metrics": ["accuracy"], "base_model": "microsoft/deberta-v2-xlarge", "model-index": [{"name": "deberta-v2-xlarge-otat", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "DandinPower/review_onlytitleandtext", "type": "DandinPower/review_onlytitleandtext"}, "metrics": [{"type": "accuracy", "value": 0.20114285714285715, "name": "Accuracy"}]}]}]} | DandinPower/deberta-v2-xlarge-otat | null | [
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"nycu-112-2-datamining-hw2",
"generated_from_trainer",
"en",
"dataset:DandinPower/review_onlytitleandtext",
"base_model:microsoft/deberta-v2-xlarge",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T19:10:51+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #deberta-v2 #text-classification #nycu-112-2-datamining-hw2 #generated_from_trainer #en #dataset-DandinPower/review_onlytitleandtext #base_model-microsoft/deberta-v2-xlarge #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| deberta-v2-xlarge-otat
======================
This model is a fine-tuned version of microsoft/deberta-v2-xlarge on the DandinPower/review\_onlytitleandtext dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6316
* Accuracy: 0.2011
* Macro F1: 0.0670
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 4.5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* 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: linear
* lr\_scheduler\_warmup\_steps: 1500
* num\_epochs: 5
### 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 hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4.5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\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: linear\n* lr\\_scheduler\\_warmup\\_steps: 1500\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.2.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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<!-- 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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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **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": []} | KaggleMasterX/llama3_8b_nonit_1804 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T19:11:20+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"## Training Details",
"### Training Data",
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"## Model Card Contact"
] |
text-generation | transformers |
# Uploaded model
- **Developed by:** IBM
- **Quantized by:** sroecker
- **License:** apache-2.0
- **Derived from model :** [ibm/merlinite-7b](https://huggingface.co/ibm/merlinite-7b)
This is just a quantized 4bit version created for further fine-tuning.
[<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", "merlinite", "lab"], "base_model": "ibm/merlinite-7b"} | sroecker/merlinite-7b-bnb-4bit | null | [
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"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
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"merlinite",
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"base_model:ibm/merlinite-7b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"region:us"
] | null | 2024-04-19T19:12:57+00:00 | [] | [
"en"
] | TAGS
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|
# Uploaded model
- Developed by: IBM
- Quantized by: sroecker
- License: apache-2.0
- Derived from model : ibm/merlinite-7b
This is just a quantized 4bit version created for further fine-tuning.
<img src="URL width="200"/>
| [
"# Uploaded model\n\n- Developed by: IBM\n- Quantized by: sroecker\n- License: apache-2.0\n- Derived from model : ibm/merlinite-7b\n\nThis is just a quantized 4bit version created for further fine-tuning.\n\n<img src=\"URL width=\"200\"/>"
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] |
text-generation | transformers |
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` | {"license": "other", "library_name": "transformers", "tags": ["autotrain", "text-generation-inference", "text-generation", "peft"], "widget": [{"messages": [{"role": "user", "content": "What is your favorite condiment?"}]}]} | zaursamedov1/FinQa-llama3 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain",
"text-generation-inference",
"peft",
"conversational",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T19:14:47+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #autotrain #text-generation-inference #peft #conversational #license-other #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit AutoTrain.
# Usage
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"# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.",
"# Usage"
] | [
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"# Usage"
] |
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. -->
# final_distilBert_cased_for_binary_sentiment_classification
This model is a fine-tuned version of [distilbert/distilbert-base-cased](https://huggingface.co/distilbert/distilbert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2317
- Accuracy: 0.9215
- F1: 0.9189
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.2821 | 1.0 | 1000 | 0.2381 | 0.9185 | 0.9143 |
| 0.2067 | 2.0 | 2000 | 0.2317 | 0.9215 | 0.9189 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "distilbert/distilbert-base-cased", "model-index": [{"name": "final_distilBert_cased_for_binary_sentiment_classification", "results": []}]} | ThoMyh/final_distilBert_cased_for_binary_sentiment_classification | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T19:16:45+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert/distilbert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| final\_distilBert\_cased\_for\_binary\_sentiment\_classification
================================================================
This model is a fine-tuned version of distilbert/distilbert-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2317
* Accuracy: 0.9215
* F1: 0.9189
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
### Training results
### Framework versions
* Transformers 4.39.3
* Pytorch 2.2.2
* Datasets 2.18.0
* Tokenizers 0.15.2
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.2.2\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. -->
# robust_llm_pythia-410m_ian-022_PasswordMatch_n-its-25
This model is a fine-tuned version of [EleutherAI/pythia-410m](https://huggingface.co/EleutherAI/pythia-410m) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-410m", "model-index": [{"name": "robust_llm_pythia-410m_ian-022_PasswordMatch_n-its-25", "results": []}]} | AlignmentResearch/robust_llm_pythia-410m_ian-022_PasswordMatch_n-its-25 | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-410m",
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"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-19T19:17:44+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #gpt_neox #text-classification #generated_from_trainer #base_model-EleutherAI/pythia-410m #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# robust_llm_pythia-410m_ian-022_PasswordMatch_n-its-25
This model is a fine-tuned version of EleutherAI/pythia-410m on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
| [
"# robust_llm_pythia-410m_ian-022_PasswordMatch_n-its-25\n\nThis model is a fine-tuned version of EleutherAI/pythia-410m on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 0\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1",
"### Training results",
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] | [
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"# robust_llm_pythia-410m_ian-022_PasswordMatch_n-its-25\n\nThis model is a fine-tuned version of EleutherAI/pythia-410m on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 0\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1",
"### Training results",
"### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.2.1\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
text-classification | transformers |
# Model Card for Model ID
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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[More Information Needed] | {"library_name": "transformers", "tags": []} | reevan/bert_kan_rom | null | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T19:19:02+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
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#### Preprocessing [optional]
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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### 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]",
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"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
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"## Glossary [optional]",
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"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-generation | transformers | 
# 🔬 Einstein-v6.1-Llama3-8B
This model is a full fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/Einstein-v6.1-Llama3-8) on diverse datasets.
This model is finetuned using `8xRTX3090` + `1xRTXA6000` using [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl).
This model's training was sponsored by [sablo.ai](https://sablo.ai).
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: meta-llama/Meta-Llama-3-8B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: false
strict: false
chat_template: chatml
datasets:
- path: data/merged_all.json
ds_type: json
type: alpaca
conversation: chatml
- path: data/gpteacher-instruct-special-alpaca.json
ds_type: json
type: gpteacher
conversation: chatml
- path: data/wizardlm_evol_instruct_70k_random_half.json
ds_type: json
type: alpaca
conversation: chatml
- path: data/capybara_sharegpt.json
ds_type: json
type: sharegpt
conversation: chatml
- path: data/synthia-v1.3_sharegpt_12500.json
ds_type: json
type: sharegpt
conversation: chatml
- path: data/cot_alpaca_gpt4_extracted_openhermes_2.5_sharegpt.json
ds_type: json
type: sharegpt
conversation: chatml
- path: data/slimorca_dedup_filtered_95k_sharegpt.json
ds_type: json
type: sharegpt
conversation: chatml
- path: data/airoboros_3.2_without_contextual_slimorca_orca_sharegpt.json
ds_type: json
type: sharegpt
conversation: chatml
- path: data/allenai_wild_chat_gpt4_english_toxic_random_half_4k_sharegpt.json
ds_type: json
type: sharegpt
strict: false
conversation: chatml
- path: data/pippa_bagel_repo_3k_sharegpt.json
ds_type: json
type: sharegpt
conversation: chatml
- path: data/gpt4_data_lmys_1m_sharegpt.json
ds_type: json
type: sharegpt
conversation: chatml
- path: data/sharegpt_gpt4_english.json
ds_type: json
type: sharegpt
conversation: chatml
- path: data/no_robots_sharegpt.json
ds_type: json
type: sharegpt
strict: false
conversation: chatml
- path: data/oasst_top1_from_fusechatmixture_sharegpt.json
ds_type: json
type: sharegpt
strict: false
conversation: chatml
- path: data/everythinglm-data-v3_sharegpt.json
ds_type: json
type: sharegpt
strict: false
conversation: chatml
dataset_prepared_path: last_run_prepared
val_set_size: 0.002
output_dir: ./Einstein-v6.1-Llama3-8B-model
sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false
wandb_project: Einstein
wandb_entity:
wandb_watch:
wandb_name: Einstein-v6.1-Llama3-2-epoch
wandb_log_model:
hub_model_id: Weyaxi/Einstein-v6.1-Llama3-8B
save_safetensors: true
gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 2
optimizer: adamw_bnb_8bit # look
lr_scheduler: cosine
learning_rate: 0.000005 # look
train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 10
evals_per_epoch: 2
eval_table_size:
eval_table_max_new_tokens: 128
saves_per_epoch: 2
debug:
deepspeed: zero3_bf16_cpuoffload_params.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
bos_token: "<s>"
eos_token: "<|im_end|>"
unk_token: "<unk>"
pad_token: <|end_of_text|> # changed
tokens:
- "<|im_start|>"
```
</details><br>
# 💬 Prompt Template
You can use ChatML prompt template while using the model:
### ChatML
```
<|im_start|>system
{system}<|im_end|>
<|im_start|>user
{user}<|im_end|>
<|im_start|>assistant
{asistant}<|im_end|>
```
This prompt template is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the
`tokenizer.apply_chat_template()` method:
```python
messages = [
{"role": "system", "content": "You are helpful AI asistant."},
{"role": "user", "content": "Hello!"}
]
gen_input = tokenizer.apply_chat_template(message, return_tensors="pt")
model.generate(**gen_input)
```
# 📊 Datasets used in this model
The datasets used to train this model are listed in the metadata section of the model card.
Please note that certain datasets mentioned in the metadata may have undergone filtering based on various criteria.
The results of this filtering process and its outcomes are in the data folder of this repository:
[Weyaxi/Einstein-v6.1-Llama3-8B/data](https://huggingface.co/Weyaxi/Einstein-v6.1-Llama3-8B/tree/main/data)
# 🔄 Quantizationed versions
## GGUF [@bartowski](https://huggingface.co/bartowski)
- https://huggingface.co/bartowski/Einstein-v6.1-Llama3-8B-GGUF
## ExLlamaV2 [@bartowski](https://huggingface.co/bartowski)
- https://huggingface.co/bartowski/Einstein-v6.1-Llama3-8B-exl2
## AWQ [@solidrust](https://huggingface.co/solidrust)
- https://huggingface.co/solidrust/Einstein-v6.1-Llama3-8B-AWQ
# 🎯 [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_Weyaxi__Einstein-v6.1-Llama3-8B)
| Metric |Value|
|---------------------------------|----:|
|Avg. |68.60|
|AI2 Reasoning Challenge (25-Shot)|62.46|
|HellaSwag (10-Shot) |82.41|
|MMLU (5-Shot) |66.19|
|TruthfulQA (0-shot) |55.10|
|Winogrande (5-shot) |79.32|
|GSM8k (5-shot) |66.11|
# 🤖 Additional information about training
This model is full fine-tuned for 2 epoch.
Total number of steps was 2026.
<details><summary>Loss graph</summary>
</details><br>
# 🤝 Acknowledgments
Thanks to [sablo.ai](https://sablo.ai) for sponsoring this model.
Thanks to all the dataset authors mentioned in the datasets section.
Thanks to [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) for making the repository I used to make this model.
Thanks to all open source AI community.
[<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)
If you would like to support me:
[☕ Buy Me a Coffee](https://www.buymeacoffee.com/weyaxi)
| {"language": ["en"], "license": "other", "tags": ["axolotl", "generated_from_trainer", "instruct", "finetune", "chatml", "gpt4", "synthetic data", "science", "physics", "chemistry", "biology", "math", "llama", "llama3"], "datasets": ["allenai/ai2_arc", "camel-ai/physics", "camel-ai/chemistry", "camel-ai/biology", "camel-ai/math", "metaeval/reclor", "openbookqa", "mandyyyyii/scibench", "derek-thomas/ScienceQA", "TIGER-Lab/ScienceEval", "jondurbin/airoboros-3.2", "LDJnr/Capybara", "Cot-Alpaca-GPT4-From-OpenHermes-2.5", "STEM-AI-mtl/Electrical-engineering", "knowrohit07/saraswati-stem", "sablo/oasst2_curated", "lmsys/lmsys-chat-1m", "TIGER-Lab/MathInstruct", "bigbio/med_qa", "meta-math/MetaMathQA-40K", "openbookqa", "piqa", "metaeval/reclor", "derek-thomas/ScienceQA", "scibench", "sciq", "Open-Orca/SlimOrca", "migtissera/Synthia-v1.3", "TIGER-Lab/ScienceEval", "allenai/WildChat", "microsoft/orca-math-word-problems-200k", "openchat/openchat_sharegpt4_dataset", "teknium/GPTeacher-General-Instruct", "m-a-p/CodeFeedback-Filtered-Instruction", "totally-not-an-llm/EverythingLM-data-V3", "HuggingFaceH4/no_robots", "OpenAssistant/oasst_top1_2023-08-25", "WizardLM/WizardLM_evol_instruct_70k"], "base_model": "meta-llama/Meta-Llama-3-8B", "model-index": [{"name": "Einstein-v6.1-Llama3-8B", "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": 62.46, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v6.1-Llama3-8B", "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": 82.41, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v6.1-Llama3-8B", "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": 66.19, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v6.1-Llama3-8B", "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": 55.1}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v6.1-Llama3-8B", "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": 79.32, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v6.1-Llama3-8B", "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": 66.11, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v6.1-Llama3-8B", "name": "Open LLM Leaderboard"}}]}]} | Weyaxi/Einstein-v6.1-Llama3-8B | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"axolotl",
"generated_from_trainer",
"instruct",
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"dataset:LDJnr/Capybara",
"dataset:Cot-Alpaca-GPT4-From-OpenHermes-2.5",
"dataset:STEM-AI-mtl/Electrical-engineering",
"dataset:knowrohit07/saraswati-stem",
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"license:other",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-19T19:21:15+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #llama #text-generation #axolotl #generated_from_trainer #instruct #finetune #chatml #gpt4 #synthetic data #science #physics #chemistry #biology #math #llama3 #conversational #en #dataset-allenai/ai2_arc #dataset-camel-ai/physics #dataset-camel-ai/chemistry #dataset-camel-ai/biology #dataset-camel-ai/math #dataset-metaeval/reclor #dataset-openbookqa #dataset-mandyyyyii/scibench #dataset-derek-thomas/ScienceQA #dataset-TIGER-Lab/ScienceEval #dataset-jondurbin/airoboros-3.2 #dataset-LDJnr/Capybara #dataset-Cot-Alpaca-GPT4-From-OpenHermes-2.5 #dataset-STEM-AI-mtl/Electrical-engineering #dataset-knowrohit07/saraswati-stem #dataset-sablo/oasst2_curated #dataset-lmsys/lmsys-chat-1m #dataset-TIGER-Lab/MathInstruct #dataset-bigbio/med_qa #dataset-meta-math/MetaMathQA-40K #dataset-piqa #dataset-scibench #dataset-sciq #dataset-Open-Orca/SlimOrca #dataset-migtissera/Synthia-v1.3 #dataset-allenai/WildChat #dataset-microsoft/orca-math-word-problems-200k #dataset-openchat/openchat_sharegpt4_dataset #dataset-teknium/GPTeacher-General-Instruct #dataset-m-a-p/CodeFeedback-Filtered-Instruction #dataset-totally-not-an-llm/EverythingLM-data-V3 #dataset-HuggingFaceH4/no_robots #dataset-OpenAssistant/oasst_top1_2023-08-25 #dataset-WizardLM/WizardLM_evol_instruct_70k #base_model-meta-llama/Meta-Llama-3-8B #license-other #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| !image/png
Einstein-v6.1-Llama3-8B
=======================
This model is a full fine-tuned version of meta-llama/Meta-Llama-3-8B on diverse datasets.
This model is finetuned using '8xRTX3090' + '1xRTXA6000' using axolotl.
This model's training was sponsored by URL.
See axolotl config
axolotl version: '0.4.0'
Prompt Template
===============
You can use ChatML prompt template while using the model:
### ChatML
This prompt template is available as a chat template, which means you can format messages using the
'tokenizer.apply\_chat\_template()' method:
Datasets used in this model
===========================
The datasets used to train this model are listed in the metadata section of the model card.
Please note that certain datasets mentioned in the metadata may have undergone filtering based on various criteria.
The results of this filtering process and its outcomes are in the data folder of this repository:
Weyaxi/Einstein-v6.1-Llama3-8B/data
Quantizationed versions
=======================
GGUF @bartowski
---------------
* URL
ExLlamaV2 @bartowski
--------------------
* URL
AWQ @solidrust
--------------
* URL
Open LLM Leaderboard Evaluation Results
=======================================
Detailed results can be found here
Additional information about training
=====================================
This model is full fine-tuned for 2 epoch.
Total number of steps was 2026.
Loss graph
!image/png
Acknowledgments
===============
Thanks to URL for sponsoring this model.
Thanks to all the dataset authors mentioned in the datasets section.
Thanks to axolotl for making the repository I used to make this model.
Thanks to all open source AI community.
<img src="URL alt="Built with Axolotl" width="200" height="32"/>
If you would like to support me:
Buy Me a Coffee
| [
"### ChatML\n\n\nThis prompt template is available as a chat template, which means you can format messages using the\n'tokenizer.apply\\_chat\\_template()' method:\n\n\nDatasets used in this model\n===========================\n\n\nThe datasets used to train this model are listed in the metadata section of the model card.\n\n\nPlease note that certain datasets mentioned in the metadata may have undergone filtering based on various criteria.\n\n\nThe results of this filtering process and its outcomes are in the data folder of this repository:\n\n\nWeyaxi/Einstein-v6.1-Llama3-8B/data\n\n\nQuantizationed versions\n=======================\n\n\nGGUF @bartowski\n---------------\n\n\n* URL\n\n\nExLlamaV2 @bartowski\n--------------------\n\n\n* URL\n\n\nAWQ @solidrust\n--------------\n\n\n* URL\n\n\nOpen LLM Leaderboard Evaluation Results\n=======================================\n\n\nDetailed results can be found here\n\n\n\nAdditional information about training\n=====================================\n\n\nThis model is full fine-tuned for 2 epoch.\n\n\nTotal number of steps was 2026.\n\n\nLoss graph\n!image/png\n\n\n \n\nAcknowledgments\n===============\n\n\nThanks to URL for sponsoring this model.\n\n\nThanks to all the dataset authors mentioned in the datasets section.\n\n\nThanks to axolotl for making the repository I used to make this model.\n\n\nThanks to all open source AI community.\n\n\n<img src=\"URL alt=\"Built with Axolotl\" width=\"200\" height=\"32\"/>\n\n\nIf you would like to support me:\n\n\nBuy Me a Coffee"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #axolotl #generated_from_trainer #instruct #finetune #chatml #gpt4 #synthetic data #science #physics #chemistry #biology #math #llama3 #conversational #en #dataset-allenai/ai2_arc #dataset-camel-ai/physics #dataset-camel-ai/chemistry #dataset-camel-ai/biology #dataset-camel-ai/math #dataset-metaeval/reclor #dataset-openbookqa #dataset-mandyyyyii/scibench #dataset-derek-thomas/ScienceQA #dataset-TIGER-Lab/ScienceEval #dataset-jondurbin/airoboros-3.2 #dataset-LDJnr/Capybara #dataset-Cot-Alpaca-GPT4-From-OpenHermes-2.5 #dataset-STEM-AI-mtl/Electrical-engineering #dataset-knowrohit07/saraswati-stem #dataset-sablo/oasst2_curated #dataset-lmsys/lmsys-chat-1m #dataset-TIGER-Lab/MathInstruct #dataset-bigbio/med_qa #dataset-meta-math/MetaMathQA-40K #dataset-piqa #dataset-scibench #dataset-sciq #dataset-Open-Orca/SlimOrca #dataset-migtissera/Synthia-v1.3 #dataset-allenai/WildChat #dataset-microsoft/orca-math-word-problems-200k #dataset-openchat/openchat_sharegpt4_dataset #dataset-teknium/GPTeacher-General-Instruct #dataset-m-a-p/CodeFeedback-Filtered-Instruction #dataset-totally-not-an-llm/EverythingLM-data-V3 #dataset-HuggingFaceH4/no_robots #dataset-OpenAssistant/oasst_top1_2023-08-25 #dataset-WizardLM/WizardLM_evol_instruct_70k #base_model-meta-llama/Meta-Llama-3-8B #license-other #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### ChatML\n\n\nThis prompt template is available as a chat template, which means you can format messages using the\n'tokenizer.apply\\_chat\\_template()' method:\n\n\nDatasets used in this model\n===========================\n\n\nThe datasets used to train this model are listed in the metadata section of the model card.\n\n\nPlease note that certain datasets mentioned in the metadata may have undergone filtering based on various criteria.\n\n\nThe results of this filtering process and its outcomes are in the data folder of this repository:\n\n\nWeyaxi/Einstein-v6.1-Llama3-8B/data\n\n\nQuantizationed versions\n=======================\n\n\nGGUF @bartowski\n---------------\n\n\n* URL\n\n\nExLlamaV2 @bartowski\n--------------------\n\n\n* URL\n\n\nAWQ @solidrust\n--------------\n\n\n* URL\n\n\nOpen LLM Leaderboard Evaluation Results\n=======================================\n\n\nDetailed results can be found here\n\n\n\nAdditional information about training\n=====================================\n\n\nThis model is full fine-tuned for 2 epoch.\n\n\nTotal number of steps was 2026.\n\n\nLoss graph\n!image/png\n\n\n \n\nAcknowledgments\n===============\n\n\nThanks to URL for sponsoring this model.\n\n\nThanks to all the dataset authors mentioned in the datasets section.\n\n\nThanks to axolotl for making the repository I used to make this model.\n\n\nThanks to all open source AI community.\n\n\n<img src=\"URL alt=\"Built with Axolotl\" width=\"200\" height=\"32\"/>\n\n\nIf you would like to support me:\n\n\nBuy Me a Coffee"
] |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | HikariLight/Mistral-SUFT-10-5e-05-1-all | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T19:21:35+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
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- Finetuned from model [optional]:
### Model Sources [optional]
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## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
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## Technical Specifications [optional]
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### Compute Infrastructure
#### Hardware
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[optional]
BibTeX:
APA:
## Glossary [optional]
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## Model Card 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.",
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"## Training Details",
"### Training Data",
"### Training Procedure",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
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"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
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"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-generation | transformers |
# 🚀 al-baka-llama3-8b
[<img src="https://i.ibb.co/fMsBM0M/Screenshot-2024-04-20-at-3-04-34-AM.png" width="150"/>](https://www.omarai.co)
Al Baka is an Experimental Fine Tuned Model based on the new released LLAMA3-8B Model on the Stanford Alpaca dataset Arabic version [Yasbok/Alpaca_arabic_instruct](https://huggingface.co/datasets/Yasbok/Alpaca_arabic_instruct).
## Model Summary
- **Model Type:** Llama3-8B FineTuned Model
- **Language(s):** Arabic
- **Base Model:** [LLAMA-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B)
- **Dataset:** [Yasbok/Alpaca_arabic_instruct](https://huggingface.co/datasets/Yasbok/Alpaca_arabic_instruct)
## Model Details
- The model was fine-tuned in 4-bit precision using [unsloth](https://github.com/unslothai/unsloth)
- The run is performed only for 1000 steps with a single Google Colab T4 GPU NVIDIA GPU with 15 GB of available memory.
<span style="color:red">The model is currently being Experimentally Fine Tuned to assess LLaMA-3's response to Arabic, following a brief period of fine-tuning. Larger and more sophisticated models will be introduced soon.</span>
## How to Get Started with the Model
### Setup
```python
# Install packages
%%capture
import torch
major_version, minor_version = torch.cuda.get_device_capability()
!pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
if major_version >= 8:
# Use this for new GPUs like Ampere, Hopper GPUs (RTX 30xx, RTX 40xx, A100, H100, L40)
!pip install --no-deps packaging ninja einops flash-attn xformers trl peft accelerate bitsandbytes
else:
# Use this for older GPUs (V100, Tesla T4, RTX 20xx)
!pip install --no-deps xformers trl peft accelerate bitsandbytes
pass
```
### First, Load the Model
```python
from unsloth import FastLanguageModel
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "Omartificial-Intelligence-Space/al-baka-16bit-llama3-8b",
max_seq_length = max_seq_length,
dtype = dtype,
load_in_4bit = load_in_4bit,
# token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)
```
### Second, Try the model
```python
alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
{}
### Input:
{}
### Response:
{}"""
# alpaca_prompt = Copied from above
FastLanguageModel.for_inference(model) # Enable native 2x faster inference
inputs = tokenizer(
[
alpaca_prompt.format(
"استخدم البيانات المعطاة لحساب الوسيط.", # instruction
"[2 ، 3 ، 7 ، 8 ، 10]", # input
"", # output - leave this blank for generation!
)
], return_tensors = "pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)
tokenizer.batch_decode(outputs)
```
### Recommendations
- [unsloth](https://github.com/unslothai/unsloth) for finetuning models. You can get a 2x faster finetuned model which can be exported to any format or uploaded to Hugging Face.
| {"language": ["ar"], "license": "apache-2.0", "tags": ["alpaca", "llama3", "arabic"]} | Omartificial-Intelligence-Space/al-baka-llama3-8b-experimental | null | [
"transformers",
"pytorch",
"tensorboard",
"llama",
"text-generation",
"alpaca",
"llama3",
"arabic",
"ar",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-19T19:22:08+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #tensorboard #llama #text-generation #alpaca #llama3 #arabic #ar #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# al-baka-llama3-8b
<img src="https://i.URL width="150"/>
Al Baka is an Experimental Fine Tuned Model based on the new released LLAMA3-8B Model on the Stanford Alpaca dataset Arabic version Yasbok/Alpaca_arabic_instruct.
## Model Summary
- Model Type: Llama3-8B FineTuned Model
- Language(s): Arabic
- Base Model: LLAMA-3-8B
- Dataset: Yasbok/Alpaca_arabic_instruct
## Model Details
- The model was fine-tuned in 4-bit precision using unsloth
- The run is performed only for 1000 steps with a single Google Colab T4 GPU NVIDIA GPU with 15 GB of available memory.
<span style="color:red">The model is currently being Experimentally Fine Tuned to assess LLaMA-3's response to Arabic, following a brief period of fine-tuning. Larger and more sophisticated models will be introduced soon.</span>
## How to Get Started with the Model
### Setup
### First, Load the Model
### Second, Try the model
### Recommendations
- unsloth for finetuning models. You can get a 2x faster finetuned model which can be exported to any format or uploaded to Hugging Face.
| [
"# al-baka-llama3-8b\n\n<img src=\"https://i.URL width=\"150\"/>\n\n\nAl Baka is an Experimental Fine Tuned Model based on the new released LLAMA3-8B Model on the Stanford Alpaca dataset Arabic version Yasbok/Alpaca_arabic_instruct.",
"## Model Summary\n\n- Model Type: Llama3-8B FineTuned Model\n- Language(s): Arabic\n- Base Model: LLAMA-3-8B\n- Dataset: Yasbok/Alpaca_arabic_instruct",
"## Model Details\n\n- The model was fine-tuned in 4-bit precision using unsloth\n\n- The run is performed only for 1000 steps with a single Google Colab T4 GPU NVIDIA GPU with 15 GB of available memory.\n\n\n<span style=\"color:red\">The model is currently being Experimentally Fine Tuned to assess LLaMA-3's response to Arabic, following a brief period of fine-tuning. Larger and more sophisticated models will be introduced soon.</span>",
"## How to Get Started with the Model",
"### Setup",
"### First, Load the Model",
"### Second, Try the model",
"### Recommendations\n\n- unsloth for finetuning models. You can get a 2x faster finetuned model which can be exported to any format or uploaded to Hugging Face."
] | [
"TAGS\n#transformers #pytorch #tensorboard #llama #text-generation #alpaca #llama3 #arabic #ar #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# al-baka-llama3-8b\n\n<img src=\"https://i.URL width=\"150\"/>\n\n\nAl Baka is an Experimental Fine Tuned Model based on the new released LLAMA3-8B Model on the Stanford Alpaca dataset Arabic version Yasbok/Alpaca_arabic_instruct.",
"## Model Summary\n\n- Model Type: Llama3-8B FineTuned Model\n- Language(s): Arabic\n- Base Model: LLAMA-3-8B\n- Dataset: Yasbok/Alpaca_arabic_instruct",
"## Model Details\n\n- The model was fine-tuned in 4-bit precision using unsloth\n\n- The run is performed only for 1000 steps with a single Google Colab T4 GPU NVIDIA GPU with 15 GB of available memory.\n\n\n<span style=\"color:red\">The model is currently being Experimentally Fine Tuned to assess LLaMA-3's response to Arabic, following a brief period of fine-tuning. Larger and more sophisticated models will be introduced soon.</span>",
"## How to Get Started with the Model",
"### Setup",
"### First, Load the Model",
"### Second, Try the model",
"### Recommendations\n\n- unsloth for finetuning models. You can get a 2x faster finetuned model which can be exported to any format or uploaded to Hugging Face."
] |
null | adapter-transformers |
# Adapter `BigTMiami/micro_seq_bn_helpfulness_classification_adapter` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/amazon_MICRO_helpfulness_dataset](https://huggingface.co/datasets/BigTMiami/amazon_MICRO_helpfulness_dataset/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("BigTMiami/micro_seq_bn_helpfulness_classification_adapter", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> | {"tags": ["roberta", "adapter-transformers"], "datasets": ["BigTMiami/amazon_MICRO_helpfulness_dataset"]} | BigTMiami/micro_seq_bn_helpfulness_classification_adapter | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/amazon_MICRO_helpfulness_dataset",
"region:us"
] | null | 2024-04-19T19:23:20+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/amazon_MICRO_helpfulness_dataset #region-us
|
# Adapter 'BigTMiami/micro_seq_bn_helpfulness_classification_adapter' for roberta-base
An adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_MICRO_helpfulness_dataset dataset and includes a prediction head for classification.
This adapter was created for usage with the Adapters library.
## Usage
First, install 'adapters':
Now, the adapter can be loaded and activated like this:
## Architecture & Training
## Evaluation results
| [
"# Adapter 'BigTMiami/micro_seq_bn_helpfulness_classification_adapter' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_MICRO_helpfulness_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] | [
"TAGS\n#adapter-transformers #roberta #dataset-BigTMiami/amazon_MICRO_helpfulness_dataset #region-us \n",
"# Adapter 'BigTMiami/micro_seq_bn_helpfulness_classification_adapter' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_MICRO_helpfulness_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### 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": []} | hgnoi/fine-tune-21 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T19:23:34+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]:",
"### 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 #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"
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed] | {"library_name": "transformers", "tags": []} | hgnoi/fine-tune-26 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T19:23:38+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
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#### 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-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
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[More Information Needed]
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### Results
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## 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]
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## Technical Specifications [optional]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | hgnoi/fine-tune-16 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
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"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T19:23:40+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:
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## Uses
### Direct Use
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
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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
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text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed] | {"library_name": "transformers", "tags": []} | hgnoi/fine-tune-28 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T19:23:41+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):
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- Finetuned from model [optional]:
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- 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-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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- **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. -->
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## Bias, Risks, and Limitations
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[More Information Needed] | {"library_name": "transformers", "tags": []} | hgnoi/fine-tune-15 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T19:23:51+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
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text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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[More Information Needed] | {"library_name": "transformers", "tags": []} | hgnoi/fine-tune-18 | null | [
"transformers",
"safetensors",
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"text-generation",
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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- Carbon Emitted:
## Technical Specifications [optional]
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### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | nuebaek/komt_mistral_mss_user_111_max_steps_200 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
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### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
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## Evaluation
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#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
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text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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### 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. -->
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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### Training Procedure
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
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[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed] | {"library_name": "transformers", "tags": []} | hgnoi/fine-tune-24 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
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"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T19:24:05+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
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] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **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:**
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## Glossary [optional]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | hgnoi/fine-tune-17 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T19:24:05+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
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text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **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]
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### Compute Infrastructure
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[More Information Needed] | {"library_name": "transformers", "tags": []} | hgnoi/fine-tune-19 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T19:24:06+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):
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## Uses
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## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
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## Technical Specifications [optional]
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### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
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## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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[More Information Needed] | {"library_name": "transformers", "tags": []} | hgnoi/fine-tune-22 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
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"arxiv:1910.09700",
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"endpoints_compatible",
"region:us"
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"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:
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- Model type:
- Language(s) (NLP):
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## Uses
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
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#### 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-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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### 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. -->
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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## Training Details
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
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#### 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. -->
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#### 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]
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[More Information Needed]
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## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed] | {"library_name": "transformers", "tags": []} | hgnoi/fine-tune-29 | null | [
"transformers",
"safetensors",
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"text-generation",
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"endpoints_compatible",
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#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]:
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### 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.
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Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
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### Model Architecture and Objective
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APA:
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] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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[More Information Needed] | {"library_name": "transformers", "tags": []} | hgnoi/fine-tune-27 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T19:24:16+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:
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- Language(s) (NLP):
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## Uses
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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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
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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## How to Get Started with the Model
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed] | {"library_name": "transformers", "tags": []} | hgnoi/fine-tune-20 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
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"endpoints_compatible",
"region:us"
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"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
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text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### 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. -->
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
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#### 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
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## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed] | {"library_name": "transformers", "tags": []} | hgnoi/fine-tune-25 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T19:24:16+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:
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] |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | hgnoi/fine-tune-23 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
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] | null | 2024-04-19T19:24:17+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
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summarization | transformers |
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[More Information Needed] | {"library_name": "transformers", "datasets": ["multi_news"], "pipeline_tag": "summarization"} | BeenaSamuel/t5_small_multi_news_abstractive_summarizer | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"summarization",
"dataset:multi_news",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-19T19:24:46+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #t5 #text2text-generation #summarization #dataset-multi_news #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
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] | [
"TAGS\n#transformers #safetensors #t5 #text2text-generation #summarization #dataset-multi_news #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
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] |
summarization | 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. -->
# multinews_cnn_logs
This model is a fine-tuned version of [BeenaSamuel/t5_cnn_daily_mail_abstractive_summarizer_v2](https://huggingface.co/BeenaSamuel/t5_cnn_daily_mail_abstractive_summarizer_v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.8039
- eval_rouge1: 0.5189
- eval_rouge2: 0.1941
- eval_rougeL: 0.397
- eval_gen_len: 311.236
- eval_runtime: 1446.5993
- eval_samples_per_second: 3.886
- eval_steps_per_second: 0.486
- epoch: 4.84
- step: 3400
## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2 | {"tags": ["generated_from_trainer"], "base_model": "BeenaSamuel/t5_cnn_daily_mail_abstractive_summarizer_v2", "pipeline_tag": "summarization", "model-index": [{"name": "multinews_cnn_logs", "results": []}]} | BeenaSamuel/multinews_cnn_logs | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"summarization",
"base_model:BeenaSamuel/t5_cnn_daily_mail_abstractive_summarizer_v2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-19T19:25:17+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #summarization #base_model-BeenaSamuel/t5_cnn_daily_mail_abstractive_summarizer_v2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# multinews_cnn_logs
This model is a fine-tuned version of BeenaSamuel/t5_cnn_daily_mail_abstractive_summarizer_v2 on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.8039
- eval_rouge1: 0.5189
- eval_rouge2: 0.1941
- eval_rougeL: 0.397
- eval_gen_len: 311.236
- eval_runtime: 1446.5993
- eval_samples_per_second: 3.886
- eval_steps_per_second: 0.486
- epoch: 4.84
- step: 3400
## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2 | [
"# multinews_cnn_logs\n\nThis model is a fine-tuned version of BeenaSamuel/t5_cnn_daily_mail_abstractive_summarizer_v2 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 1.8039\n- eval_rouge1: 0.5189\n- eval_rouge2: 0.1941\n- eval_rougeL: 0.397\n- eval_gen_len: 311.236\n- eval_runtime: 1446.5993\n- eval_samples_per_second: 3.886\n- eval_steps_per_second: 0.486\n- epoch: 4.84\n- step: 3400",
"## 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: 0.0001\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 10",
"### 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 #summarization #base_model-BeenaSamuel/t5_cnn_daily_mail_abstractive_summarizer_v2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# multinews_cnn_logs\n\nThis model is a fine-tuned version of BeenaSamuel/t5_cnn_daily_mail_abstractive_summarizer_v2 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 1.8039\n- eval_rouge1: 0.5189\n- eval_rouge2: 0.1941\n- eval_rougeL: 0.397\n- eval_gen_len: 311.236\n- eval_runtime: 1446.5993\n- eval_samples_per_second: 3.886\n- eval_steps_per_second: 0.486\n- epoch: 4.84\n- step: 3400",
"## 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: 0.0001\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 10",
"### Framework versions\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. -->
# classfier_model
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4654
- Accuracy: 0.9468
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1614 | 1.0 | 667 | 0.2974 | 0.9288 |
| 0.1254 | 2.0 | 1334 | 0.2327 | 0.9475 |
| 0.074 | 3.0 | 2001 | 0.2994 | 0.9408 |
| 0.0395 | 4.0 | 2668 | 0.2954 | 0.9490 |
| 0.0357 | 5.0 | 3335 | 0.3130 | 0.9468 |
| 0.0157 | 6.0 | 4002 | 0.3754 | 0.9370 |
| 0.0125 | 7.0 | 4669 | 0.3821 | 0.9460 |
| 0.0148 | 8.0 | 5336 | 0.3943 | 0.9460 |
| 0.0082 | 9.0 | 6003 | 0.3938 | 0.9453 |
| 0.0074 | 10.0 | 6670 | 0.3879 | 0.9490 |
| 0.0063 | 11.0 | 7337 | 0.4074 | 0.9460 |
| 0.005 | 12.0 | 8004 | 0.4431 | 0.9460 |
| 0.0052 | 13.0 | 8671 | 0.4539 | 0.9460 |
| 0.0037 | 14.0 | 9338 | 0.4731 | 0.9415 |
| 0.0042 | 15.0 | 10005 | 0.4563 | 0.9460 |
| 0.0021 | 16.0 | 10672 | 0.4594 | 0.9445 |
| 0.0011 | 17.0 | 11339 | 0.4608 | 0.9475 |
| 0.0021 | 18.0 | 12006 | 0.4669 | 0.9468 |
| 0.0012 | 19.0 | 12673 | 0.4676 | 0.9460 |
| 0.0009 | 20.0 | 13340 | 0.4654 | 0.9468 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "distilbert/distilbert-base-uncased", "model-index": [{"name": "classfier_model", "results": []}]} | maiphong0411/classfier_model | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T19:26:13+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert/distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| classfier\_model
================
This model is a fine-tuned version of distilbert/distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4654
* Accuracy: 0.9468
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
### Training results
### Framework versions
* Transformers 4.38.2
* Pytorch 2.2.1+cu121
* Datasets 2.19.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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: 20",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.15.2"
] | [
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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: 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: 20",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.15.2"
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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<!-- 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. -->
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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<!-- 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]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | harir/zephyr-revision-test | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T19:26:53+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]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"### Direct Use",
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"## Bias, Risks, and Limitations",
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"#### Metrics",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
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"## 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]",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
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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 |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
Quick Llama 3 8B finetune with ORPO. Demontration that it can be fine tune in 2 hours only.
Thanks to Maxime Labonne's notebook:
https://colab.research.google.com/drive/1eHNWg9gnaXErdAa8_mcvjMupbSS6rDvi?usp=sharing
- **Number of training samples from the dataset**: 1500 out of 40K
- **Hardware Type:** L4
- **Hours of training:** 2
- **Cloud Provider:** google colab
| {"license": "apache-2.0", "library_name": "transformers", "tags": ["orpo", "llama", "llama 3", "fine tune"], "datasets": ["mlabonne/orpo-dpo-mix-40k"]} | mayacinka/OrpoLlama-3-8B | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"orpo",
"llama 3",
"fine tune",
"conversational",
"dataset:mlabonne/orpo-dpo-mix-40k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-19T19:26:58+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #orpo #llama 3 #fine tune #conversational #dataset-mlabonne/orpo-dpo-mix-40k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
Quick Llama 3 8B finetune with ORPO. Demontration that it can be fine tune in 2 hours only.
Thanks to Maxime Labonne's notebook:
URL
- Number of training samples from the dataset: 1500 out of 40K
- Hardware Type: L4
- Hours of training: 2
- Cloud Provider: google colab
| [
"# Model Card for Model ID\n\n\nQuick Llama 3 8B finetune with ORPO. Demontration that it can be fine tune in 2 hours only. \nThanks to Maxime Labonne's notebook: \n\nURL\n\n- Number of training samples from the dataset: 1500 out of 40K\n- Hardware Type: L4\n- Hours of training: 2\n- Cloud Provider: google colab"
] | [
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"# Model Card for Model ID\n\n\nQuick Llama 3 8B finetune with ORPO. Demontration that it can be fine tune in 2 hours only. \nThanks to Maxime Labonne's notebook: \n\nURL\n\n- Number of training samples from the dataset: 1500 out of 40K\n- Hardware Type: L4\n- Hours of training: 2\n- Cloud Provider: google colab"
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
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## Uses
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
### Results
[More Information Needed]
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | whizzzzkid/llama3333 | null | [
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"safetensors",
"llama",
"text-generation",
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"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
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"1910.09700"
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#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
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
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[optional]
BibTeX:
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## Glossary [optional]
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text-generation | transformers |
# Uploaded model
- **Developed by:** nehuggingface
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama 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", "llama", "trl", "sft"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | nehuggingface/tmptmptmp2 | null | [
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|
# Uploaded model
- Developed by: nehuggingface
- License: apache-2.0
- Finetuned from model : unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
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] |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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### Results
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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | hi000000/insta_upnormal-llama3_200 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T19:35:06+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
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### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
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#### Speeds, Sizes, Times [optional]
## Evaluation
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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## Technical Specifications [optional]
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#### Hardware
#### Software
[optional]
BibTeX:
APA:
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## Model Card Authors [optional]
## Model Card Contact
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] |
text-generation | transformers |
# Uploaded model
- **Developed by:** BarraHome
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama 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", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | BarraHome/llama-3-newborn-16bits | null | [
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"license:apache-2.0",
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"region:us"
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"en"
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#transformers #safetensors #llama #text-generation #text-generation-inference #unsloth #trl #en #base_model-unsloth/llama-3-8b-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: BarraHome
- License: apache-2.0
- Finetuned from model : unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
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] |
text-classification | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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
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[More Information Needed] | {"library_name": "transformers", "tags": []} | reevan/test_bert | null | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T19:35:51+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
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- Language(s) (NLP):
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### Model Sources [optional]
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## Uses
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### Out-of-Scope Use
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### Recommendations
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## How to Get Started with the Model
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## Training Details
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## Technical Specifications [optional]
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### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
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"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
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"## Training Details",
"### Training Data",
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"#### Testing Data",
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"#### Metrics",
"### Results",
"#### Summary",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
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"## Model Card Contact"
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"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
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"## Glossary [optional]",
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"## Model Card Authors [optional]",
"## Model Card Contact"
] |
sentence-similarity | sentence-transformers |
# luiz-and-robert-thesis/all-mpnet-base-newtriplets-v2-lr-1e-8-m-5-e-3
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('luiz-and-robert-thesis/all-mpnet-base-newtriplets-v2-lr-1e-8-m-5-e-3')
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=luiz-and-robert-thesis/all-mpnet-base-newtriplets-v2-lr-1e-8-m-5-e-3)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 5885 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.TripletLoss.TripletLoss` with parameters:
```
{'distance_metric': 'TripletDistanceMetric.COSINE', 'triplet_margin': 5}
```
Parameters of the fit()-Method:
```
{
"epochs": 3,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 1e-08
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 2648,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
(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"} | luiz-and-robert-thesis/all-mpnet-base-newtriplets-v2-lr-1e-8-m-5-e-3 | null | [
"sentence-transformers",
"safetensors",
"mpnet",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T19:37:53+00:00 | [] | [] | TAGS
#sentence-transformers #safetensors #mpnet #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# luiz-and-robert-thesis/all-mpnet-base-newtriplets-v2-lr-1e-8-m-5-e-3
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:
'URL.dataloader.DataLoader' of length 5885 with parameters:
Loss:
'sentence_transformers.losses.TripletLoss.TripletLoss' with parameters:
Parameters of the fit()-Method:
## Full Model Architecture
## Citing & Authors
| [
"# luiz-and-robert-thesis/all-mpnet-base-newtriplets-v2-lr-1e-8-m-5-e-3\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'URL.dataloader.DataLoader' of length 5885 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.TripletLoss.TripletLoss' with parameters:\n \n\nParameters of the fit()-Method:",
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"## 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\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'URL.dataloader.DataLoader' of length 5885 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.TripletLoss.TripletLoss' with parameters:\n \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
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[More Information Needed]
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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed] | {"library_name": "transformers", "tags": []} | whizzzzkid/llamaft3v2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-19T19:42:00+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
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- Finetuned from model [optional]:
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## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"### Training Data",
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"#### Metrics",
"### Results",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
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"## 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",
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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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"### 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.",
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"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### 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]
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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | OwOOwO/dumbo-krillin106 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-19T19:43:11+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.",
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"## Training Details",
"### Training Data",
"### Training Procedure",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"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]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "236.87 +/- 13.37", "name": "mean_reward", "verified": false}]}]}]} | rahil1206/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2024-04-19T19:43:32+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | Yasusan/Llama_221_110_220_110 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-19T19:43:45+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #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:",
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"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
null | adapter-transformers |
# Adapter `BigTMiami/dapt_tapt_seq_bn_helpfulness_classification_adapter` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/amazon_helpfulness](https://huggingface.co/datasets/BigTMiami/amazon_helpfulness/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("BigTMiami/dapt_tapt_seq_bn_helpfulness_classification_adapter", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> | {"tags": ["adapter-transformers", "roberta"], "datasets": ["BigTMiami/amazon_helpfulness"]} | BigTMiami/dapt_tapt_seq_bn_helpfulness_classification_adapter | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/amazon_helpfulness",
"region:us"
] | null | 2024-04-19T19:43:54+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/amazon_helpfulness #region-us
|
# Adapter 'BigTMiami/dapt_tapt_seq_bn_helpfulness_classification_adapter' for roberta-base
An adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_helpfulness dataset and includes a prediction head for classification.
This adapter was created for usage with the Adapters library.
## Usage
First, install 'adapters':
Now, the adapter can be loaded and activated like this:
## Architecture & Training
## Evaluation results
| [
"# Adapter 'BigTMiami/dapt_tapt_seq_bn_helpfulness_classification_adapter' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_helpfulness dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] | [
"TAGS\n#adapter-transformers #roberta #dataset-BigTMiami/amazon_helpfulness #region-us \n",
"# Adapter 'BigTMiami/dapt_tapt_seq_bn_helpfulness_classification_adapter' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_helpfulness dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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[More Information Needed] | {"library_name": "transformers", "tags": []} | baconnier/CIB_BANKER_Llama-3-8B | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-19T19:44:11+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
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BibTeX:
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## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
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text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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[More Information Needed] | {"library_name": "transformers", "tags": []} | tdeshane/OrpoLlama-3-8B | null | [
"transformers",
"safetensors",
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"region:us"
] | null | 2024-04-19T19:45:36+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
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## Training Details
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### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
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## Evaluation
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#### Metrics
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## How to Get Started with the Model
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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### Results
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## 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]
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## Technical Specifications [optional]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | Yasusan/Llama_211_110 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
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"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-19T19:45:40+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #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:
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- Shared by [optional]:
- Model type:
- Language(s) (NLP):
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- Finetuned from model [optional]:
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- 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.
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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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"## 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 #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:
* [nehuggingface/tmptmptmp2](https://huggingface.co/nehuggingface/tmptmptmp2)
* [JDBMG/Llama3-8B-SlimOrca_f16](https://huggingface.co/JDBMG/Llama3-8B-SlimOrca_f16)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: JDBMG/Llama3-8B-SlimOrca_f16
layer_range: [0, 32]
- model: nehuggingface/tmptmptmp2
layer_range: [0, 32]
merge_method: slerp
base_model: nehuggingface/tmptmptmp2
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": ["nehuggingface/tmptmptmp2", "JDBMG/Llama3-8B-SlimOrca_f16"]} | nehuggingface/llama3-8B-simOrca-lgalclause-inverted-persuasion-slerp-fp16 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"base_model:nehuggingface/tmptmptmp2",
"base_model:JDBMG/Llama3-8B-SlimOrca_f16",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-19T19:46:19+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #mergekit #merge #base_model-nehuggingface/tmptmptmp2 #base_model-JDBMG/Llama3-8B-SlimOrca_f16 #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:
* nehuggingface/tmptmptmp2
* JDBMG/Llama3-8B-SlimOrca_f16
### 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* nehuggingface/tmptmptmp2\n* JDBMG/Llama3-8B-SlimOrca_f16",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #base_model-nehuggingface/tmptmptmp2 #base_model-JDBMG/Llama3-8B-SlimOrca_f16 #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* nehuggingface/tmptmptmp2\n* JDBMG/Llama3-8B-SlimOrca_f16",
"### 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 🤗 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": []} | Yasusan/Llama_121_110 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-19T19:47:34+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #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 #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 | <!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto"
>
<img src="https://github.com/janhq/jan/assets/89722390/35daac7d-b895-487c-a6ac-6663daaad78e" alt="Jan banner"
style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<p align="center">
<a href="https://jan.ai/">Jan</a
>
- <a href="https://discord.gg/AsJ8krTT3N">Discord</a>
</p>
<!-- header end -->
This is a math fine-tuning of the LLaMA-3 70B model.
# Prompt template
Llama3
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{{ system_prompt }}<|eot_id|><|start_header_id|>user<|end_header_id|>
{{ user_message_1 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
{{ model_answer_1 }}<|eot_id|><|start_header_id|>user<|end_header_id|>
{{ user_message_2 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```
# Run this model
You can run this model using [Jan Desktop](https://jan.ai/) on Mac, Windows, or Linux.
Jan is an open source, ChatGPT alternative that is:
- 💻 **100% offline on your machine**: Your conversations remain confidential, and visible only to you.
- 🗂️ **
An Open File Format**: Conversations and model settings stay on your computer and can be exported or deleted at any time.
- 🌐 **OpenAI Compatible**: Local server on port `1337` with OpenAI compatible endpoints
- 🌍 **Open Source & Free**: We build in public; check out our [Github](https://github.com/janhq)
# About Jan
Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones.
Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life. | {"language": ["en"], "license": "llama2"} | jan-hq/Yakult-70B | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-19T19:47:36+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #llama #text-generation #conversational #en #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<div style="width: auto; margin-left: auto; margin-right: auto"
>
<img src="URL alt="Jan banner"
style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<p align="center">
<a href="URL
>
- <a href="URL
</p>
This is a math fine-tuning of the LLaMA-3 70B model.
# Prompt template
Llama3
# Run this model
You can run this model using Jan Desktop on Mac, Windows, or Linux.
Jan is an open source, ChatGPT alternative that is:
- 100% offline on your machine: Your conversations remain confidential, and visible only to you.
- ️
An Open File Format: Conversations and model settings stay on your computer and can be exported or deleted at any time.
- OpenAI Compatible: Local server on port '1337' with OpenAI compatible endpoints
- Open Source & Free: We build in public; check out our Github
!image/png
# About Jan
Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones.
Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life. | [
"# Prompt template\n\nLlama3",
"# Run this model\nYou can run this model using Jan Desktop on Mac, Windows, or Linux.\n\nJan is an open source, ChatGPT alternative that is:\n\n- 100% offline on your machine: Your conversations remain confidential, and visible only to you.\n- ️ \nAn Open File Format: Conversations and model settings stay on your computer and can be exported or deleted at any time.\n- OpenAI Compatible: Local server on port '1337' with OpenAI compatible endpoints\n\n- Open Source & Free: We build in public; check out our Github\n\n!image/png",
"# About Jan\nJan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones.\n\nJan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life."
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #en #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Prompt template\n\nLlama3",
"# Run this model\nYou can run this model using Jan Desktop on Mac, Windows, or Linux.\n\nJan is an open source, ChatGPT alternative that is:\n\n- 100% offline on your machine: Your conversations remain confidential, and visible only to you.\n- ️ \nAn Open File Format: Conversations and model settings stay on your computer and can be exported or deleted at any time.\n- OpenAI Compatible: Local server on port '1337' with OpenAI compatible endpoints\n\n- Open Source & Free: We build in public; check out our Github\n\n!image/png",
"# About Jan\nJan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones.\n\nJan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life."
] |
null | transformers |
# Uploaded model
- **Developed by:** shivam9980
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama 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", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | shivam9980/llama-8b-news-bhojpuri | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T19:48:14+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #text-generation-inference #unsloth #llama #trl #en #base_model-unsloth/llama-3-8b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: shivam9980
- License: apache-2.0
- Finetuned from model : unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
| [
"# Uploaded model\n\n- Developed by: shivam9980\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
"TAGS\n#transformers #safetensors #text-generation-inference #unsloth #llama #trl #en #base_model-unsloth/llama-3-8b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Uploaded model\n\n- Developed by: shivam9980\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | Yasusan/Llama_112_110 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-19T19:49:16+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #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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"## Model Details",
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"## 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. -->
# MODEL_C
This model is a fine-tuned version of [NousResearch/Llama-2-7b-hf](https://huggingface.co/NousResearch/Llama-2-7b-hf) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## 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
- load_in_4bit: True
- load_in_8bit: False
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.4.0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
| {"library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "NousResearch/Llama-2-7b-hf", "model-index": [{"name": "MODEL_C", "results": []}]} | LLMLover/MODEL_C | null | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:NousResearch/Llama-2-7b-hf",
"region:us"
] | null | 2024-04-19T19:50:04+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #generated_from_trainer #base_model-NousResearch/Llama-2-7b-hf #region-us
|
# MODEL_C
This model is a fine-tuned version of NousResearch/Llama-2-7b-hf on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## 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
- load_in_4bit: True
- load_in_8bit: False
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.4.0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
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] |
null | transformers |
# Uploaded model
- **Developed by:** cackerman
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.2-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl"], "base_model": "unsloth/mistral-7b-instruct-v0.2-bnb-4bit"} | cackerman/rewrites_mistral7unsloth_4bit_ft_full_secondft3 | null | [
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"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T19:50:09+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #text-generation-inference #unsloth #mistral #trl #en #base_model-unsloth/mistral-7b-instruct-v0.2-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: cackerman
- License: apache-2.0
- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit
This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
| [
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] |
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-base-invoice-extractor-remake-2
This model was trained from scratch on the imagefolder 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: 1
- 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 4.39.3
- Pytorch 2.2.2+cu118
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "model-index": [{"name": "donut-base-invoice-extractor-remake-2", "results": []}]} | kantii/donut-base-invoice-extractor-remake-2 | null | [
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"generated_from_trainer",
"dataset:imagefolder",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T19:53:40+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #vision-encoder-decoder #generated_from_trainer #dataset-imagefolder #endpoints_compatible #region-us
|
# donut-base-invoice-extractor-remake-2
This model was trained from scratch on the imagefolder 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: 1
- 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 4.39.3
- Pytorch 2.2.2+cu118
- Datasets 2.18.0
- Tokenizers 0.15.2
| [
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"### Training results",
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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. -->
# bert-emotion
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0968
- Precision: 0.7318
- Recall: 0.7244
- Fscore: 0.7260
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- 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 | Precision | Recall | Fscore |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.8764 | 1.0 | 815 | 0.7180 | 0.7398 | 0.6814 | 0.6949 |
| 0.5429 | 2.0 | 1630 | 0.9484 | 0.7405 | 0.6849 | 0.7021 |
| 0.2913 | 3.0 | 2445 | 1.0968 | 0.7318 | 0.7244 | 0.7260 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall"], "base_model": "distilbert-base-cased", "model-index": [{"name": "bert-emotion", "results": []}]} | hirenvadalia/bert-emotion | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T19:56:18+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-emotion
============
This model is a fine-tuned version of distilbert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0968
* Precision: 0.7318
* Recall: 0.7244
* Fscore: 0.7260
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
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* Pytorch 2.2.1+cu121
* Datasets 2.19.0
* Tokenizers 0.19.1
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] |
summarization | transformers |
# Model Card for Model ID
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[More Information Needed] | {"license": "apache-2.0", "library_name": "transformers", "datasets": ["kshitij230/title_generation"], "pipeline_tag": "summarization"} | kshitij230/llama2-title-generation | null | [
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"dataset:kshitij230/title_generation",
"arxiv:1910.09700",
"license:apache-2.0",
"endpoints_compatible",
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] | null | 2024-04-19T19:58:00+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #summarization #dataset-kshitij230/title_generation #arxiv-1910.09700 #license-apache-2.0 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
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## Technical Specifications [optional]
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### Compute Infrastructure
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[optional]
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## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
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feature-extraction | transformers |
# Model Card for Model ID
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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[More Information Needed] | {"library_name": "transformers", "tags": []} | stvhuang/rcr-run-5pqr6lwp-90396-master-0_20240402T105012-ep24 | null | [
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T19:58:05+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #xlm-roberta #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
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## Evaluation
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#### Factors
#### Metrics
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
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- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
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### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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text-classification | transformers |
# Model Card for Model ID
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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#### 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]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | tiya1012/suicidepost_electra | null | [
"transformers",
"safetensors",
"electra",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T20:00:12+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #electra #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
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## How to Get Started with the Model
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## Training Details
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- Hardware Type:
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[optional]
BibTeX:
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## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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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]
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## Uses
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### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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## Evaluation
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[More Information Needed]
### Results
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#### Summary
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[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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### Framework versions
- PEFT 0.10.0 | {"library_name": "peft", "base_model": "unsloth/llama-3-8b-bnb-4bit"} | ResplendentAI/BlueMoon_Llama3 | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"region:us"
] | null | 2024-04-19T20:00:26+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-unsloth/llama-3-8b-bnb-4bit #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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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
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
### Framework versions
- PEFT 0.10.0 | [
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null | transformers |
# Uploaded model
- **Developed by:** dyngnosis
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama 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", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | dyngnosis/llama3-8b-functioncalling | null | [
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"base_model:unsloth/llama-3-8b-bnb-4bit",
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"region:us"
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] | TAGS
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|
# Uploaded model
- Developed by: dyngnosis
- License: apache-2.0
- Finetuned from model : unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
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] |
null | adapter-transformers |
# Adapter `BigTMiami/dapt_tapt_unipelt_helpfulness_classification_adapter` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/amazon_helpfulness](https://huggingface.co/datasets/BigTMiami/amazon_helpfulness/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("BigTMiami/dapt_tapt_unipelt_helpfulness_classification_adapter", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> | {"tags": ["adapter-transformers", "roberta"], "datasets": ["BigTMiami/amazon_helpfulness"]} | BigTMiami/dapt_tapt_unipelt_helpfulness_classification_adapter | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/amazon_helpfulness",
"region:us"
] | null | 2024-04-19T20:02:00+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/amazon_helpfulness #region-us
|
# Adapter 'BigTMiami/dapt_tapt_unipelt_helpfulness_classification_adapter' for roberta-base
An adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_helpfulness dataset and includes a prediction head for classification.
This adapter was created for usage with the Adapters library.
## Usage
First, install 'adapters':
Now, the adapter can be loaded and activated like this:
## Architecture & Training
## Evaluation results
| [
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] |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | zandfj/LLaMA2-7B-Chat-dpo-042002 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T20:03:43+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
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## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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| {"library_name": "transformers", "tags": []} | zzttbrdd/sn6_07l | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-19T20:05:58+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 |
From Fine Tuning To merging a model into a fully working model... Right now this model is still not trained !!
The mini_merge Series are For individual training Regimes Hence Being merged into a single model ; this enable for models to stay focused on a single task producing individual charactaristics ie coding question, gramer , knowledge seeking , chat, roleplay , friendly:
These models are easy enough to train on a laptop !
Hence creating a series for training start points as well as merge start points (ie construct a model with the charactersistic via merge ),
Personally i beleive that a well trained model is the same as a good lora model ; hence merging is a great way to combine qualitys !
once a suitable base model starting point is arrived then previous merge iterations disappear!
Or become specialist merge points .
In general a good chat dataset is what is needed at all times. (in general they refer to the model as a friendly AI (not good for creating a personality !))
To remove such contamination you would have to retrain until its gone but ! ... these are embedded now into models hence beging from scratch with a prefered smaller model for chat and mini tasks ,,, in fact an network is a network as long as it performs well :
Extra large languge models are an myth as they seem to believe that teh larger the model the more data it contains !
In fact data is not stroed in the model !!! Only weights and probabilitys of combinations of words ie conversations so repeatetive entrys can embed information in where as sparse information does not get retreived as much ... hence many simular records which have only small differences hence also doubles are also ok !
as ther are so many ways to say hello!!!
so we need a dataset of at least 1000 hellos and greetings and responses ! to epoch until under 0.5 ie its in the matrix of weights as a probability! when we train other knowelge we may need to return to favrited knowlegde to re mbed this knowledge and reraise its probablity of being returned ie 0.50 is 50% Wring answer 0.45 is 45% wrong answer !!
so for encoding of data on epoch as long as the data averages around these values then it will be retrived if the values are embedded at 0.2 0.001 then it is locked in and maybe a problem as it is favorite !
hence more simular informations
Eventually it will be viable to save all collected data into a folder to be fine tuned into the llm at regular intervals there fore continuing to update its own brain ! by sending it on aresearch it can collect relevant media and on some interval fine tune its weights! (while it is loaded !!!!! and reload After tuning is complete ! or upload!)
### Models Merged
The following models were included in the merge:
* Base_1
* Base_2
* [LeroyDyer/MIni_Merge_Dictionary](https://huggingface.co/LeroyDyer/MIni_Merge_Dictionary)
* [LeroyDyer/Mini_Merge_Dolphin](https://huggingface.co/LeroyDyer/Mini_Merge_Dolphin)
* [LeroyDyer/Mixtral_AI_MiniTron_2b_Chat](https://huggingface.co/LeroyDyer/Mixtral_AI_MiniTron_2b_Chat)
* [LeroyDyer/Mini_Merge_BaseAlignment](https://huggingface.co/LeroyDyer/Mini_Merge_BaseAlignment)
* [LeroyDyer/Mini_Merge_ChatBot](https://huggingface.co/LeroyDyer/Mini_Merge_ChatBot)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: LeroyDyer/Mini_Merge_Dolphin
parameters:
weight: 0.28944
- model: LeroyDyer/MIni_Merge_Dictionary
parameters:
weight: 0.28944
- model: Base_1
parameters:
weight: 0.68944
- model: Base_2
parameters:
weight: 0.68944
- model: LeroyDyer/Mini_Merge_ChatBot
parameters:
weight: 0.8853
- model: LeroyDyer/Mini_Merge_BaseAlignment
parameters:
weight: 0.28944
- model: LeroyDyer/Mixtral_AI_MiniTron_2b_Chat
parameters:
weight: 0.1453
merge_method: linear
dtype: float16
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["LeroyDyer/MIni_Merge_Dictionary", "LeroyDyer/Mini_Merge_Dolphin", "LeroyDyer/Mixtral_AI_MiniTron_2b_Chat", "LeroyDyer/Mini_Merge_BaseAlignment", "LeroyDyer/Mini_Merge_ChatBot"]} | LeroyDyer/Mixtral_AI_Minitron_2b_1.0 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:LeroyDyer/MIni_Merge_Dictionary",
"base_model:LeroyDyer/Mini_Merge_Dolphin",
"base_model:LeroyDyer/Mixtral_AI_MiniTron_2b_Chat",
"base_model:LeroyDyer/Mini_Merge_BaseAlignment",
"base_model:LeroyDyer/Mini_Merge_ChatBot",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-19T20:06:01+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #mergekit #merge #conversational #base_model-LeroyDyer/MIni_Merge_Dictionary #base_model-LeroyDyer/Mini_Merge_Dolphin #base_model-LeroyDyer/Mixtral_AI_MiniTron_2b_Chat #base_model-LeroyDyer/Mini_Merge_BaseAlignment #base_model-LeroyDyer/Mini_Merge_ChatBot #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
From Fine Tuning To merging a model into a fully working model... Right now this model is still not trained !!
The mini_merge Series are For individual training Regimes Hence Being merged into a single model ; this enable for models to stay focused on a single task producing individual charactaristics ie coding question, gramer , knowledge seeking , chat, roleplay , friendly:
These models are easy enough to train on a laptop !
Hence creating a series for training start points as well as merge start points (ie construct a model with the charactersistic via merge ),
Personally i beleive that a well trained model is the same as a good lora model ; hence merging is a great way to combine qualitys !
once a suitable base model starting point is arrived then previous merge iterations disappear!
Or become specialist merge points .
In general a good chat dataset is what is needed at all times. (in general they refer to the model as a friendly AI (not good for creating a personality !))
To remove such contamination you would have to retrain until its gone but ! ... these are embedded now into models hence beging from scratch with a prefered smaller model for chat and mini tasks ,,, in fact an network is a network as long as it performs well :
Extra large languge models are an myth as they seem to believe that teh larger the model the more data it contains !
In fact data is not stroed in the model !!! Only weights and probabilitys of combinations of words ie conversations so repeatetive entrys can embed information in where as sparse information does not get retreived as much ... hence many simular records which have only small differences hence also doubles are also ok !
as ther are so many ways to say hello!!!
so we need a dataset of at least 1000 hellos and greetings and responses ! to epoch until under 0.5 ie its in the matrix of weights as a probability! when we train other knowelge we may need to return to favrited knowlegde to re mbed this knowledge and reraise its probablity of being returned ie 0.50 is 50% Wring answer 0.45 is 45% wrong answer !!
so for encoding of data on epoch as long as the data averages around these values then it will be retrived if the values are embedded at 0.2 0.001 then it is locked in and maybe a problem as it is favorite !
hence more simular informations
Eventually it will be viable to save all collected data into a folder to be fine tuned into the llm at regular intervals there fore continuing to update its own brain ! by sending it on aresearch it can collect relevant media and on some interval fine tune its weights! (while it is loaded !!!!! and reload After tuning is complete ! or upload!)
### Models Merged
The following models were included in the merge:
* Base_1
* Base_2
* LeroyDyer/MIni_Merge_Dictionary
* LeroyDyer/Mini_Merge_Dolphin
* LeroyDyer/Mixtral_AI_MiniTron_2b_Chat
* LeroyDyer/Mini_Merge_BaseAlignment
* LeroyDyer/Mini_Merge_ChatBot
### Configuration
The following YAML configuration was used to produce this model:
| [
"### Models Merged\n\nThe following models were included in the merge:\n* Base_1\n* Base_2\n* LeroyDyer/MIni_Merge_Dictionary\n* LeroyDyer/Mini_Merge_Dolphin\n* LeroyDyer/Mixtral_AI_MiniTron_2b_Chat\n* LeroyDyer/Mini_Merge_BaseAlignment\n* LeroyDyer/Mini_Merge_ChatBot",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #conversational #base_model-LeroyDyer/MIni_Merge_Dictionary #base_model-LeroyDyer/Mini_Merge_Dolphin #base_model-LeroyDyer/Mixtral_AI_MiniTron_2b_Chat #base_model-LeroyDyer/Mini_Merge_BaseAlignment #base_model-LeroyDyer/Mini_Merge_ChatBot #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Models Merged\n\nThe following models were included in the merge:\n* Base_1\n* Base_2\n* LeroyDyer/MIni_Merge_Dictionary\n* LeroyDyer/Mini_Merge_Dolphin\n* LeroyDyer/Mixtral_AI_MiniTron_2b_Chat\n* LeroyDyer/Mini_Merge_BaseAlignment\n* LeroyDyer/Mini_Merge_ChatBot",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] |
text-generation | transformers |
# Uploaded model
- **Developed by:** BarraHome
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama 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", "llama", "trl", "sft"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | BarraHome/llama-3-newborn-4bits | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"region:us"
] | null | 2024-04-19T20:09:49+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #llama #text-generation #text-generation-inference #unsloth #trl #sft #en #base_model-unsloth/llama-3-8b-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #4-bit #region-us
|
# Uploaded model
- Developed by: BarraHome
- License: apache-2.0
- Finetuned from model : unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
| [
"# Uploaded model\n\n- Developed by: BarraHome\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #text-generation-inference #unsloth #trl #sft #en #base_model-unsloth/llama-3-8b-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #4-bit #region-us \n",
"# Uploaded model\n\n- Developed by: BarraHome\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] |
text-generation | null |
# MoMonir/CodeQwen1.5-7B-Chat-GGUF
This model was converted to GGUF format from [`Qwen/CodeQwen1.5-7B-Chat`](https://huggingface.co/Qwen/CodeQwen1.5-7B-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/Qwen/CodeQwen1.5-7B-Chat) for more details on the model.
<!-- README_GGUF.md-about-gguf start -->
### About GGUF ([TheBloke](https://huggingface.co/TheBloke) Description)
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). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
<!-- README_GGUF.md-about-gguf end -->
## 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 MoMonir/CodeQwen1.5-7B-Chat-GGUF --model codeqwen1.5-7b-chat.Q5_K_M.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo MoMonir/CodeQwen1.5-7B-Chat-GGUF --model codeqwen1.5-7b-chat.Q5_K_M.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 codeqwen1.5-7b-chat.Q5_K_M.gguf -n 128
```
| {"language": ["en"], "license": "other", "tags": ["chat", "llama-cpp", "gguf-my-repo"], "license_name": "tongyi-qianwen", "license_link": "https://huggingface.co/Qwen/CodeQwen1.5-7B-Chat/blob/main/LICENSE", "pipeline_tag": "text-generation"} | MoMonir/CodeQwen1.5-7B-Chat-GGUF | null | [
"gguf",
"chat",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"license:other",
"region:us"
] | null | 2024-04-19T20:12:56+00:00 | [] | [
"en"
] | TAGS
#gguf #chat #llama-cpp #gguf-my-repo #text-generation #en #license-other #region-us
|
# MoMonir/CodeQwen1.5-7B-Chat-GGUF
This model was converted to GGUF format from 'Qwen/CodeQwen1.5-7B-Chat' using URL via the URL's GGUF-my-repo space.
Refer to the original model card for more details on the model.
### About GGUF (TheBloke Description)
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. The source project for GGUF. Offers a CLI and a server option.
* text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.
* URL, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
## 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.
| [
"# MoMonir/CodeQwen1.5-7B-Chat-GGUF\nThis model was converted to GGUF format from 'Qwen/CodeQwen1.5-7B-Chat' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.",
"### About GGUF (TheBloke Description)\n\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.\n\nHere is an incomplete list of clients and libraries that are known to support GGUF:\n\n* URL. The source project for GGUF. Offers a CLI and a server option.\n* text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.\n* KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.\n* GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.\n* LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.\n* LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.\n* URL, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.\n* llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.\n* candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.\n* ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.",
"## 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 #chat #llama-cpp #gguf-my-repo #text-generation #en #license-other #region-us \n",
"# MoMonir/CodeQwen1.5-7B-Chat-GGUF\nThis model was converted to GGUF format from 'Qwen/CodeQwen1.5-7B-Chat' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.",
"### About GGUF (TheBloke Description)\n\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.\n\nHere is an incomplete list of clients and libraries that are known to support GGUF:\n\n* URL. The source project for GGUF. Offers a CLI and a server option.\n* text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.\n* KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.\n* GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.\n* LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.\n* LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.\n* URL, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.\n* llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.\n* candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.\n* ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.",
"## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well."
] |
null | transformers |
# Uploaded model
- **Developed by:** BarraHome
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama 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", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | BarraHome/llama-3-newborn-lora | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T20:14:04+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #text-generation-inference #unsloth #llama #trl #en #base_model-unsloth/llama-3-8b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: BarraHome
- License: apache-2.0
- Finetuned from model : unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
| [
"# Uploaded model\n\n- Developed by: BarraHome\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
"TAGS\n#transformers #safetensors #text-generation-inference #unsloth #llama #trl #en #base_model-unsloth/llama-3-8b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Uploaded model\n\n- Developed by: BarraHome\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | nninjun/gpt2-xl-lora-anti-stereoset-v1 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T20:17:28+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
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"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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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"
] | [
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"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"### Direct Use",
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"### Out-of-Scope Use",
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"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
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"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# model_data
This model is a fine-tuned version of [google/canine-c](https://huggingface.co/google/canine-c) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0002
- Accuracy: 1.0000
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0106 | 1.0 | 1500 | 0.1555 | 0.9762 |
| 0.0001 | 2.0 | 3000 | 0.0002 | 1.0000 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.0.0+cu117
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "google/canine-c", "model-index": [{"name": "model_data", "results": []}]} | addykan/model_data | null | [
"transformers",
"tensorboard",
"safetensors",
"canine",
"text-classification",
"generated_from_trainer",
"base_model:google/canine-c",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T20:18:09+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #canine #text-classification #generated_from_trainer #base_model-google/canine-c #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| model\_data
===========
This model is a fine-tuned version of google/canine-c on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0002
* Accuracy: 1.0000
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: 2
### Training results
### Framework versions
* Transformers 4.40.0
* Pytorch 2.0.0+cu117
* Datasets 2.19.0
* Tokenizers 0.19.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.0.0+cu117\n* Datasets 2.19.0\n* Tokenizers 0.19.1"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.0.0+cu117\n* Datasets 2.19.0\n* Tokenizers 0.19.1"
] |
text-to-image | diffusers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🧨 diffusers 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]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
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[More Information Needed]
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[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "diffusers"} | rubbrband/realisticVisionV60B1_v51VAE | null | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | null | 2024-04-19T20:20:12+00:00 | [
"1910.09700"
] | [] | TAGS
#diffusers #safetensors #arxiv-1910.09700 #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a diffusers 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 diffusers 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#diffusers #safetensors #arxiv-1910.09700 #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a diffusers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
null | null |
# CalmexperimentM7-7B
CalmexperimentM7-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration.
* [liminerity/M7-7b](https://huggingface.co/liminerity/M7-7b)
## 🧩 Configuration
```yaml
models:
- model: allknowingroger/CalmExperiment-7B-slerp
# No parameters necessary for base model
- model: liminerity/M7-7b
parameters:
density: 0.53
weight: 0.6
merge_method: dare_ties
base_model: allknowingroger/CalmExperiment-7B-slerp
parameters:
int8_mask: true
dtype: bfloat16
random_seed: 0
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "automerger/CalmexperimentM7-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "automerger"], "base_model": ["liminerity/M7-7b"]} | automerger/CalmexperimentM7-7B | null | [
"merge",
"mergekit",
"lazymergekit",
"automerger",
"base_model:liminerity/M7-7b",
"license:apache-2.0",
"region:us"
] | null | 2024-04-19T20:21:41+00:00 | [] | [] | TAGS
#merge #mergekit #lazymergekit #automerger #base_model-liminerity/M7-7b #license-apache-2.0 #region-us
|
# CalmexperimentM7-7B
CalmexperimentM7-7B is an automated merge created by Maxime Labonne using the following configuration.
* liminerity/M7-7b
## Configuration
## Usage
| [
"# CalmexperimentM7-7B\n\nCalmexperimentM7-7B is an automated merge created by Maxime Labonne using the following configuration.\n* liminerity/M7-7b",
"## Configuration",
"## Usage"
] | [
"TAGS\n#merge #mergekit #lazymergekit #automerger #base_model-liminerity/M7-7b #license-apache-2.0 #region-us \n",
"# CalmexperimentM7-7B\n\nCalmexperimentM7-7B is an automated merge created by Maxime Labonne using the following configuration.\n* liminerity/M7-7b",
"## Configuration",
"## Usage"
] |
null | transformers |
# Uploaded model
- **Developed by:** karsar
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama 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", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | karsar/Llama3_8B_finetune | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T20:21:54+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #text-generation-inference #unsloth #llama #trl #en #base_model-unsloth/llama-3-8b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: karsar
- License: apache-2.0
- Finetuned from model : unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
| [
"# Uploaded model\n\n- Developed by: karsar\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
"TAGS\n#transformers #safetensors #text-generation-inference #unsloth #llama #trl #en #base_model-unsloth/llama-3-8b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Uploaded model\n\n- Developed by: karsar\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] |
token-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. -->
# output_LiLT_test_01
This model is a fine-tuned version of [nielsr/lilt-xlm-roberta-base](https://huggingface.co/nielsr/lilt-xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3152
- Precision: 0.8023
- Recall: 0.8270
- F1: 0.8145
- Accuracy: 0.9639
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.08 | 100 | 0.5536 | 0.0255 | 0.0020 | 0.0037 | 0.8730 |
| No log | 0.16 | 200 | 0.3587 | 0.2042 | 0.0957 | 0.1303 | 0.8865 |
| No log | 0.24 | 300 | 0.2894 | 0.4203 | 0.5526 | 0.4775 | 0.9087 |
| No log | 0.32 | 400 | 0.2185 | 0.6486 | 0.6567 | 0.6526 | 0.9390 |
| 0.3252 | 0.4 | 500 | 0.1703 | 0.6833 | 0.6959 | 0.6895 | 0.9473 |
| 0.3252 | 0.49 | 600 | 0.1698 | 0.6855 | 0.7334 | 0.7086 | 0.9482 |
| 0.3252 | 0.57 | 700 | 0.1748 | 0.6449 | 0.7999 | 0.7141 | 0.9447 |
| 0.3252 | 0.65 | 800 | 0.1693 | 0.7038 | 0.7870 | 0.7431 | 0.9522 |
| 0.3252 | 0.73 | 900 | 0.1639 | 0.7002 | 0.7916 | 0.7431 | 0.9532 |
| 0.0997 | 0.81 | 1000 | 0.1474 | 0.7408 | 0.7521 | 0.7464 | 0.9573 |
| 0.0997 | 0.89 | 1100 | 0.1518 | 0.7588 | 0.7489 | 0.7538 | 0.9549 |
| 0.0997 | 0.97 | 1200 | 0.1459 | 0.7313 | 0.7847 | 0.7571 | 0.9587 |
| 0.0997 | 1.05 | 1300 | 0.1561 | 0.7347 | 0.7544 | 0.7444 | 0.9552 |
| 0.0997 | 1.13 | 1400 | 0.1583 | 0.7685 | 0.7875 | 0.7779 | 0.9586 |
| 0.0805 | 1.21 | 1500 | 0.1658 | 0.6876 | 0.7835 | 0.7324 | 0.9487 |
| 0.0805 | 1.29 | 1600 | 0.1548 | 0.7470 | 0.7550 | 0.7510 | 0.9556 |
| 0.0805 | 1.37 | 1700 | 0.1416 | 0.7753 | 0.7976 | 0.7863 | 0.9604 |
| 0.0805 | 1.46 | 1800 | 0.1802 | 0.8107 | 0.7319 | 0.7693 | 0.9561 |
| 0.0805 | 1.54 | 1900 | 0.1553 | 0.7476 | 0.8146 | 0.7797 | 0.9587 |
| 0.0665 | 1.62 | 2000 | 0.1237 | 0.7692 | 0.8002 | 0.7844 | 0.9614 |
| 0.0665 | 1.7 | 2100 | 0.1504 | 0.7023 | 0.7945 | 0.7456 | 0.9543 |
| 0.0665 | 1.78 | 2200 | 0.1360 | 0.7920 | 0.8066 | 0.7992 | 0.9629 |
| 0.0665 | 1.86 | 2300 | 0.1538 | 0.7032 | 0.7881 | 0.7432 | 0.9520 |
| 0.0665 | 1.94 | 2400 | 0.1374 | 0.7559 | 0.8025 | 0.7785 | 0.9603 |
| 0.0624 | 2.02 | 2500 | 0.1346 | 0.7738 | 0.8213 | 0.7968 | 0.9610 |
| 0.0624 | 2.1 | 2600 | 0.1453 | 0.7381 | 0.8247 | 0.7790 | 0.9568 |
| 0.0624 | 2.18 | 2700 | 0.1525 | 0.7615 | 0.8138 | 0.7868 | 0.9600 |
| 0.0624 | 2.26 | 2800 | 0.1515 | 0.7387 | 0.8020 | 0.7690 | 0.9558 |
| 0.0624 | 2.34 | 2900 | 0.1307 | 0.7695 | 0.7968 | 0.7829 | 0.9601 |
| 0.0548 | 2.43 | 3000 | 0.1371 | 0.7761 | 0.7873 | 0.7816 | 0.9611 |
| 0.0548 | 2.51 | 3100 | 0.1365 | 0.7246 | 0.8426 | 0.7792 | 0.9567 |
| 0.0548 | 2.59 | 3200 | 0.1158 | 0.8012 | 0.8144 | 0.8077 | 0.9660 |
| 0.0548 | 2.67 | 3300 | 0.1259 | 0.8211 | 0.8057 | 0.8133 | 0.9647 |
| 0.0548 | 2.75 | 3400 | 0.1561 | 0.8310 | 0.7440 | 0.7851 | 0.9611 |
| 0.0521 | 2.83 | 3500 | 0.1433 | 0.7956 | 0.7956 | 0.7956 | 0.9629 |
| 0.0521 | 2.91 | 3600 | 0.1508 | 0.7838 | 0.7985 | 0.7911 | 0.9603 |
| 0.0521 | 2.99 | 3700 | 0.1533 | 0.7949 | 0.7867 | 0.7908 | 0.9622 |
| 0.0521 | 3.07 | 3800 | 0.1739 | 0.7361 | 0.8129 | 0.7726 | 0.9507 |
| 0.0521 | 3.15 | 3900 | 0.1421 | 0.8128 | 0.8011 | 0.8069 | 0.9639 |
| 0.0504 | 3.23 | 4000 | 0.1371 | 0.7739 | 0.8161 | 0.7944 | 0.9626 |
| 0.0504 | 3.31 | 4100 | 0.1540 | 0.7464 | 0.8155 | 0.7794 | 0.9583 |
| 0.0504 | 3.4 | 4200 | 0.2071 | 0.7787 | 0.7850 | 0.7818 | 0.9545 |
| 0.0504 | 3.48 | 4300 | 0.1693 | 0.8328 | 0.7740 | 0.8023 | 0.9635 |
| 0.0504 | 3.56 | 4400 | 0.1670 | 0.7478 | 0.8170 | 0.7808 | 0.9559 |
| 0.0408 | 3.64 | 4500 | 0.1507 | 0.7267 | 0.8510 | 0.7840 | 0.9547 |
| 0.0408 | 3.72 | 4600 | 0.1477 | 0.7625 | 0.8285 | 0.7941 | 0.9612 |
| 0.0408 | 3.8 | 4700 | 0.1326 | 0.8010 | 0.8204 | 0.8106 | 0.9645 |
| 0.0408 | 3.88 | 4800 | 0.1280 | 0.7863 | 0.8409 | 0.8126 | 0.9648 |
| 0.0408 | 3.96 | 4900 | 0.1376 | 0.7673 | 0.8184 | 0.7920 | 0.9617 |
| 0.0463 | 4.04 | 5000 | 0.1396 | 0.7560 | 0.8415 | 0.7965 | 0.9617 |
| 0.0463 | 4.12 | 5100 | 0.1232 | 0.7809 | 0.8187 | 0.7993 | 0.9633 |
| 0.0463 | 4.2 | 5200 | 0.1322 | 0.8039 | 0.8164 | 0.8101 | 0.9651 |
| 0.0463 | 4.28 | 5300 | 0.1428 | 0.7891 | 0.8426 | 0.8150 | 0.9641 |
| 0.0463 | 4.37 | 5400 | 0.1669 | 0.7783 | 0.8331 | 0.8048 | 0.9592 |
| 0.0383 | 4.45 | 5500 | 0.1419 | 0.7843 | 0.8282 | 0.8057 | 0.9645 |
| 0.0383 | 4.53 | 5600 | 0.1398 | 0.7855 | 0.8118 | 0.7984 | 0.9613 |
| 0.0383 | 4.61 | 5700 | 0.1392 | 0.7703 | 0.7861 | 0.7781 | 0.9613 |
| 0.0383 | 4.69 | 5800 | 0.1341 | 0.8025 | 0.8175 | 0.8099 | 0.9646 |
| 0.0383 | 4.77 | 5900 | 0.1432 | 0.7931 | 0.8123 | 0.8026 | 0.9623 |
| 0.0434 | 4.85 | 6000 | 0.1434 | 0.8266 | 0.7956 | 0.8108 | 0.9666 |
| 0.0434 | 4.93 | 6100 | 0.1448 | 0.8051 | 0.8014 | 0.8032 | 0.9634 |
| 0.0434 | 5.01 | 6200 | 0.1496 | 0.7687 | 0.8342 | 0.8001 | 0.9615 |
| 0.0434 | 5.09 | 6300 | 0.1437 | 0.8187 | 0.8071 | 0.8129 | 0.9647 |
| 0.0434 | 5.17 | 6400 | 0.1539 | 0.7499 | 0.8244 | 0.7854 | 0.9595 |
| 0.0329 | 5.25 | 6500 | 0.1888 | 0.7268 | 0.8458 | 0.7818 | 0.9538 |
| 0.0329 | 5.34 | 6600 | 0.1573 | 0.7591 | 0.8210 | 0.7888 | 0.9572 |
| 0.0329 | 5.42 | 6700 | 0.1348 | 0.7916 | 0.8288 | 0.8097 | 0.9656 |
| 0.0329 | 5.5 | 6800 | 0.1405 | 0.7692 | 0.8244 | 0.7959 | 0.9617 |
| 0.0329 | 5.58 | 6900 | 0.1410 | 0.8133 | 0.8175 | 0.8154 | 0.9660 |
| 0.036 | 5.66 | 7000 | 0.1574 | 0.7990 | 0.8363 | 0.8172 | 0.9635 |
| 0.036 | 5.74 | 7100 | 0.1446 | 0.8103 | 0.8092 | 0.8098 | 0.9670 |
| 0.036 | 5.82 | 7200 | 0.1465 | 0.7681 | 0.7936 | 0.7807 | 0.9585 |
| 0.036 | 5.9 | 7300 | 0.1339 | 0.8027 | 0.8138 | 0.8082 | 0.9648 |
| 0.036 | 5.98 | 7400 | 0.1329 | 0.7989 | 0.8201 | 0.8094 | 0.9660 |
| 0.0367 | 6.06 | 7500 | 0.1422 | 0.7892 | 0.8181 | 0.8034 | 0.9652 |
| 0.0367 | 6.14 | 7600 | 0.1782 | 0.7760 | 0.8178 | 0.7964 | 0.9591 |
| 0.0367 | 6.22 | 7700 | 0.1418 | 0.7793 | 0.8328 | 0.8052 | 0.9614 |
| 0.0367 | 6.31 | 7800 | 0.1569 | 0.7720 | 0.7899 | 0.7808 | 0.9602 |
| 0.0367 | 6.39 | 7900 | 0.1456 | 0.8021 | 0.8328 | 0.8171 | 0.9632 |
| 0.0329 | 6.47 | 8000 | 0.1513 | 0.7954 | 0.8227 | 0.8088 | 0.9648 |
| 0.0329 | 6.55 | 8100 | 0.1446 | 0.8010 | 0.8239 | 0.8123 | 0.9647 |
| 0.0329 | 6.63 | 8200 | 0.1532 | 0.7911 | 0.8132 | 0.8020 | 0.9601 |
| 0.0329 | 6.71 | 8300 | 0.1524 | 0.7971 | 0.8043 | 0.8007 | 0.9632 |
| 0.0329 | 6.79 | 8400 | 0.1375 | 0.7869 | 0.8112 | 0.7989 | 0.9638 |
| 0.0297 | 6.87 | 8500 | 0.1584 | 0.8097 | 0.8155 | 0.8126 | 0.9649 |
| 0.0297 | 6.95 | 8600 | 0.1406 | 0.7890 | 0.8279 | 0.8080 | 0.9631 |
| 0.0297 | 7.03 | 8700 | 0.1605 | 0.7687 | 0.8276 | 0.7971 | 0.9615 |
| 0.0297 | 7.11 | 8800 | 0.1535 | 0.7571 | 0.8400 | 0.7964 | 0.9605 |
| 0.0297 | 7.19 | 8900 | 0.1527 | 0.7949 | 0.8233 | 0.8088 | 0.9649 |
| 0.0302 | 7.28 | 9000 | 0.1457 | 0.8183 | 0.8311 | 0.8247 | 0.968 |
| 0.0302 | 7.36 | 9100 | 0.1582 | 0.7588 | 0.8432 | 0.7987 | 0.9600 |
| 0.0302 | 7.44 | 9200 | 0.1473 | 0.8267 | 0.8322 | 0.8295 | 0.9685 |
| 0.0302 | 7.52 | 9300 | 0.1351 | 0.8017 | 0.8403 | 0.8205 | 0.9663 |
| 0.0302 | 7.6 | 9400 | 0.1535 | 0.8116 | 0.8380 | 0.8246 | 0.9684 |
| 0.0273 | 7.68 | 9500 | 0.1322 | 0.8001 | 0.8201 | 0.8100 | 0.9654 |
| 0.0273 | 7.76 | 9600 | 0.1563 | 0.7742 | 0.8400 | 0.8058 | 0.9629 |
| 0.0273 | 7.84 | 9700 | 0.1739 | 0.7492 | 0.8492 | 0.7961 | 0.9560 |
| 0.0273 | 7.92 | 9800 | 0.1654 | 0.8122 | 0.8279 | 0.8200 | 0.9638 |
| 0.0273 | 8.0 | 9900 | 0.1663 | 0.7798 | 0.8380 | 0.8078 | 0.9614 |
| 0.0301 | 8.08 | 10000 | 0.1609 | 0.8256 | 0.8230 | 0.8243 | 0.9663 |
| 0.0301 | 8.16 | 10100 | 0.1704 | 0.7858 | 0.8449 | 0.8143 | 0.9620 |
| 0.0301 | 8.25 | 10200 | 0.1551 | 0.7984 | 0.8299 | 0.8139 | 0.9644 |
| 0.0301 | 8.33 | 10300 | 0.1598 | 0.8017 | 0.8334 | 0.8172 | 0.9630 |
| 0.0301 | 8.41 | 10400 | 0.1662 | 0.8084 | 0.8164 | 0.8124 | 0.9638 |
| 0.0252 | 8.49 | 10500 | 0.1876 | 0.7432 | 0.8351 | 0.7865 | 0.9550 |
| 0.0252 | 8.57 | 10600 | 0.1724 | 0.7866 | 0.8351 | 0.8101 | 0.9614 |
| 0.0252 | 8.65 | 10700 | 0.1931 | 0.7352 | 0.8458 | 0.7866 | 0.9491 |
| 0.0252 | 8.73 | 10800 | 0.1727 | 0.7968 | 0.8296 | 0.8129 | 0.9641 |
| 0.0252 | 8.81 | 10900 | 0.1655 | 0.7640 | 0.8380 | 0.7993 | 0.9588 |
| 0.0256 | 8.89 | 11000 | 0.1527 | 0.8191 | 0.8351 | 0.8270 | 0.9672 |
| 0.0256 | 8.97 | 11100 | 0.1542 | 0.8048 | 0.8239 | 0.8142 | 0.9640 |
| 0.0256 | 9.05 | 11200 | 0.1713 | 0.8139 | 0.8146 | 0.8143 | 0.9636 |
| 0.0256 | 9.14 | 11300 | 0.1654 | 0.8130 | 0.8221 | 0.8175 | 0.9645 |
| 0.0256 | 9.22 | 11400 | 0.1712 | 0.7757 | 0.8342 | 0.8039 | 0.9618 |
| 0.0253 | 9.3 | 11500 | 0.1575 | 0.7724 | 0.8268 | 0.7987 | 0.9615 |
| 0.0253 | 9.38 | 11600 | 0.1697 | 0.8018 | 0.8233 | 0.8124 | 0.9648 |
| 0.0253 | 9.46 | 11700 | 0.1506 | 0.7798 | 0.8259 | 0.8022 | 0.9618 |
| 0.0253 | 9.54 | 11800 | 0.1686 | 0.8034 | 0.8331 | 0.8180 | 0.9649 |
| 0.0253 | 9.62 | 11900 | 0.1863 | 0.7939 | 0.8227 | 0.8080 | 0.9624 |
| 0.0215 | 9.7 | 12000 | 0.1794 | 0.8056 | 0.8233 | 0.8144 | 0.9644 |
| 0.0215 | 9.78 | 12100 | 0.1746 | 0.8083 | 0.8288 | 0.8184 | 0.9644 |
| 0.0215 | 9.86 | 12200 | 0.1668 | 0.8112 | 0.8247 | 0.8179 | 0.9647 |
| 0.0215 | 9.94 | 12300 | 0.1792 | 0.7835 | 0.8357 | 0.8088 | 0.9597 |
| 0.0215 | 10.02 | 12400 | 0.2200 | 0.7550 | 0.8386 | 0.7946 | 0.9575 |
| 0.0202 | 10.11 | 12500 | 0.1842 | 0.7772 | 0.8317 | 0.8035 | 0.9566 |
| 0.0202 | 10.19 | 12600 | 0.1930 | 0.7816 | 0.8149 | 0.7979 | 0.9617 |
| 0.0202 | 10.27 | 12700 | 0.1894 | 0.7615 | 0.8458 | 0.8014 | 0.9571 |
| 0.0202 | 10.35 | 12800 | 0.1711 | 0.8302 | 0.8103 | 0.8201 | 0.9661 |
| 0.0202 | 10.43 | 12900 | 0.1981 | 0.7802 | 0.8063 | 0.7930 | 0.9581 |
| 0.021 | 10.51 | 13000 | 0.1586 | 0.8045 | 0.8317 | 0.8179 | 0.9643 |
| 0.021 | 10.59 | 13100 | 0.1589 | 0.8136 | 0.8155 | 0.8146 | 0.9657 |
| 0.021 | 10.67 | 13200 | 0.1758 | 0.7997 | 0.8242 | 0.8118 | 0.9640 |
| 0.021 | 10.75 | 13300 | 0.1862 | 0.8046 | 0.8299 | 0.8171 | 0.9635 |
| 0.021 | 10.83 | 13400 | 0.1854 | 0.7864 | 0.8403 | 0.8124 | 0.9628 |
| 0.0205 | 10.91 | 13500 | 0.1815 | 0.8168 | 0.8291 | 0.8229 | 0.9655 |
| 0.0205 | 10.99 | 13600 | 0.2104 | 0.8244 | 0.7838 | 0.8036 | 0.9627 |
| 0.0205 | 11.08 | 13700 | 0.1783 | 0.7719 | 0.8273 | 0.7987 | 0.9595 |
| 0.0205 | 11.16 | 13800 | 0.1911 | 0.8176 | 0.8178 | 0.8177 | 0.9656 |
| 0.0205 | 11.24 | 13900 | 0.1989 | 0.8072 | 0.8317 | 0.8193 | 0.9643 |
| 0.0194 | 11.32 | 14000 | 0.1959 | 0.7926 | 0.8394 | 0.8153 | 0.9627 |
| 0.0194 | 11.4 | 14100 | 0.1686 | 0.7977 | 0.8420 | 0.8192 | 0.9642 |
| 0.0194 | 11.48 | 14200 | 0.1818 | 0.8224 | 0.8074 | 0.8148 | 0.9658 |
| 0.0194 | 11.56 | 14300 | 0.1841 | 0.7470 | 0.8426 | 0.7919 | 0.9549 |
| 0.0194 | 11.64 | 14400 | 0.1736 | 0.7826 | 0.8458 | 0.8130 | 0.9624 |
| 0.0187 | 11.72 | 14500 | 0.1937 | 0.8026 | 0.8204 | 0.8114 | 0.9614 |
| 0.0187 | 11.8 | 14600 | 0.1893 | 0.8113 | 0.8366 | 0.8237 | 0.9643 |
| 0.0187 | 11.88 | 14700 | 0.1721 | 0.8173 | 0.8345 | 0.8258 | 0.9663 |
| 0.0187 | 11.96 | 14800 | 0.1881 | 0.8018 | 0.8325 | 0.8169 | 0.9642 |
| 0.0187 | 12.05 | 14900 | 0.2184 | 0.7770 | 0.8417 | 0.8081 | 0.9589 |
| 0.0168 | 12.13 | 15000 | 0.1992 | 0.7864 | 0.8288 | 0.8070 | 0.9602 |
| 0.0168 | 12.21 | 15100 | 0.2041 | 0.8273 | 0.7829 | 0.8045 | 0.9641 |
| 0.0168 | 12.29 | 15200 | 0.2166 | 0.7893 | 0.8250 | 0.8068 | 0.9584 |
| 0.0168 | 12.37 | 15300 | 0.1868 | 0.7712 | 0.8423 | 0.8052 | 0.9577 |
| 0.0168 | 12.45 | 15400 | 0.1777 | 0.8016 | 0.8351 | 0.8180 | 0.9640 |
| 0.019 | 12.53 | 15500 | 0.1847 | 0.7896 | 0.8158 | 0.8025 | 0.9620 |
| 0.019 | 12.61 | 15600 | 0.2094 | 0.8033 | 0.8100 | 0.8067 | 0.9632 |
| 0.019 | 12.69 | 15700 | 0.1953 | 0.7992 | 0.8351 | 0.8167 | 0.9630 |
| 0.019 | 12.77 | 15800 | 0.1735 | 0.7944 | 0.8106 | 0.8024 | 0.9605 |
| 0.019 | 12.85 | 15900 | 0.2150 | 0.7750 | 0.8501 | 0.8108 | 0.9621 |
| 0.0163 | 12.93 | 16000 | 0.1975 | 0.7828 | 0.8374 | 0.8092 | 0.9624 |
| 0.0163 | 13.02 | 16100 | 0.1829 | 0.8207 | 0.8363 | 0.8284 | 0.9668 |
| 0.0163 | 13.1 | 16200 | 0.1872 | 0.8293 | 0.8037 | 0.8163 | 0.9660 |
| 0.0163 | 13.18 | 16300 | 0.1876 | 0.8022 | 0.8244 | 0.8132 | 0.9642 |
| 0.0163 | 13.26 | 16400 | 0.1890 | 0.7981 | 0.8374 | 0.8173 | 0.9646 |
| 0.0142 | 13.34 | 16500 | 0.2230 | 0.7739 | 0.8357 | 0.8036 | 0.9605 |
| 0.0142 | 13.42 | 16600 | 0.1933 | 0.8082 | 0.8103 | 0.8093 | 0.9629 |
| 0.0142 | 13.5 | 16700 | 0.1963 | 0.7937 | 0.8305 | 0.8117 | 0.9610 |
| 0.0142 | 13.58 | 16800 | 0.2178 | 0.7847 | 0.8299 | 0.8067 | 0.9593 |
| 0.0142 | 13.66 | 16900 | 0.2074 | 0.7814 | 0.8449 | 0.8119 | 0.9615 |
| 0.0167 | 13.74 | 17000 | 0.2265 | 0.7561 | 0.8489 | 0.7998 | 0.9582 |
| 0.0167 | 13.82 | 17100 | 0.2205 | 0.7982 | 0.8242 | 0.8109 | 0.9627 |
| 0.0167 | 13.9 | 17200 | 0.1883 | 0.7718 | 0.8305 | 0.8001 | 0.9608 |
| 0.0167 | 13.99 | 17300 | 0.2138 | 0.7837 | 0.8282 | 0.8053 | 0.9613 |
| 0.0167 | 14.07 | 17400 | 0.2117 | 0.7815 | 0.8322 | 0.8061 | 0.9635 |
| 0.0154 | 14.15 | 17500 | 0.2053 | 0.8065 | 0.8170 | 0.8117 | 0.9639 |
| 0.0154 | 14.23 | 17600 | 0.2164 | 0.7921 | 0.8115 | 0.8017 | 0.9609 |
| 0.0154 | 14.31 | 17700 | 0.2117 | 0.7618 | 0.8351 | 0.7968 | 0.9568 |
| 0.0154 | 14.39 | 17800 | 0.2287 | 0.7709 | 0.8351 | 0.8017 | 0.9585 |
| 0.0154 | 14.47 | 17900 | 0.2074 | 0.7969 | 0.8383 | 0.8171 | 0.9639 |
| 0.0152 | 14.55 | 18000 | 0.2111 | 0.7571 | 0.8455 | 0.7989 | 0.9598 |
| 0.0152 | 14.63 | 18100 | 0.2091 | 0.7995 | 0.8288 | 0.8139 | 0.9639 |
| 0.0152 | 14.71 | 18200 | 0.2146 | 0.7787 | 0.8389 | 0.8077 | 0.9602 |
| 0.0152 | 14.79 | 18300 | 0.1898 | 0.7884 | 0.8129 | 0.8005 | 0.9604 |
| 0.0152 | 14.87 | 18400 | 0.1974 | 0.7912 | 0.8106 | 0.8008 | 0.9602 |
| 0.0133 | 14.96 | 18500 | 0.1950 | 0.8191 | 0.8273 | 0.8232 | 0.9644 |
| 0.0133 | 15.04 | 18600 | 0.2113 | 0.8119 | 0.8311 | 0.8214 | 0.9640 |
| 0.0133 | 15.12 | 18700 | 0.2172 | 0.7726 | 0.8435 | 0.8065 | 0.9592 |
| 0.0133 | 15.2 | 18800 | 0.2290 | 0.7450 | 0.8464 | 0.7924 | 0.9542 |
| 0.0133 | 15.28 | 18900 | 0.2200 | 0.7924 | 0.8363 | 0.8137 | 0.9630 |
| 0.0135 | 15.36 | 19000 | 0.2064 | 0.8168 | 0.8279 | 0.8223 | 0.9657 |
| 0.0135 | 15.44 | 19100 | 0.2244 | 0.8249 | 0.8296 | 0.8272 | 0.9655 |
| 0.0135 | 15.52 | 19200 | 0.2271 | 0.8134 | 0.8141 | 0.8137 | 0.9649 |
| 0.0135 | 15.6 | 19300 | 0.2167 | 0.8099 | 0.8242 | 0.8170 | 0.9644 |
| 0.0135 | 15.68 | 19400 | 0.2142 | 0.8020 | 0.8265 | 0.8140 | 0.9632 |
| 0.0125 | 15.76 | 19500 | 0.2040 | 0.8106 | 0.8253 | 0.8179 | 0.9648 |
| 0.0125 | 15.84 | 19600 | 0.2035 | 0.8216 | 0.8207 | 0.8212 | 0.9660 |
| 0.0125 | 15.93 | 19700 | 0.2160 | 0.7937 | 0.8317 | 0.8122 | 0.9640 |
| 0.0125 | 16.01 | 19800 | 0.2110 | 0.7947 | 0.8314 | 0.8126 | 0.9622 |
| 0.0125 | 16.09 | 19900 | 0.2296 | 0.8034 | 0.8233 | 0.8132 | 0.9641 |
| 0.0115 | 16.17 | 20000 | 0.2127 | 0.8007 | 0.8291 | 0.8146 | 0.9645 |
| 0.0115 | 16.25 | 20100 | 0.2268 | 0.8 | 0.8198 | 0.8098 | 0.9636 |
| 0.0115 | 16.33 | 20200 | 0.2427 | 0.7904 | 0.8314 | 0.8103 | 0.9617 |
| 0.0115 | 16.41 | 20300 | 0.2247 | 0.7629 | 0.8412 | 0.8001 | 0.9578 |
| 0.0115 | 16.49 | 20400 | 0.2320 | 0.8106 | 0.8227 | 0.8166 | 0.9639 |
| 0.0106 | 16.57 | 20500 | 0.2021 | 0.8103 | 0.8187 | 0.8145 | 0.9643 |
| 0.0106 | 16.65 | 20600 | 0.2328 | 0.7668 | 0.8426 | 0.8029 | 0.9592 |
| 0.0106 | 16.73 | 20700 | 0.2084 | 0.8073 | 0.8380 | 0.8223 | 0.9645 |
| 0.0106 | 16.81 | 20800 | 0.1896 | 0.7807 | 0.8314 | 0.8052 | 0.9622 |
| 0.0106 | 16.9 | 20900 | 0.2005 | 0.8008 | 0.8308 | 0.8155 | 0.9652 |
| 0.0124 | 16.98 | 21000 | 0.2114 | 0.8084 | 0.8210 | 0.8146 | 0.9656 |
| 0.0124 | 17.06 | 21100 | 0.2327 | 0.7932 | 0.8357 | 0.8139 | 0.9628 |
| 0.0124 | 17.14 | 21200 | 0.2311 | 0.8190 | 0.8071 | 0.8130 | 0.9648 |
| 0.0124 | 17.22 | 21300 | 0.2222 | 0.7904 | 0.8394 | 0.8142 | 0.9626 |
| 0.0124 | 17.3 | 21400 | 0.2147 | 0.8003 | 0.8236 | 0.8118 | 0.9624 |
| 0.0093 | 17.38 | 21500 | 0.2409 | 0.8167 | 0.8285 | 0.8226 | 0.9655 |
| 0.0093 | 17.46 | 21600 | 0.2183 | 0.7792 | 0.8250 | 0.8015 | 0.9609 |
| 0.0093 | 17.54 | 21700 | 0.2159 | 0.7962 | 0.8242 | 0.8099 | 0.9628 |
| 0.0093 | 17.62 | 21800 | 0.2046 | 0.8061 | 0.8279 | 0.8168 | 0.9661 |
| 0.0093 | 17.7 | 21900 | 0.2116 | 0.8003 | 0.8377 | 0.8186 | 0.9652 |
| 0.0106 | 17.78 | 22000 | 0.2069 | 0.8102 | 0.8354 | 0.8226 | 0.9651 |
| 0.0106 | 17.87 | 22100 | 0.2276 | 0.8134 | 0.8181 | 0.8158 | 0.9652 |
| 0.0106 | 17.95 | 22200 | 0.2091 | 0.8239 | 0.8172 | 0.8205 | 0.9661 |
| 0.0106 | 18.03 | 22300 | 0.2295 | 0.7801 | 0.8354 | 0.8068 | 0.9629 |
| 0.0106 | 18.11 | 22400 | 0.2248 | 0.7851 | 0.8224 | 0.8033 | 0.9629 |
| 0.0104 | 18.19 | 22500 | 0.2403 | 0.7866 | 0.8244 | 0.8051 | 0.9630 |
| 0.0104 | 18.27 | 22600 | 0.2352 | 0.8004 | 0.8161 | 0.8082 | 0.9637 |
| 0.0104 | 18.35 | 22700 | 0.2164 | 0.8002 | 0.8279 | 0.8138 | 0.9644 |
| 0.0104 | 18.43 | 22800 | 0.2123 | 0.7934 | 0.8256 | 0.8092 | 0.9644 |
| 0.0104 | 18.51 | 22900 | 0.2195 | 0.7788 | 0.8394 | 0.8080 | 0.9623 |
| 0.0086 | 18.59 | 23000 | 0.2060 | 0.8130 | 0.8273 | 0.8201 | 0.9651 |
| 0.0086 | 18.67 | 23100 | 0.2147 | 0.8034 | 0.8282 | 0.8156 | 0.9648 |
| 0.0086 | 18.76 | 23200 | 0.2261 | 0.8036 | 0.8337 | 0.8183 | 0.9639 |
| 0.0086 | 18.84 | 23300 | 0.2202 | 0.8004 | 0.8380 | 0.8188 | 0.9638 |
| 0.0086 | 18.92 | 23400 | 0.2267 | 0.8110 | 0.8299 | 0.8203 | 0.9641 |
| 0.0093 | 19.0 | 23500 | 0.2191 | 0.8045 | 0.8256 | 0.8149 | 0.9632 |
| 0.0093 | 19.08 | 23600 | 0.2492 | 0.8098 | 0.8247 | 0.8172 | 0.9641 |
| 0.0093 | 19.16 | 23700 | 0.2427 | 0.8176 | 0.8207 | 0.8192 | 0.9648 |
| 0.0093 | 19.24 | 23800 | 0.2353 | 0.8009 | 0.8314 | 0.8158 | 0.9640 |
| 0.0093 | 19.32 | 23900 | 0.2431 | 0.8343 | 0.8071 | 0.8205 | 0.9665 |
| 0.0092 | 19.4 | 24000 | 0.2178 | 0.8098 | 0.8123 | 0.8111 | 0.9646 |
| 0.0092 | 19.48 | 24100 | 0.2238 | 0.8097 | 0.8020 | 0.8058 | 0.9634 |
| 0.0092 | 19.56 | 24200 | 0.2322 | 0.8044 | 0.8334 | 0.8186 | 0.9644 |
| 0.0092 | 19.64 | 24300 | 0.2368 | 0.8009 | 0.8374 | 0.8188 | 0.9643 |
| 0.0092 | 19.73 | 24400 | 0.2306 | 0.8165 | 0.8299 | 0.8232 | 0.9652 |
| 0.0076 | 19.81 | 24500 | 0.2257 | 0.8040 | 0.8299 | 0.8167 | 0.9641 |
| 0.0076 | 19.89 | 24600 | 0.2365 | 0.8117 | 0.8291 | 0.8203 | 0.9649 |
| 0.0076 | 19.97 | 24700 | 0.2439 | 0.8088 | 0.8230 | 0.8158 | 0.9647 |
| 0.0076 | 20.05 | 24800 | 0.2487 | 0.7994 | 0.8354 | 0.8170 | 0.9636 |
| 0.0076 | 20.13 | 24900 | 0.2468 | 0.8033 | 0.8276 | 0.8153 | 0.9646 |
| 0.0072 | 20.21 | 25000 | 0.2571 | 0.7922 | 0.8299 | 0.8106 | 0.9631 |
| 0.0072 | 20.29 | 25100 | 0.2498 | 0.7881 | 0.8429 | 0.8146 | 0.9636 |
| 0.0072 | 20.37 | 25200 | 0.2499 | 0.7897 | 0.8305 | 0.8096 | 0.9625 |
| 0.0072 | 20.45 | 25300 | 0.2623 | 0.7978 | 0.8282 | 0.8127 | 0.9624 |
| 0.0072 | 20.53 | 25400 | 0.2633 | 0.8036 | 0.8219 | 0.8126 | 0.9635 |
| 0.0068 | 20.61 | 25500 | 0.2695 | 0.7960 | 0.8279 | 0.8116 | 0.9626 |
| 0.0068 | 20.7 | 25600 | 0.2541 | 0.7757 | 0.8314 | 0.8026 | 0.9601 |
| 0.0068 | 20.78 | 25700 | 0.2706 | 0.7863 | 0.8233 | 0.8044 | 0.9616 |
| 0.0068 | 20.86 | 25800 | 0.2725 | 0.7990 | 0.8149 | 0.8069 | 0.9621 |
| 0.0068 | 20.94 | 25900 | 0.2758 | 0.7820 | 0.8325 | 0.8065 | 0.9611 |
| 0.0073 | 21.02 | 26000 | 0.2450 | 0.8081 | 0.8216 | 0.8148 | 0.9639 |
| 0.0073 | 21.1 | 26100 | 0.2640 | 0.7904 | 0.8253 | 0.8075 | 0.9622 |
| 0.0073 | 21.18 | 26200 | 0.2513 | 0.7874 | 0.8400 | 0.8128 | 0.9628 |
| 0.0073 | 21.26 | 26300 | 0.2661 | 0.8136 | 0.8141 | 0.8138 | 0.9641 |
| 0.0073 | 21.34 | 26400 | 0.2481 | 0.7880 | 0.8273 | 0.8072 | 0.9627 |
| 0.0061 | 21.42 | 26500 | 0.2662 | 0.7932 | 0.8293 | 0.8109 | 0.9627 |
| 0.0061 | 21.5 | 26600 | 0.2651 | 0.8095 | 0.8233 | 0.8163 | 0.9642 |
| 0.0061 | 21.58 | 26700 | 0.2569 | 0.7904 | 0.8270 | 0.8083 | 0.9618 |
| 0.0061 | 21.67 | 26800 | 0.2672 | 0.8006 | 0.8092 | 0.8049 | 0.9625 |
| 0.0061 | 21.75 | 26900 | 0.2721 | 0.7976 | 0.8167 | 0.8070 | 0.9619 |
| 0.0066 | 21.83 | 27000 | 0.2456 | 0.7904 | 0.8066 | 0.7984 | 0.9616 |
| 0.0066 | 21.91 | 27100 | 0.2709 | 0.8015 | 0.8195 | 0.8104 | 0.9631 |
| 0.0066 | 21.99 | 27200 | 0.2607 | 0.8072 | 0.8291 | 0.8180 | 0.9640 |
| 0.0066 | 22.07 | 27300 | 0.2841 | 0.8024 | 0.8227 | 0.8124 | 0.9627 |
| 0.0066 | 22.15 | 27400 | 0.2941 | 0.7906 | 0.8285 | 0.8091 | 0.9621 |
| 0.0056 | 22.23 | 27500 | 0.2856 | 0.8032 | 0.8213 | 0.8121 | 0.9633 |
| 0.0056 | 22.31 | 27600 | 0.2753 | 0.7933 | 0.8276 | 0.8101 | 0.9629 |
| 0.0056 | 22.39 | 27700 | 0.2739 | 0.8050 | 0.8022 | 0.8036 | 0.9636 |
| 0.0056 | 22.47 | 27800 | 0.3057 | 0.7689 | 0.8250 | 0.7960 | 0.9589 |
| 0.0056 | 22.55 | 27900 | 0.2722 | 0.7760 | 0.8279 | 0.8011 | 0.9607 |
| 0.0061 | 22.64 | 28000 | 0.2811 | 0.7880 | 0.8377 | 0.8121 | 0.9622 |
| 0.0061 | 22.72 | 28100 | 0.2819 | 0.7873 | 0.8345 | 0.8102 | 0.9606 |
| 0.0061 | 22.8 | 28200 | 0.2836 | 0.7939 | 0.8282 | 0.8107 | 0.9613 |
| 0.0061 | 22.88 | 28300 | 0.2911 | 0.7886 | 0.8279 | 0.8078 | 0.9603 |
| 0.0061 | 22.96 | 28400 | 0.2930 | 0.7844 | 0.8380 | 0.8103 | 0.9608 |
| 0.0046 | 23.04 | 28500 | 0.3019 | 0.7928 | 0.8360 | 0.8138 | 0.9620 |
| 0.0046 | 23.12 | 28600 | 0.2857 | 0.8027 | 0.8314 | 0.8168 | 0.9627 |
| 0.0046 | 23.2 | 28700 | 0.2794 | 0.8026 | 0.8276 | 0.8149 | 0.9634 |
| 0.0046 | 23.28 | 28800 | 0.2971 | 0.7950 | 0.8340 | 0.8140 | 0.9623 |
| 0.0046 | 23.36 | 28900 | 0.2689 | 0.8045 | 0.8219 | 0.8131 | 0.9632 |
| 0.0051 | 23.44 | 29000 | 0.2727 | 0.7957 | 0.8340 | 0.8144 | 0.9630 |
| 0.0051 | 23.52 | 29100 | 0.2707 | 0.7967 | 0.8291 | 0.8125 | 0.9626 |
| 0.0051 | 23.61 | 29200 | 0.2733 | 0.7978 | 0.8305 | 0.8138 | 0.9630 |
| 0.0051 | 23.69 | 29300 | 0.2744 | 0.7840 | 0.8371 | 0.8097 | 0.9620 |
| 0.0051 | 23.77 | 29400 | 0.2788 | 0.7905 | 0.8363 | 0.8127 | 0.9620 |
| 0.0046 | 23.85 | 29500 | 0.2913 | 0.7829 | 0.8391 | 0.8101 | 0.9611 |
| 0.0046 | 23.93 | 29600 | 0.2761 | 0.7919 | 0.8282 | 0.8096 | 0.9625 |
| 0.0046 | 24.01 | 29700 | 0.2858 | 0.7924 | 0.8342 | 0.8128 | 0.9629 |
| 0.0046 | 24.09 | 29800 | 0.2821 | 0.8062 | 0.8285 | 0.8172 | 0.9642 |
| 0.0046 | 24.17 | 29900 | 0.2802 | 0.8148 | 0.8129 | 0.8139 | 0.9638 |
| 0.0038 | 24.25 | 30000 | 0.2977 | 0.7952 | 0.8262 | 0.8104 | 0.9622 |
| 0.0038 | 24.33 | 30100 | 0.2848 | 0.8016 | 0.8337 | 0.8173 | 0.9639 |
| 0.0038 | 24.41 | 30200 | 0.2741 | 0.8096 | 0.8178 | 0.8137 | 0.9642 |
| 0.0038 | 24.49 | 30300 | 0.2896 | 0.7928 | 0.8308 | 0.8114 | 0.9636 |
| 0.0038 | 24.58 | 30400 | 0.2841 | 0.7884 | 0.8377 | 0.8123 | 0.9634 |
| 0.005 | 24.66 | 30500 | 0.2822 | 0.7924 | 0.8308 | 0.8111 | 0.9627 |
| 0.005 | 24.74 | 30600 | 0.2875 | 0.7940 | 0.8334 | 0.8132 | 0.9628 |
| 0.005 | 24.82 | 30700 | 0.2830 | 0.8012 | 0.8216 | 0.8113 | 0.9635 |
| 0.005 | 24.9 | 30800 | 0.2912 | 0.7978 | 0.8279 | 0.8126 | 0.9628 |
| 0.005 | 24.98 | 30900 | 0.2791 | 0.8066 | 0.8187 | 0.8126 | 0.9640 |
| 0.0039 | 25.06 | 31000 | 0.2774 | 0.7948 | 0.8296 | 0.8118 | 0.9627 |
| 0.0039 | 25.14 | 31100 | 0.2839 | 0.7945 | 0.8293 | 0.8116 | 0.9631 |
| 0.0039 | 25.22 | 31200 | 0.2948 | 0.7897 | 0.8322 | 0.8104 | 0.9628 |
| 0.0039 | 25.3 | 31300 | 0.2881 | 0.7925 | 0.8325 | 0.8120 | 0.9626 |
| 0.0039 | 25.38 | 31400 | 0.2917 | 0.7875 | 0.8331 | 0.8096 | 0.9626 |
| 0.0036 | 25.46 | 31500 | 0.2842 | 0.7942 | 0.8308 | 0.8121 | 0.9632 |
| 0.0036 | 25.55 | 31600 | 0.2776 | 0.7963 | 0.8305 | 0.8130 | 0.9635 |
| 0.0036 | 25.63 | 31700 | 0.2943 | 0.8009 | 0.8314 | 0.8158 | 0.9638 |
| 0.0036 | 25.71 | 31800 | 0.2888 | 0.8016 | 0.8268 | 0.8140 | 0.9643 |
| 0.0036 | 25.79 | 31900 | 0.2926 | 0.7957 | 0.8288 | 0.8119 | 0.9635 |
| 0.0034 | 25.87 | 32000 | 0.2916 | 0.7993 | 0.8302 | 0.8145 | 0.9637 |
| 0.0034 | 25.95 | 32100 | 0.2924 | 0.7973 | 0.8288 | 0.8127 | 0.9631 |
| 0.0034 | 26.03 | 32200 | 0.2918 | 0.8083 | 0.8204 | 0.8143 | 0.9640 |
| 0.0034 | 26.11 | 32300 | 0.3024 | 0.7931 | 0.8319 | 0.8120 | 0.9633 |
| 0.0034 | 26.19 | 32400 | 0.2996 | 0.7964 | 0.8268 | 0.8113 | 0.9636 |
| 0.0032 | 26.27 | 32500 | 0.3000 | 0.7972 | 0.8239 | 0.8103 | 0.9627 |
| 0.0032 | 26.35 | 32600 | 0.2856 | 0.8071 | 0.8201 | 0.8136 | 0.9636 |
| 0.0032 | 26.43 | 32700 | 0.2938 | 0.7968 | 0.8276 | 0.8119 | 0.9628 |
| 0.0032 | 26.52 | 32800 | 0.2897 | 0.8052 | 0.8259 | 0.8154 | 0.9641 |
| 0.0032 | 26.6 | 32900 | 0.2931 | 0.8074 | 0.8227 | 0.8150 | 0.9643 |
| 0.0035 | 26.68 | 33000 | 0.2879 | 0.8137 | 0.8244 | 0.8190 | 0.9651 |
| 0.0035 | 26.76 | 33100 | 0.2985 | 0.8026 | 0.8308 | 0.8164 | 0.9641 |
| 0.0035 | 26.84 | 33200 | 0.2864 | 0.8054 | 0.8242 | 0.8146 | 0.9639 |
| 0.0035 | 26.92 | 33300 | 0.2830 | 0.8094 | 0.8253 | 0.8173 | 0.9645 |
| 0.0035 | 27.0 | 33400 | 0.2951 | 0.7879 | 0.8363 | 0.8114 | 0.9629 |
| 0.0031 | 27.08 | 33500 | 0.2873 | 0.7969 | 0.8279 | 0.8121 | 0.9639 |
| 0.0031 | 27.16 | 33600 | 0.2927 | 0.8113 | 0.8204 | 0.8158 | 0.9648 |
| 0.0031 | 27.24 | 33700 | 0.2987 | 0.8015 | 0.8273 | 0.8142 | 0.9641 |
| 0.0031 | 27.32 | 33800 | 0.2990 | 0.8017 | 0.8250 | 0.8132 | 0.9641 |
| 0.0031 | 27.41 | 33900 | 0.2978 | 0.8033 | 0.8276 | 0.8153 | 0.9640 |
| 0.0025 | 27.49 | 34000 | 0.2960 | 0.8026 | 0.8273 | 0.8148 | 0.9644 |
| 0.0025 | 27.57 | 34100 | 0.2980 | 0.8051 | 0.8239 | 0.8144 | 0.9644 |
| 0.0025 | 27.65 | 34200 | 0.2978 | 0.8070 | 0.8233 | 0.8151 | 0.9646 |
| 0.0025 | 27.73 | 34300 | 0.3003 | 0.8070 | 0.8230 | 0.8149 | 0.9640 |
| 0.0025 | 27.81 | 34400 | 0.3026 | 0.8025 | 0.8221 | 0.8122 | 0.9640 |
| 0.0028 | 27.89 | 34500 | 0.3072 | 0.8010 | 0.8250 | 0.8128 | 0.9637 |
| 0.0028 | 27.97 | 34600 | 0.3137 | 0.7931 | 0.8319 | 0.8120 | 0.9631 |
| 0.0028 | 28.05 | 34700 | 0.3062 | 0.7932 | 0.8259 | 0.8092 | 0.9628 |
| 0.0028 | 28.13 | 34800 | 0.3053 | 0.7968 | 0.8250 | 0.8107 | 0.9634 |
| 0.0028 | 28.21 | 34900 | 0.3034 | 0.7979 | 0.8207 | 0.8092 | 0.9632 |
| 0.0025 | 28.29 | 35000 | 0.3094 | 0.7994 | 0.8213 | 0.8102 | 0.9635 |
| 0.0025 | 28.38 | 35100 | 0.3115 | 0.7970 | 0.8239 | 0.8102 | 0.9633 |
| 0.0025 | 28.46 | 35200 | 0.3116 | 0.7979 | 0.8230 | 0.8103 | 0.9633 |
| 0.0025 | 28.54 | 35300 | 0.3148 | 0.7959 | 0.8305 | 0.8128 | 0.9633 |
| 0.0025 | 28.62 | 35400 | 0.3127 | 0.7970 | 0.8273 | 0.8119 | 0.9633 |
| 0.0021 | 28.7 | 35500 | 0.3104 | 0.8015 | 0.8207 | 0.8110 | 0.9638 |
| 0.0021 | 28.78 | 35600 | 0.3127 | 0.8052 | 0.8221 | 0.8136 | 0.9640 |
| 0.0021 | 28.86 | 35700 | 0.3137 | 0.8001 | 0.8247 | 0.8122 | 0.9638 |
| 0.0021 | 28.94 | 35800 | 0.3172 | 0.7953 | 0.8311 | 0.8128 | 0.9632 |
| 0.0021 | 29.02 | 35900 | 0.3176 | 0.7987 | 0.8302 | 0.8141 | 0.9636 |
| 0.0021 | 29.1 | 36000 | 0.3207 | 0.7956 | 0.8317 | 0.8132 | 0.9635 |
| 0.0021 | 29.18 | 36100 | 0.3208 | 0.7956 | 0.8305 | 0.8127 | 0.9633 |
| 0.0021 | 29.26 | 36200 | 0.3192 | 0.7976 | 0.8293 | 0.8132 | 0.9635 |
| 0.0021 | 29.35 | 36300 | 0.3167 | 0.8006 | 0.8273 | 0.8137 | 0.9638 |
| 0.0021 | 29.43 | 36400 | 0.3167 | 0.7998 | 0.8279 | 0.8136 | 0.9637 |
| 0.0024 | 29.51 | 36500 | 0.3160 | 0.8014 | 0.8282 | 0.8146 | 0.9638 |
| 0.0024 | 29.59 | 36600 | 0.3156 | 0.8002 | 0.8268 | 0.8133 | 0.9637 |
| 0.0024 | 29.67 | 36700 | 0.3156 | 0.8027 | 0.8268 | 0.8145 | 0.9637 |
| 0.0024 | 29.75 | 36800 | 0.3154 | 0.8026 | 0.8262 | 0.8142 | 0.9638 |
| 0.0024 | 29.83 | 36900 | 0.3153 | 0.8020 | 0.8265 | 0.8140 | 0.9638 |
| 0.0017 | 29.91 | 37000 | 0.3152 | 0.8020 | 0.8268 | 0.8142 | 0.9638 |
| 0.0017 | 29.99 | 37100 | 0.3152 | 0.8023 | 0.8270 | 0.8145 | 0.9639 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "nielsr/lilt-xlm-roberta-base", "model-index": [{"name": "output_LiLT_test_01", "results": []}]} | timmy-1-2-3/output_LiLT_test_01 | null | [
"transformers",
"tensorboard",
"safetensors",
"lilt",
"token-classification",
"generated_from_trainer",
"base_model:nielsr/lilt-xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T20:24:33+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #lilt #token-classification #generated_from_trainer #base_model-nielsr/lilt-xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
| output\_LiLT\_test\_01
======================
This model is a fine-tuned version of nielsr/lilt-xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3152
* Precision: 0.8023
* Recall: 0.8270
* F1: 0.8145
* Accuracy: 0.9639
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
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30
### 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: 32\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: 30",
"### 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 #lilt #token-classification #generated_from_trainer #base_model-nielsr/lilt-xlm-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: 5e-05\n* train\\_batch\\_size: 32\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: 30",
"### 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"
] |
text-generation | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | Grayx/sad_llama_0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-19T20:24:38+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:
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## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
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## Evaluation
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- Hardware Type:
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#### Hardware
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[optional]
BibTeX:
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## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### 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"
] |
null | transformers | ## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Dogge/llama-3-8B-RP
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/llama-3-8B-RP-GGUF/resolve/main/llama-3-8B-RP.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-8B-RP-GGUF/resolve/main/llama-3-8B-RP.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-8B-RP-GGUF/resolve/main/llama-3-8B-RP.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-8B-RP-GGUF/resolve/main/llama-3-8B-RP.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/llama-3-8B-RP-GGUF/resolve/main/llama-3-8B-RP.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-8B-RP-GGUF/resolve/main/llama-3-8B-RP.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/llama-3-8B-RP-GGUF/resolve/main/llama-3-8B-RP.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-8B-RP-GGUF/resolve/main/llama-3-8B-RP.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-8B-RP-GGUF/resolve/main/llama-3-8B-RP.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/llama-3-8B-RP-GGUF/resolve/main/llama-3-8B-RP.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/llama-3-8B-RP-GGUF/resolve/main/llama-3-8B-RP.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-8B-RP-GGUF/resolve/main/llama-3-8B-RP.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-8B-RP-GGUF/resolve/main/llama-3-8B-RP.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/llama-3-8B-RP-GGUF/resolve/main/llama-3-8B-RP.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl", "sft"], "base_model": "Dogge/llama-3-8B-RP", "quantized_by": "mradermacher"} | mradermacher/llama-3-8B-RP-GGUF | null | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"sft",
"en",
"base_model:Dogge/llama-3-8B-RP",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T20:28:02+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #text-generation-inference #unsloth #llama #trl #sft #en #base_model-Dogge/llama-3-8B-RP #license-apache-2.0 #endpoints_compatible #region-us
| About
-----
static quants of URL
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
Usage
-----
If you are unsure how to use GGUF files, refer to one of TheBloke's
READMEs for
more details, including on how to concatenate multi-part files.
Provided Quants
---------------
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
!URL
And here are Artefact2's thoughts on the matter:
URL
FAQ / Model Request
-------------------
See URL for some answers to
questions you might have and/or if you want some other model quantized.
Thanks
------
I thank my company, nethype GmbH, for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
| [] | [
"TAGS\n#transformers #gguf #text-generation-inference #unsloth #llama #trl #sft #en #base_model-Dogge/llama-3-8B-RP #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
null | null | After downloading both .calm files and the `run` binary, run:
```
chmod a+x run
```
and then:
```
./run llama3-8b-fp8.calm -i "?" -n 32 -r 5
CALM_POSO=4000 ./run llama3-8b-fp8.calm -i "?" -n 32 -r 5
./run llama3-8b-gf4.calm -i "?" -n 32 -r 5
CALM_POSO=4000 ./run llama3-8b-gf4.calm -i "?" -n 32 -r 5
```
Note: the `run` binary is not signed, so macOS will refuse to run it by default, but you can ctrl+click the binary and press "Open", which will show a dialog where you need to press "Open" again.
After this the binary should be fine to run from terminal.
If you'd rather build the binary yourself, you can do it like this:
```
git clone https://github.com/zeux/calm
make -C calm
```
... but you'll need Xcode installed for this to work. | {} | zeuxcg/llama3-8b-calm | null | [
"region:us"
] | null | 2024-04-19T20:28:41+00:00 | [] | [] | TAGS
#region-us
| After downloading both .calm files and the 'run' binary, run:
and then:
Note: the 'run' binary is not signed, so macOS will refuse to run it by default, but you can ctrl+click the binary and press "Open", which will show a dialog where you need to press "Open" again.
After this the binary should be fine to run from terminal.
If you'd rather build the binary yourself, you can do it like this:
... but you'll need Xcode installed for this to work. | [] | [
"TAGS\n#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 🤗 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]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### 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": []} | Yasusan/Llama_111_220_110 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-19T20:30:23+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #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 #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"
] |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: conlan/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget"]} | conlan/ppo-SnowballTarget | null | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | null | 2024-04-19T20:32:18+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #SnowballTarget #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SnowballTarget #region-us
|
# ppo Agent playing SnowballTarget
This is a trained model of a ppo agent playing SnowballTarget
using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your
browser: URL
- A *longer tutorial* to understand how works ML-Agents:
URL
### Resume the training
### Watch your Agent play
You can watch your agent playing directly in your browser
1. If the environment is part of ML-Agents official environments, go to URL
2. Step 1: Find your model_id: conlan/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play
| [
"# ppo Agent playing SnowballTarget\n This is a trained model of a ppo agent playing SnowballTarget\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: conlan/ppo-SnowballTarget\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
"TAGS\n#ml-agents #tensorboard #onnx #SnowballTarget #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SnowballTarget #region-us \n",
"# ppo Agent playing SnowballTarget\n This is a trained model of a ppo agent playing SnowballTarget\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: conlan/ppo-SnowballTarget\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] |
text-generation | transformers | llava-v1.5 mixed with SoM data (10k listing)
| {} | zzxslp/som-llava-v1.5-13b-listing | null | [
"transformers",
"safetensors",
"llava_llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T20:32:52+00:00 | [] | [] | TAGS
#transformers #safetensors #llava_llama #text-generation #autotrain_compatible #endpoints_compatible #region-us
| llava-v1.5 mixed with SoM data (10k listing)
| [] | [
"TAGS\n#transformers #safetensors #llava_llama #text-generation #autotrain_compatible #endpoints_compatible #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 🤗 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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[More Information Needed] | {"library_name": "transformers", "tags": []} | Yasusan/Llama_111_original | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-19T20:32:53+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
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text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | mp1704/qwen_1.8b_sft_full | null | [
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"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
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] | null | 2024-04-19T20:35:36+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #qwen2 #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
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null | transformers | ## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/mpasila/Meta-Llama-3-11.5B-Instruct
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-11.5B-Instruct-GGUF/resolve/main/Meta-Llama-3-11.5B-Instruct.Q2_K.gguf) | Q2_K | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-11.5B-Instruct-GGUF/resolve/main/Meta-Llama-3-11.5B-Instruct.IQ3_XS.gguf) | IQ3_XS | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-11.5B-Instruct-GGUF/resolve/main/Meta-Llama-3-11.5B-Instruct.Q3_K_S.gguf) | Q3_K_S | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-11.5B-Instruct-GGUF/resolve/main/Meta-Llama-3-11.5B-Instruct.IQ3_S.gguf) | IQ3_S | 5.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-11.5B-Instruct-GGUF/resolve/main/Meta-Llama-3-11.5B-Instruct.IQ3_M.gguf) | IQ3_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-11.5B-Instruct-GGUF/resolve/main/Meta-Llama-3-11.5B-Instruct.Q3_K_M.gguf) | Q3_K_M | 5.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-11.5B-Instruct-GGUF/resolve/main/Meta-Llama-3-11.5B-Instruct.Q3_K_L.gguf) | Q3_K_L | 6.3 | |
| [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-11.5B-Instruct-GGUF/resolve/main/Meta-Llama-3-11.5B-Instruct.IQ4_XS.gguf) | IQ4_XS | 6.5 | |
| [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-11.5B-Instruct-GGUF/resolve/main/Meta-Llama-3-11.5B-Instruct.Q4_K_S.gguf) | Q4_K_S | 6.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-11.5B-Instruct-GGUF/resolve/main/Meta-Llama-3-11.5B-Instruct.Q4_K_M.gguf) | Q4_K_M | 7.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-11.5B-Instruct-GGUF/resolve/main/Meta-Llama-3-11.5B-Instruct.Q5_K_S.gguf) | Q5_K_S | 8.1 | |
| [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-11.5B-Instruct-GGUF/resolve/main/Meta-Llama-3-11.5B-Instruct.Q5_K_M.gguf) | Q5_K_M | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-11.5B-Instruct-GGUF/resolve/main/Meta-Llama-3-11.5B-Instruct.Q6_K.gguf) | Q6_K | 9.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-11.5B-Instruct-GGUF/resolve/main/Meta-Llama-3-11.5B-Instruct.Q8_0.gguf) | Q8_0 | 12.3 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "license": "other", "library_name": "transformers", "tags": ["mergekit", "merge", "facebook", "meta", "pytorch", "llama", "llama-3"], "base_model": "mpasila/Meta-Llama-3-11.5B-Instruct", "extra_gated_button_content": "Submit", "extra_gated_fields": {"Affiliation": "text", "By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy": "checkbox", "Country": "country", "Date of birth": "date_picker", "First Name": "text", "Last Name": "text", "geo": "ip_location"}, "extra_gated_prompt": "### META LLAMA 3 COMMUNITY LICENSE AGREEMENT\nMeta Llama 3 Version Release Date: April 18, 2024\n\"Agreement\" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein.\n\"Documentation\" means the specifications, manuals and documentation accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/.\n\"Licensee\" or \"you\" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity\u2019s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf.\n\"Meta Llama 3\" means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://llama.meta.com/llama-downloads.\n\"Llama Materials\" means, collectively, Meta\u2019s proprietary Meta Llama 3 and Documentation (and any portion thereof) made available under this Agreement.\n\"Meta\" or \"we\" means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland).\n \n1. License Rights and Redistribution.\na. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta\u2019s intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials.\nb. Redistribution and Use.\ni. If you distribute or make available the Llama Materials (or any derivative works thereof), or a product or service that uses any of them, including another AI model, you shall (A) provide a copy of this Agreement with any such Llama Materials; and (B) prominently display \u201cBuilt with Meta Llama 3\u201d on a related website, user interface, blogpost, about page, or product documentation. If you use the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is distributed or made available, you shall also include \u201cLlama 3\u201d at the beginning of any such AI model name.\nii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you.\niii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a \u201cNotice\u201d text file distributed as a part of such copies: \u201cMeta Llama 3 is licensed under the Meta Llama 3 Community License, Copyright \u00a9 Meta Platforms, Inc. All Rights Reserved.\u201d\niv. 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No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you a license to use \u201cLlama 3\u201d (the \u201cMark\u201d) solely as required to comply with the last sentence of Section 1.b.i. You will comply with Meta\u2019s brand guidelines (currently accessible at https://about.meta.com/brand/resources/meta/company-brand/ ). All goodwill arising out of your use of the Mark will inure to the benefit of Meta.\nb. 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The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement.\n7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement.\n### Meta Llama 3 Acceptable Use Policy\nMeta is committed to promoting safe and fair use of its tools and features, including Meta Llama 3. If you access or use Meta Llama 3, you agree to this Acceptable Use Policy (\u201cPolicy\u201d). The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy)\n#### Prohibited Uses\nWe want everyone to use Meta Llama 3 safely and responsibly. You agree you will not use, or allow others to use, Meta Llama 3 to: 1. Violate the law or others\u2019 rights, including to:\n 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:\n 1. Violence or terrorism\n 2. 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Generating, promoting, or furthering fraud or the creation or promotion of disinformation\n 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content\n 3. Generating, promoting, or further distributing spam\n 4. Impersonating another individual without consent, authorization, or legal right\n 5. Representing that the use of Meta Llama 3 or outputs are human-generated\n 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement\n4. Fail to appropriately disclose to end users any known dangers of your AI system\nPlease report any violation of this Policy, software \u201cbug,\u201d or other problems that could lead to a violation of this Policy through one of the following means:\n * Reporting issues with the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3)\n * Reporting risky content generated by the model:\n developers.facebook.com/llama_output_feedback\n * Reporting bugs and security concerns: facebook.com/whitehat/info\n * Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected]", "license_link": "LICENSE", "license_name": "llama3", "quantized_by": "mradermacher"} | mradermacher/Meta-Llama-3-11.5B-Instruct-GGUF | null | [
"transformers",
"gguf",
"mergekit",
"merge",
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"en",
"base_model:mpasila/Meta-Llama-3-11.5B-Instruct",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T20:38:26+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #mergekit #merge #facebook #meta #pytorch #llama #llama-3 #en #base_model-mpasila/Meta-Llama-3-11.5B-Instruct #license-other #endpoints_compatible #region-us
| About
-----
static quants of URL
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
Usage
-----
If you are unsure how to use GGUF files, refer to one of TheBloke's
READMEs for
more details, including on how to concatenate multi-part files.
Provided Quants
---------------
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
!URL
And here are Artefact2's thoughts on the matter:
URL
FAQ / Model Request
-------------------
See URL for some answers to
questions you might have and/or if you want some other model quantized.
Thanks
------
I thank my company, nethype GmbH, for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
| [] | [
"TAGS\n#transformers #gguf #mergekit #merge #facebook #meta #pytorch #llama #llama-3 #en #base_model-mpasila/Meta-Llama-3-11.5B-Instruct #license-other #endpoints_compatible #region-us \n"
] |
summarization | 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. -->
# BioNLP-intro-disc-tech-decoder-PLOS
This model was trained from scratch 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: 1.3739167643078955e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 1.13.1+cu117
- Datasets 2.16.1
- Tokenizers 0.15.2
| {"tags": ["summarization", "generated_from_trainer"], "model-index": [{"name": "BioNLP-intro-disc-tech-decoder-PLOS", "results": []}]} | dtorber/BioNLP-intro-disc-tech-decoder-PLOS | null | [
"transformers",
"safetensors",
"led",
"text2text-generation",
"summarization",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T20:39:01+00:00 | [] | [] | TAGS
#transformers #safetensors #led #text2text-generation #summarization #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# BioNLP-intro-disc-tech-decoder-PLOS
This model was trained from scratch 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: 1.3739167643078955e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 1.13.1+cu117
- Datasets 2.16.1
- Tokenizers 0.15.2
| [
"# BioNLP-intro-disc-tech-decoder-PLOS\n\nThis model was trained from scratch 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: 1.3739167643078955e-06\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- distributed_type: multi-GPU\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 5\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 1.13.1+cu117\n- Datasets 2.16.1\n- Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #safetensors #led #text2text-generation #summarization #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# BioNLP-intro-disc-tech-decoder-PLOS\n\nThis model was trained from scratch 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: 1.3739167643078955e-06\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- distributed_type: multi-GPU\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 5\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 1.13.1+cu117\n- Datasets 2.16.1\n- Tokenizers 0.15.2"
] |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **CartPole-v1**
This is a trained model of a **DQN** agent playing **CartPole-v1**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["CartPole-v1", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": "mean_reward", "value": "20.50 +/- 2.11", "name": "mean_reward", "verified": false}]}]}]} | CrispyJLoHalo/dqn-CartPole-v1 | null | [
"stable-baselines3",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2024-04-19T20:39:03+00:00 | [] | [] | TAGS
#stable-baselines3 #CartPole-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing CartPole-v1
This is a trained model of a DQN agent playing CartPole-v1
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# DQN Agent playing CartPole-v1\nThis is a trained model of a DQN agent playing CartPole-v1\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #CartPole-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing CartPole-v1\nThis is a trained model of a DQN agent playing CartPole-v1\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Pixelcopter-PLE-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}, "metrics": [{"type": "mean_reward", "value": "54.10 +/- 28.86", "name": "mean_reward", "verified": false}]}]}]} | eulpicard/Reinforce-Pixelcopter-PLE-v1 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2024-04-19T20:41:21+00:00 | [] | [] | TAGS
#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing Pixelcopter-PLE-v0
This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL
| [
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
"TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] |
object-detection | 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. -->
# jozhang97-deta-resnet-50-finetuned-10k-cppe5
This model is a fine-tuned version of [jozhang97/deta-resnet-50](https://huggingface.co/jozhang97/deta-resnet-50) on the cppe-5 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7663
- Map: 0.2022
- Map 50: 0.4588
- Map 75: 0.1509
- Map Small: 0.0948
- Map Medium: 0.1223
- Map Large: 0.2882
- Mar 1: 0.2396
- Mar 10: 0.405
- Mar 100: 0.4238
- Mar Small: 0.2134
- Mar Medium: 0.3177
- Mar Large: 0.5501
- Map Coverall: 0.5051
- Mar 100 Coverall: 0.6628
- Map Face Shield: 0.1207
- Mar 100 Face Shield: 0.3371
- Map Gloves: 0.0983
- Mar 100 Gloves: 0.3115
- Map Goggles: 0.1325
- Mar 100 Goggles: 0.431
- Map Mask: 0.1545
- Mar 100 Mask: 0.3768
## 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: 1337
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large | Map Coverall | Mar 100 Coverall | Map Face Shield | Mar 100 Face Shield | Map Gloves | Mar 100 Gloves | Map Goggles | Mar 100 Goggles | Map Mask | Mar 100 Mask |
|:-------------:|:-------:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:------------:|:----------------:|:---------------:|:-------------------:|:----------:|:--------------:|:-----------:|:---------------:|:--------:|:------------:|
| 12.2099 | 0.9953 | 106 | 3.7120 | 0.0067 | 0.0314 | 0.0003 | 0.0 | 0.0006 | 0.0068 | 0.0122 | 0.0353 | 0.0421 | 0.0 | 0.0067 | 0.0451 | 0.0332 | 0.2049 | 0.0001 | 0.0057 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 3.4288 | 2.0 | 213 | 3.8665 | 0.0117 | 0.0403 | 0.0038 | 0.0 | 0.0004 | 0.0118 | 0.0199 | 0.0448 | 0.0505 | 0.0 | 0.0015 | 0.0592 | 0.0577 | 0.2348 | 0.001 | 0.0143 | 0.0 | 0.0021 | 0.0 | 0.0 | 0.0 | 0.0011 |
| 3.5031 | 2.9953 | 319 | 3.5816 | 0.014 | 0.0448 | 0.0075 | 0.0 | 0.0003 | 0.0143 | 0.0236 | 0.0424 | 0.0486 | 0.0 | 0.0015 | 0.0659 | 0.0693 | 0.2238 | 0.0001 | 0.0071 | 0.0 | 0.0031 | 0.0004 | 0.0024 | 0.0001 | 0.0068 |
| 3.3269 | 4.0 | 426 | 3.1994 | 0.02 | 0.0594 | 0.0065 | 0.0 | 0.0002 | 0.0203 | 0.0242 | 0.0567 | 0.066 | 0.0004 | 0.0027 | 0.1003 | 0.0991 | 0.2768 | 0.0002 | 0.0271 | 0.0 | 0.0078 | 0.0 | 0.0095 | 0.0004 | 0.0085 |
| 3.1804 | 4.9953 | 532 | 3.0309 | 0.0158 | 0.0494 | 0.0066 | 0.0 | 0.0017 | 0.0151 | 0.0262 | 0.0535 | 0.064 | 0.0004 | 0.0092 | 0.1022 | 0.0713 | 0.2451 | 0.001 | 0.0343 | 0.0001 | 0.0104 | 0.0 | 0.0 | 0.0068 | 0.0299 |
| 2.9763 | 6.0 | 639 | 2.7981 | 0.0281 | 0.0939 | 0.0086 | 0.0019 | 0.0023 | 0.03 | 0.0354 | 0.0831 | 0.098 | 0.0147 | 0.0284 | 0.1149 | 0.1311 | 0.3433 | 0.0006 | 0.05 | 0.0 | 0.0052 | 0.0001 | 0.0024 | 0.009 | 0.0893 |
| 2.7978 | 6.9953 | 745 | 2.6720 | 0.0344 | 0.0999 | 0.0131 | 0.0017 | 0.0036 | 0.0338 | 0.0498 | 0.109 | 0.1197 | 0.0263 | 0.0547 | 0.1193 | 0.1616 | 0.372 | 0.005 | 0.0957 | 0.0 | 0.0177 | 0.0004 | 0.0333 | 0.0047 | 0.0797 |
| 2.6679 | 8.0 | 852 | 2.6906 | 0.0363 | 0.0999 | 0.0137 | 0.0146 | 0.0065 | 0.0366 | 0.0438 | 0.0947 | 0.104 | 0.0321 | 0.0332 | 0.1264 | 0.1681 | 0.3415 | 0.0083 | 0.0886 | 0.0004 | 0.0167 | 0.0002 | 0.0119 | 0.0047 | 0.0616 |
| 2.6637 | 8.9953 | 958 | 2.6518 | 0.0319 | 0.1009 | 0.012 | 0.0032 | 0.005 | 0.0309 | 0.0462 | 0.1171 | 0.133 | 0.0089 | 0.0649 | 0.1368 | 0.1457 | 0.4006 | 0.0048 | 0.0943 | 0.0001 | 0.0167 | 0.0004 | 0.0381 | 0.0086 | 0.1153 |
| 2.6412 | 10.0 | 1065 | 2.5417 | 0.0443 | 0.12 | 0.0164 | 0.0548 | 0.0015 | 0.0465 | 0.0567 | 0.1178 | 0.1316 | 0.0591 | 0.0452 | 0.171 | 0.2142 | 0.4183 | 0.0031 | 0.0929 | 0.0002 | 0.0281 | 0.0006 | 0.0381 | 0.0032 | 0.0808 |
| 2.6155 | 10.9953 | 1171 | 2.7743 | 0.0356 | 0.1122 | 0.0216 | 0.0031 | 0.0049 | 0.0435 | 0.0527 | 0.1254 | 0.1399 | 0.0239 | 0.0623 | 0.1768 | 0.1562 | 0.3909 | 0.0054 | 0.08 | 0.0005 | 0.0396 | 0.0015 | 0.0714 | 0.0142 | 0.1175 |
| 2.5621 | 12.0 | 1278 | 2.5292 | 0.0473 | 0.1309 | 0.0258 | 0.003 | 0.0027 | 0.0567 | 0.06 | 0.1265 | 0.1404 | 0.0107 | 0.042 | 0.2137 | 0.2226 | 0.4561 | 0.0038 | 0.0629 | 0.0014 | 0.038 | 0.0044 | 0.0643 | 0.0045 | 0.0808 |
| 2.4871 | 12.9953 | 1384 | 2.5235 | 0.0418 | 0.1211 | 0.0183 | 0.01 | 0.0017 | 0.0451 | 0.0555 | 0.1225 | 0.1389 | 0.0219 | 0.0412 | 0.2163 | 0.1994 | 0.4287 | 0.0021 | 0.0757 | 0.0014 | 0.063 | 0.0018 | 0.0595 | 0.0041 | 0.0678 |
| 2.4874 | 14.0 | 1491 | 2.4597 | 0.0585 | 0.148 | 0.0377 | 0.0142 | 0.0047 | 0.0656 | 0.0746 | 0.1555 | 0.1698 | 0.0353 | 0.0689 | 0.2626 | 0.2705 | 0.4659 | 0.0064 | 0.13 | 0.0012 | 0.0562 | 0.0058 | 0.0929 | 0.0087 | 0.104 |
| 2.4629 | 14.9953 | 1597 | 2.3910 | 0.0587 | 0.1563 | 0.0333 | 0.0039 | 0.0092 | 0.0703 | 0.086 | 0.1896 | 0.1994 | 0.0238 | 0.0976 | 0.299 | 0.2543 | 0.4994 | 0.0115 | 0.1971 | 0.0022 | 0.0448 | 0.003 | 0.1238 | 0.0225 | 0.1316 |
| 2.4231 | 16.0 | 1704 | 2.6214 | 0.0538 | 0.1494 | 0.0256 | 0.0069 | 0.0102 | 0.0676 | 0.0821 | 0.1857 | 0.2013 | 0.0119 | 0.0926 | 0.3454 | 0.2271 | 0.489 | 0.0093 | 0.1914 | 0.0023 | 0.0656 | 0.0038 | 0.1095 | 0.0266 | 0.1508 |
| 2.4166 | 16.9953 | 1810 | 2.5325 | 0.0632 | 0.1615 | 0.0332 | 0.0075 | 0.0096 | 0.0749 | 0.0829 | 0.1743 | 0.1932 | 0.0202 | 0.0914 | 0.3148 | 0.2769 | 0.4756 | 0.0044 | 0.1657 | 0.0038 | 0.0651 | 0.0065 | 0.1286 | 0.0244 | 0.1311 |
| 2.3794 | 18.0 | 1917 | 2.4702 | 0.0843 | 0.1921 | 0.0629 | 0.0217 | 0.0134 | 0.0982 | 0.1035 | 0.2074 | 0.2204 | 0.0226 | 0.1022 | 0.3572 | 0.3649 | 0.5555 | 0.0108 | 0.1957 | 0.0031 | 0.0911 | 0.0143 | 0.1286 | 0.0283 | 0.1311 |
| 2.3384 | 18.9953 | 2023 | 2.5070 | 0.0737 | 0.1736 | 0.0519 | 0.0007 | 0.012 | 0.0866 | 0.0936 | 0.1942 | 0.2156 | 0.0032 | 0.1048 | 0.3206 | 0.32 | 0.5494 | 0.0125 | 0.2029 | 0.0058 | 0.0932 | 0.0054 | 0.0976 | 0.0249 | 0.135 |
| 2.3538 | 20.0 | 2130 | 2.5906 | 0.0647 | 0.1794 | 0.0375 | 0.0033 | 0.0198 | 0.0829 | 0.1073 | 0.2141 | 0.2289 | 0.0059 | 0.1278 | 0.3508 | 0.2457 | 0.5171 | 0.0264 | 0.2214 | 0.0041 | 0.1016 | 0.0081 | 0.1214 | 0.0395 | 0.1831 |
| 2.5129 | 20.9953 | 2236 | 2.9187 | 0.0672 | 0.1692 | 0.0533 | 0.0005 | 0.0144 | 0.0905 | 0.1011 | 0.2206 | 0.2352 | 0.002 | 0.1177 | 0.4044 | 0.2685 | 0.5421 | 0.028 | 0.2929 | 0.0083 | 0.1083 | 0.0016 | 0.0881 | 0.0297 | 0.1446 |
| 2.7734 | 22.0 | 2343 | 3.1307 | 0.0512 | 0.1436 | 0.0257 | 0.0042 | 0.0134 | 0.0861 | 0.0949 | 0.2129 | 0.2249 | 0.0077 | 0.1169 | 0.3711 | 0.1908 | 0.5091 | 0.0348 | 0.27 | 0.0037 | 0.0625 | 0.0057 | 0.1333 | 0.0212 | 0.1497 |
| 2.9978 | 22.9953 | 2449 | 3.1732 | 0.0459 | 0.1255 | 0.027 | 0.0093 | 0.0144 | 0.069 | 0.0842 | 0.2302 | 0.2463 | 0.0128 | 0.1446 | 0.3827 | 0.1654 | 0.5201 | 0.0227 | 0.2414 | 0.0066 | 0.0807 | 0.0138 | 0.1905 | 0.021 | 0.1989 |
| 3.3046 | 24.0 | 2556 | 3.4681 | 0.0339 | 0.1034 | 0.0174 | 0.0159 | 0.0106 | 0.0538 | 0.065 | 0.2138 | 0.2341 | 0.1286 | 0.1402 | 0.3069 | 0.1219 | 0.5024 | 0.0072 | 0.1929 | 0.0037 | 0.0823 | 0.0128 | 0.1738 | 0.0241 | 0.2192 |
| 3.0429 | 24.9953 | 2662 | 2.8145 | 0.0577 | 0.1646 | 0.0351 | 0.0084 | 0.0234 | 0.0838 | 0.0969 | 0.2569 | 0.2758 | 0.0238 | 0.167 | 0.4128 | 0.1906 | 0.5646 | 0.0341 | 0.2957 | 0.0068 | 0.0885 | 0.0075 | 0.2071 | 0.0493 | 0.2232 |
| 2.4662 | 26.0 | 2769 | 2.2661 | 0.0914 | 0.2143 | 0.0758 | 0.0178 | 0.0174 | 0.1372 | 0.127 | 0.2461 | 0.2609 | 0.0259 | 0.167 | 0.3821 | 0.3735 | 0.5579 | 0.0195 | 0.2086 | 0.0065 | 0.0958 | 0.0086 | 0.2071 | 0.0491 | 0.235 |
| 2.1285 | 26.9953 | 2875 | 2.2159 | 0.0968 | 0.2383 | 0.0584 | 0.0347 | 0.0279 | 0.1258 | 0.1285 | 0.2658 | 0.2884 | 0.0488 | 0.1964 | 0.4154 | 0.374 | 0.5579 | 0.0305 | 0.28 | 0.0103 | 0.1161 | 0.0161 | 0.2643 | 0.053 | 0.2237 |
| 2.1347 | 28.0 | 2982 | 2.5509 | 0.0706 | 0.1836 | 0.0441 | 0.0144 | 0.0222 | 0.1024 | 0.1252 | 0.2744 | 0.2974 | 0.033 | 0.2016 | 0.403 | 0.253 | 0.5677 | 0.0291 | 0.26 | 0.0164 | 0.138 | 0.0144 | 0.2833 | 0.0399 | 0.2379 |
| 2.3668 | 28.9953 | 3088 | 2.4054 | 0.0966 | 0.2499 | 0.0643 | 0.0773 | 0.0414 | 0.1119 | 0.1484 | 0.2965 | 0.3169 | 0.1885 | 0.2178 | 0.4012 | 0.3196 | 0.5811 | 0.0377 | 0.2757 | 0.0233 | 0.1432 | 0.0304 | 0.2905 | 0.0722 | 0.2938 |
| 2.2677 | 30.0 | 3195 | 2.1192 | 0.1068 | 0.2461 | 0.0794 | 0.036 | 0.0276 | 0.1437 | 0.1434 | 0.3102 | 0.3274 | 0.1926 | 0.2175 | 0.4496 | 0.3972 | 0.5927 | 0.0419 | 0.3186 | 0.0113 | 0.1453 | 0.0285 | 0.2976 | 0.055 | 0.2831 |
| 2.2587 | 30.9953 | 3301 | 2.6246 | 0.0757 | 0.2145 | 0.0386 | 0.0579 | 0.0372 | 0.0801 | 0.1214 | 0.2851 | 0.3075 | 0.2843 | 0.2145 | 0.3503 | 0.2378 | 0.536 | 0.0198 | 0.2543 | 0.0159 | 0.15 | 0.0357 | 0.3048 | 0.0695 | 0.2927 |
| 2.7778 | 32.0 | 3408 | 2.6193 | 0.0877 | 0.226 | 0.0504 | 0.0423 | 0.0431 | 0.0964 | 0.1302 | 0.2978 | 0.3203 | 0.1287 | 0.232 | 0.3622 | 0.2707 | 0.5768 | 0.0352 | 0.2486 | 0.0169 | 0.1594 | 0.0482 | 0.3167 | 0.0675 | 0.3 |
| 2.2752 | 32.9953 | 3514 | 2.2670 | 0.1165 | 0.286 | 0.0836 | 0.0218 | 0.0533 | 0.1523 | 0.1711 | 0.3059 | 0.3261 | 0.0799 | 0.2451 | 0.4439 | 0.4075 | 0.5909 | 0.0485 | 0.2957 | 0.0223 | 0.1656 | 0.0207 | 0.2857 | 0.0834 | 0.2927 |
| 2.1109 | 34.0 | 3621 | 2.1783 | 0.1182 | 0.2898 | 0.0878 | 0.0207 | 0.0406 | 0.1695 | 0.1547 | 0.3043 | 0.3242 | 0.0858 | 0.2155 | 0.4605 | 0.402 | 0.5762 | 0.0411 | 0.2757 | 0.0214 | 0.1807 | 0.0475 | 0.3 | 0.079 | 0.2881 |
| 2.22 | 34.9953 | 3727 | 2.2399 | 0.1203 | 0.2683 | 0.1016 | 0.0223 | 0.0404 | 0.1578 | 0.1656 | 0.323 | 0.3393 | 0.2078 | 0.2267 | 0.4161 | 0.4218 | 0.6262 | 0.0401 | 0.3057 | 0.0241 | 0.1609 | 0.0327 | 0.3143 | 0.0826 | 0.2893 |
| 2.5775 | 36.0 | 3834 | 3.4084 | 0.0927 | 0.2324 | 0.0649 | 0.022 | 0.0379 | 0.115 | 0.1397 | 0.278 | 0.298 | 0.093 | 0.2007 | 0.3901 | 0.2973 | 0.5427 | 0.0292 | 0.2414 | 0.0228 | 0.125 | 0.0481 | 0.3 | 0.0661 | 0.2808 |
| 2.9257 | 36.9953 | 3940 | 2.5430 | 0.1051 | 0.2535 | 0.0731 | 0.0469 | 0.0389 | 0.1393 | 0.1607 | 0.317 | 0.3411 | 0.1976 | 0.2365 | 0.4454 | 0.3591 | 0.5909 | 0.0259 | 0.2857 | 0.0213 | 0.2104 | 0.0442 | 0.3262 | 0.075 | 0.2921 |
| 2.1766 | 38.0 | 4047 | 2.2615 | 0.1232 | 0.2757 | 0.1013 | 0.0573 | 0.0413 | 0.154 | 0.1625 | 0.3287 | 0.3527 | 0.2073 | 0.24 | 0.4692 | 0.4301 | 0.6073 | 0.0324 | 0.3143 | 0.0249 | 0.2042 | 0.0528 | 0.3357 | 0.0756 | 0.3023 |
| 2.2174 | 38.9953 | 4153 | 2.1064 | 0.139 | 0.3199 | 0.1186 | 0.0442 | 0.0476 | 0.1704 | 0.1744 | 0.3165 | 0.3317 | 0.2105 | 0.2171 | 0.4155 | 0.4647 | 0.6122 | 0.0287 | 0.2343 | 0.051 | 0.2016 | 0.05 | 0.3071 | 0.1007 | 0.3034 |
| 2.0069 | 40.0 | 4260 | 2.1137 | 0.1274 | 0.2796 | 0.1086 | 0.0711 | 0.0423 | 0.1375 | 0.1705 | 0.3207 | 0.3397 | 0.1597 | 0.225 | 0.4317 | 0.4744 | 0.6293 | 0.0293 | 0.2443 | 0.0292 | 0.2146 | 0.021 | 0.3048 | 0.0831 | 0.3056 |
| 2.0335 | 40.9953 | 4366 | 2.0880 | 0.1278 | 0.2884 | 0.1103 | 0.0374 | 0.0416 | 0.1395 | 0.1631 | 0.3068 | 0.3302 | 0.1615 | 0.2089 | 0.4282 | 0.4782 | 0.628 | 0.0342 | 0.2357 | 0.0179 | 0.1745 | 0.0397 | 0.3571 | 0.0689 | 0.2554 |
| 2.1166 | 42.0 | 4473 | 2.1062 | 0.1303 | 0.299 | 0.1098 | 0.0324 | 0.0445 | 0.1594 | 0.1709 | 0.3222 | 0.3467 | 0.1433 | 0.2403 | 0.4547 | 0.45 | 0.6012 | 0.0586 | 0.2814 | 0.0363 | 0.2208 | 0.0355 | 0.3381 | 0.0708 | 0.2921 |
| 1.9831 | 42.9953 | 4579 | 2.0591 | 0.142 | 0.3266 | 0.1181 | 0.0548 | 0.0532 | 0.166 | 0.1881 | 0.3438 | 0.3595 | 0.1447 | 0.249 | 0.4546 | 0.4789 | 0.6268 | 0.0469 | 0.2686 | 0.0471 | 0.2255 | 0.0494 | 0.3476 | 0.0877 | 0.3288 |
| 2.0249 | 44.0 | 4686 | 2.1750 | 0.1331 | 0.3023 | 0.108 | 0.0487 | 0.0453 | 0.1746 | 0.1843 | 0.3345 | 0.3565 | 0.1692 | 0.2419 | 0.449 | 0.4406 | 0.6226 | 0.065 | 0.3257 | 0.0396 | 0.2234 | 0.0421 | 0.3119 | 0.0783 | 0.2989 |
| 2.1806 | 44.9953 | 4792 | 2.1668 | 0.1299 | 0.3103 | 0.1009 | 0.0523 | 0.0368 | 0.1587 | 0.1604 | 0.3055 | 0.3313 | 0.1054 | 0.2044 | 0.4652 | 0.4617 | 0.6165 | 0.0563 | 0.27 | 0.0277 | 0.2089 | 0.0341 | 0.2833 | 0.0697 | 0.278 |
| 1.9865 | 46.0 | 4899 | 2.1022 | 0.1337 | 0.3124 | 0.1006 | 0.0331 | 0.0573 | 0.1585 | 0.177 | 0.332 | 0.3522 | 0.121 | 0.2542 | 0.4359 | 0.451 | 0.6159 | 0.0228 | 0.2386 | 0.0341 | 0.2339 | 0.0467 | 0.3524 | 0.1137 | 0.3203 |
| 2.0517 | 46.9953 | 5005 | 2.0253 | 0.1437 | 0.3449 | 0.1205 | 0.038 | 0.058 | 0.1937 | 0.1831 | 0.3327 | 0.3535 | 0.1973 | 0.2473 | 0.4626 | 0.4515 | 0.6201 | 0.0507 | 0.2729 | 0.0345 | 0.2255 | 0.0846 | 0.3452 | 0.0969 | 0.304 |
| 1.8315 | 48.0 | 5112 | 1.9870 | 0.1528 | 0.3572 | 0.1134 | 0.0298 | 0.0525 | 0.2276 | 0.1953 | 0.3464 | 0.3668 | 0.1622 | 0.2418 | 0.5026 | 0.4801 | 0.6305 | 0.0633 | 0.2929 | 0.0491 | 0.2354 | 0.0861 | 0.3714 | 0.0855 | 0.304 |
| 1.8105 | 48.9953 | 5218 | 1.9702 | 0.1436 | 0.3227 | 0.1212 | 0.0437 | 0.0429 | 0.2046 | 0.1911 | 0.3314 | 0.3517 | 0.1959 | 0.2283 | 0.4668 | 0.4903 | 0.6372 | 0.0616 | 0.27 | 0.0348 | 0.2479 | 0.066 | 0.331 | 0.0653 | 0.2723 |
| 1.7928 | 50.0 | 5325 | 1.9007 | 0.1549 | 0.3631 | 0.1243 | 0.0413 | 0.0563 | 0.2183 | 0.2026 | 0.3488 | 0.369 | 0.13 | 0.2439 | 0.4907 | 0.4838 | 0.6476 | 0.0679 | 0.2914 | 0.0585 | 0.2495 | 0.0713 | 0.3571 | 0.0931 | 0.2994 |
| 1.7696 | 50.9953 | 5431 | 1.9786 | 0.1511 | 0.3631 | 0.1196 | 0.0232 | 0.0595 | 0.2346 | 0.204 | 0.3601 | 0.3848 | 0.1566 | 0.2821 | 0.4908 | 0.4767 | 0.6287 | 0.0758 | 0.3171 | 0.0484 | 0.25 | 0.0748 | 0.4214 | 0.0801 | 0.3068 |
| 1.7579 | 52.0 | 5538 | 1.9172 | 0.1628 | 0.3659 | 0.132 | 0.0341 | 0.0698 | 0.2224 | 0.2133 | 0.3753 | 0.3968 | 0.2334 | 0.2767 | 0.5034 | 0.498 | 0.6524 | 0.0869 | 0.3343 | 0.0472 | 0.2646 | 0.0795 | 0.4024 | 0.1023 | 0.3305 |
| 1.7493 | 52.9953 | 5644 | 1.8806 | 0.1599 | 0.3797 | 0.1159 | 0.0522 | 0.0727 | 0.2175 | 0.2111 | 0.3677 | 0.3969 | 0.2264 | 0.2769 | 0.5069 | 0.4723 | 0.6482 | 0.0713 | 0.3314 | 0.0662 | 0.2792 | 0.066 | 0.3833 | 0.1239 | 0.3424 |
| 1.7134 | 54.0 | 5751 | 1.9045 | 0.1663 | 0.3599 | 0.1239 | 0.0372 | 0.0754 | 0.2379 | 0.2115 | 0.3648 | 0.3911 | 0.1374 | 0.2659 | 0.522 | 0.4841 | 0.6476 | 0.0674 | 0.2829 | 0.0588 | 0.2807 | 0.1056 | 0.4095 | 0.1155 | 0.335 |
| 1.7611 | 54.9953 | 5857 | 1.9231 | 0.1606 | 0.3967 | 0.1162 | 0.0462 | 0.0722 | 0.2079 | 0.2059 | 0.3553 | 0.3789 | 0.1245 | 0.2652 | 0.4963 | 0.4928 | 0.639 | 0.0981 | 0.3429 | 0.0482 | 0.2406 | 0.0709 | 0.3643 | 0.093 | 0.3079 |
| 1.7949 | 56.0 | 5964 | 1.8811 | 0.182 | 0.4156 | 0.1361 | 0.0724 | 0.0814 | 0.2389 | 0.2195 | 0.3801 | 0.3956 | 0.1577 | 0.2722 | 0.4984 | 0.4928 | 0.6366 | 0.1112 | 0.3386 | 0.0831 | 0.2812 | 0.1114 | 0.4119 | 0.1113 | 0.3096 |
| 1.7328 | 56.9953 | 6070 | 1.8393 | 0.1802 | 0.4138 | 0.143 | 0.0649 | 0.0869 | 0.2511 | 0.2184 | 0.3899 | 0.4079 | 0.2281 | 0.2913 | 0.5215 | 0.502 | 0.6512 | 0.1311 | 0.3471 | 0.0729 | 0.263 | 0.0834 | 0.4357 | 0.1115 | 0.3424 |
| 1.6581 | 58.0 | 6177 | 1.8911 | 0.1818 | 0.4149 | 0.1458 | 0.0567 | 0.0851 | 0.254 | 0.2101 | 0.3745 | 0.3931 | 0.1613 | 0.266 | 0.5025 | 0.4957 | 0.6512 | 0.1287 | 0.3443 | 0.0735 | 0.2505 | 0.083 | 0.3905 | 0.1282 | 0.3288 |
| 1.6535 | 58.9953 | 6283 | 1.8845 | 0.1712 | 0.4046 | 0.1308 | 0.0742 | 0.0783 | 0.2418 | 0.2009 | 0.3816 | 0.3972 | 0.2855 | 0.2882 | 0.4836 | 0.5077 | 0.6518 | 0.0892 | 0.3229 | 0.0578 | 0.2589 | 0.0898 | 0.4286 | 0.1113 | 0.3237 |
| 1.6606 | 60.0 | 6390 | 1.9009 | 0.1779 | 0.4172 | 0.1297 | 0.0798 | 0.0946 | 0.2376 | 0.2137 | 0.3647 | 0.3862 | 0.235 | 0.2811 | 0.4988 | 0.4882 | 0.6354 | 0.0948 | 0.2729 | 0.0739 | 0.2854 | 0.1031 | 0.4 | 0.1297 | 0.3373 |
| 1.6766 | 60.9953 | 6496 | 1.9100 | 0.18 | 0.4238 | 0.1385 | 0.092 | 0.087 | 0.2338 | 0.2233 | 0.384 | 0.3989 | 0.2325 | 0.2852 | 0.4949 | 0.4817 | 0.636 | 0.0971 | 0.3171 | 0.0831 | 0.2714 | 0.1194 | 0.4429 | 0.1185 | 0.3271 |
| 1.7227 | 62.0 | 6603 | 1.8943 | 0.173 | 0.3979 | 0.1294 | 0.0935 | 0.0739 | 0.2254 | 0.2172 | 0.3674 | 0.389 | 0.1763 | 0.2647 | 0.5074 | 0.5011 | 0.6463 | 0.1096 | 0.3286 | 0.0731 | 0.2854 | 0.0727 | 0.3786 | 0.1087 | 0.3062 |
| 1.6737 | 62.9953 | 6709 | 1.9753 | 0.1645 | 0.398 | 0.1159 | 0.0841 | 0.0849 | 0.2382 | 0.2065 | 0.3693 | 0.3867 | 0.2084 | 0.2771 | 0.4862 | 0.4483 | 0.6171 | 0.0934 | 0.3114 | 0.0869 | 0.3005 | 0.0684 | 0.3738 | 0.1256 | 0.3305 |
| 1.6768 | 64.0 | 6816 | 1.8531 | 0.1759 | 0.401 | 0.1408 | 0.065 | 0.0851 | 0.2592 | 0.2178 | 0.3635 | 0.3872 | 0.1646 | 0.2774 | 0.5118 | 0.5142 | 0.6463 | 0.0906 | 0.3114 | 0.062 | 0.2844 | 0.0843 | 0.3571 | 0.1284 | 0.3367 |
| 1.6543 | 64.9953 | 6922 | 1.8840 | 0.1828 | 0.4139 | 0.1364 | 0.0964 | 0.0979 | 0.2575 | 0.2195 | 0.3841 | 0.4027 | 0.1731 | 0.3073 | 0.504 | 0.4805 | 0.6409 | 0.1276 | 0.3529 | 0.088 | 0.2969 | 0.093 | 0.3881 | 0.1252 | 0.335 |
| 1.6153 | 66.0 | 7029 | 1.9622 | 0.1684 | 0.3804 | 0.1298 | 0.0652 | 0.0743 | 0.235 | 0.2143 | 0.3699 | 0.391 | 0.1739 | 0.2697 | 0.4861 | 0.4784 | 0.6293 | 0.1049 | 0.33 | 0.0461 | 0.2745 | 0.0881 | 0.4024 | 0.1244 | 0.3186 |
| 1.6143 | 66.9953 | 7135 | 1.8685 | 0.178 | 0.394 | 0.1375 | 0.0621 | 0.0948 | 0.2429 | 0.2178 | 0.3883 | 0.4114 | 0.2767 | 0.2987 | 0.5148 | 0.5007 | 0.6372 | 0.113 | 0.3529 | 0.0771 | 0.2932 | 0.0741 | 0.4238 | 0.1252 | 0.3497 |
| 1.6326 | 68.0 | 7242 | 1.8852 | 0.1745 | 0.3983 | 0.1289 | 0.0568 | 0.0764 | 0.23 | 0.2163 | 0.3856 | 0.4044 | 0.2097 | 0.2825 | 0.5231 | 0.5052 | 0.6482 | 0.1178 | 0.3514 | 0.0672 | 0.2849 | 0.0624 | 0.3905 | 0.1196 | 0.3469 |
| 1.6507 | 68.9953 | 7348 | 1.8130 | 0.1821 | 0.4003 | 0.1556 | 0.06 | 0.1034 | 0.2663 | 0.2157 | 0.3933 | 0.4077 | 0.3075 | 0.2933 | 0.5294 | 0.5087 | 0.6579 | 0.1021 | 0.3529 | 0.0749 | 0.2969 | 0.0992 | 0.4024 | 0.1256 | 0.3282 |
| 1.5577 | 70.0 | 7455 | 1.8646 | 0.1775 | 0.3934 | 0.1375 | 0.0802 | 0.0721 | 0.2704 | 0.2248 | 0.386 | 0.4115 | 0.2884 | 0.2959 | 0.5086 | 0.5029 | 0.6518 | 0.0973 | 0.3357 | 0.0637 | 0.2979 | 0.0962 | 0.4262 | 0.1273 | 0.3458 |
| 1.5784 | 70.9953 | 7561 | 1.7817 | 0.1912 | 0.4163 | 0.1575 | 0.0783 | 0.0846 | 0.2848 | 0.229 | 0.3986 | 0.4184 | 0.3431 | 0.2889 | 0.532 | 0.5187 | 0.6695 | 0.1152 | 0.3429 | 0.0723 | 0.3005 | 0.0953 | 0.4214 | 0.1546 | 0.3576 |
| 1.506 | 72.0 | 7668 | 1.7696 | 0.1938 | 0.4236 | 0.1464 | 0.0803 | 0.1105 | 0.2592 | 0.2337 | 0.3895 | 0.4151 | 0.1821 | 0.2993 | 0.5415 | 0.5214 | 0.6634 | 0.0966 | 0.34 | 0.0775 | 0.2932 | 0.1302 | 0.4262 | 0.1434 | 0.3525 |
| 1.5384 | 72.9953 | 7774 | 1.8206 | 0.189 | 0.4304 | 0.1388 | 0.0836 | 0.0839 | 0.2802 | 0.2353 | 0.3889 | 0.4041 | 0.2535 | 0.272 | 0.5363 | 0.5046 | 0.6591 | 0.109 | 0.3357 | 0.0785 | 0.2922 | 0.1227 | 0.4 | 0.1301 | 0.3333 |
| 1.5022 | 74.0 | 7881 | 1.8055 | 0.197 | 0.4341 | 0.1495 | 0.1022 | 0.0883 | 0.2747 | 0.2256 | 0.3898 | 0.4084 | 0.1913 | 0.2789 | 0.5424 | 0.5225 | 0.661 | 0.1332 | 0.34 | 0.0687 | 0.2891 | 0.1366 | 0.4286 | 0.1239 | 0.3232 |
| 1.5157 | 74.9953 | 7987 | 1.7750 | 0.1992 | 0.4404 | 0.1541 | 0.104 | 0.0937 | 0.2516 | 0.2332 | 0.3996 | 0.4154 | 0.1952 | 0.2843 | 0.5587 | 0.5311 | 0.6762 | 0.1331 | 0.36 | 0.0869 | 0.2964 | 0.1116 | 0.4071 | 0.1332 | 0.3373 |
| 1.4439 | 76.0 | 8094 | 1.8431 | 0.1877 | 0.4295 | 0.1374 | 0.0729 | 0.0844 | 0.2654 | 0.225 | 0.3975 | 0.4176 | 0.2597 | 0.2999 | 0.544 | 0.5215 | 0.6579 | 0.1322 | 0.3471 | 0.0673 | 0.2875 | 0.0936 | 0.4643 | 0.1241 | 0.3311 |
| 1.4989 | 76.9953 | 8200 | 1.8236 | 0.1907 | 0.4343 | 0.1425 | 0.055 | 0.095 | 0.2776 | 0.2183 | 0.3861 | 0.4059 | 0.1827 | 0.2745 | 0.5308 | 0.5238 | 0.6634 | 0.1084 | 0.33 | 0.0754 | 0.2849 | 0.1091 | 0.4 | 0.1368 | 0.3514 |
| 1.4759 | 78.0 | 8307 | 1.7953 | 0.1973 | 0.4484 | 0.15 | 0.077 | 0.0949 | 0.2868 | 0.2369 | 0.402 | 0.4161 | 0.1753 | 0.2936 | 0.5428 | 0.5239 | 0.6634 | 0.124 | 0.3514 | 0.0865 | 0.3109 | 0.0945 | 0.3976 | 0.1574 | 0.3571 |
| 1.4302 | 78.9953 | 8413 | 1.8257 | 0.1921 | 0.4446 | 0.1476 | 0.0569 | 0.1067 | 0.2706 | 0.2263 | 0.3944 | 0.4135 | 0.17 | 0.2982 | 0.5529 | 0.508 | 0.6591 | 0.1226 | 0.36 | 0.0782 | 0.3021 | 0.1114 | 0.3976 | 0.1403 | 0.3486 |
| 1.4879 | 80.0 | 8520 | 1.8216 | 0.1977 | 0.4478 | 0.1511 | 0.0607 | 0.1093 | 0.28 | 0.233 | 0.4025 | 0.4213 | 0.1947 | 0.3143 | 0.5392 | 0.5125 | 0.6573 | 0.1274 | 0.3514 | 0.087 | 0.3177 | 0.1197 | 0.4286 | 0.1421 | 0.3514 |
| 1.4674 | 80.9953 | 8626 | 1.8194 | 0.186 | 0.4463 | 0.1374 | 0.056 | 0.0953 | 0.28 | 0.2289 | 0.3882 | 0.4068 | 0.1807 | 0.2878 | 0.5349 | 0.5061 | 0.6457 | 0.1236 | 0.3371 | 0.0662 | 0.276 | 0.0917 | 0.4143 | 0.1424 | 0.361 |
| 1.4603 | 82.0 | 8733 | 1.7888 | 0.1925 | 0.4437 | 0.142 | 0.0727 | 0.0973 | 0.2871 | 0.2257 | 0.39 | 0.4054 | 0.1866 | 0.2849 | 0.5352 | 0.5014 | 0.6463 | 0.1343 | 0.33 | 0.0787 | 0.2995 | 0.1028 | 0.4 | 0.1455 | 0.3514 |
| 1.4798 | 82.9953 | 8839 | 1.8245 | 0.1922 | 0.4473 | 0.1358 | 0.0696 | 0.1126 | 0.2677 | 0.2258 | 0.3993 | 0.415 | 0.1864 | 0.3067 | 0.5338 | 0.5 | 0.6427 | 0.1361 | 0.3314 | 0.0868 | 0.3073 | 0.0965 | 0.4286 | 0.1414 | 0.365 |
| 1.4253 | 84.0 | 8946 | 1.7753 | 0.1932 | 0.4601 | 0.1409 | 0.0801 | 0.1169 | 0.2778 | 0.2316 | 0.4027 | 0.4172 | 0.1949 | 0.3178 | 0.5316 | 0.5108 | 0.6567 | 0.124 | 0.3329 | 0.0831 | 0.2937 | 0.0979 | 0.4452 | 0.1501 | 0.3576 |
| 1.4397 | 84.9953 | 9052 | 1.7778 | 0.2023 | 0.4698 | 0.1484 | 0.0747 | 0.1264 | 0.2903 | 0.2312 | 0.4076 | 0.423 | 0.2089 | 0.3229 | 0.5092 | 0.5011 | 0.6494 | 0.1172 | 0.3129 | 0.107 | 0.3214 | 0.1282 | 0.45 | 0.158 | 0.3814 |
| 1.4086 | 86.0 | 9159 | 1.7550 | 0.2015 | 0.472 | 0.1626 | 0.0926 | 0.1138 | 0.2819 | 0.2308 | 0.4085 | 0.4275 | 0.2033 | 0.3209 | 0.5665 | 0.5092 | 0.6591 | 0.1319 | 0.3543 | 0.0894 | 0.3042 | 0.1295 | 0.4571 | 0.1473 | 0.3627 |
| 1.4261 | 86.9953 | 9265 | 1.7907 | 0.1997 | 0.4662 | 0.1461 | 0.1078 | 0.1194 | 0.2946 | 0.2402 | 0.3993 | 0.4178 | 0.1766 | 0.3128 | 0.5568 | 0.5156 | 0.661 | 0.1338 | 0.3371 | 0.0888 | 0.301 | 0.1181 | 0.431 | 0.1423 | 0.3588 |
| 1.3943 | 88.0 | 9372 | 1.7906 | 0.1891 | 0.4431 | 0.133 | 0.0591 | 0.1053 | 0.2925 | 0.234 | 0.4004 | 0.419 | 0.1942 | 0.3249 | 0.5366 | 0.5019 | 0.6488 | 0.1097 | 0.3271 | 0.0877 | 0.3052 | 0.1014 | 0.4524 | 0.1446 | 0.3616 |
| 1.4016 | 88.9953 | 9478 | 1.7760 | 0.1929 | 0.4462 | 0.1424 | 0.0828 | 0.1114 | 0.2675 | 0.238 | 0.4045 | 0.4254 | 0.2069 | 0.3236 | 0.5476 | 0.5031 | 0.6616 | 0.121 | 0.3329 | 0.0897 | 0.3104 | 0.098 | 0.4548 | 0.1525 | 0.3672 |
| 1.3955 | 90.0 | 9585 | 1.7786 | 0.1955 | 0.4452 | 0.1534 | 0.0952 | 0.1147 | 0.2789 | 0.2383 | 0.3979 | 0.4189 | 0.2118 | 0.3166 | 0.5502 | 0.4977 | 0.6378 | 0.1173 | 0.3286 | 0.0946 | 0.312 | 0.1176 | 0.4405 | 0.1502 | 0.3757 |
| 1.4014 | 90.9953 | 9691 | 1.7644 | 0.1975 | 0.4627 | 0.1508 | 0.0865 | 0.1134 | 0.2852 | 0.2394 | 0.4042 | 0.4219 | 0.1963 | 0.3221 | 0.5521 | 0.5035 | 0.6549 | 0.1326 | 0.3371 | 0.0991 | 0.3193 | 0.1154 | 0.4429 | 0.1367 | 0.3554 |
| 1.3626 | 92.0 | 9798 | 1.7705 | 0.1993 | 0.4752 | 0.143 | 0.07 | 0.1152 | 0.2855 | 0.2361 | 0.4022 | 0.4221 | 0.1801 | 0.3227 | 0.5575 | 0.5064 | 0.6555 | 0.1307 | 0.3486 | 0.0898 | 0.299 | 0.1215 | 0.4429 | 0.1481 | 0.3644 |
| 1.3655 | 92.9953 | 9904 | 1.7689 | 0.2081 | 0.4735 | 0.1425 | 0.0975 | 0.1234 | 0.2855 | 0.2413 | 0.4007 | 0.422 | 0.198 | 0.3091 | 0.5544 | 0.5014 | 0.6616 | 0.1282 | 0.3386 | 0.0918 | 0.3073 | 0.1587 | 0.4381 | 0.1603 | 0.3644 |
| 1.3913 | 94.0 | 10011 | 1.7834 | 0.2003 | 0.4624 | 0.149 | 0.0951 | 0.1082 | 0.2823 | 0.2407 | 0.4025 | 0.4227 | 0.2077 | 0.2991 | 0.5416 | 0.4993 | 0.6628 | 0.1198 | 0.3371 | 0.1071 | 0.3208 | 0.1209 | 0.419 | 0.1545 | 0.3734 |
| 1.4071 | 94.9953 | 10117 | 1.7609 | 0.2046 | 0.4761 | 0.1521 | 0.0954 | 0.1232 | 0.2968 | 0.2392 | 0.4064 | 0.4251 | 0.1986 | 0.3225 | 0.5536 | 0.4975 | 0.6579 | 0.1359 | 0.3614 | 0.1034 | 0.3068 | 0.1342 | 0.4238 | 0.1522 | 0.3757 |
| 1.3651 | 96.0 | 10224 | 1.7628 | 0.2016 | 0.4717 | 0.1487 | 0.0995 | 0.1203 | 0.2895 | 0.2381 | 0.3965 | 0.416 | 0.1919 | 0.3111 | 0.5508 | 0.5079 | 0.664 | 0.1225 | 0.3371 | 0.0941 | 0.3005 | 0.1301 | 0.4095 | 0.1533 | 0.3689 |
| 1.3568 | 96.9953 | 10330 | 1.7858 | 0.2008 | 0.4706 | 0.15 | 0.0885 | 0.1223 | 0.286 | 0.2415 | 0.3952 | 0.4149 | 0.2044 | 0.31 | 0.5375 | 0.5041 | 0.6604 | 0.1152 | 0.3214 | 0.0946 | 0.3052 | 0.135 | 0.4214 | 0.1552 | 0.3661 |
| 1.3502 | 98.0 | 10437 | 1.7613 | 0.2041 | 0.4599 | 0.1525 | 0.0904 | 0.1249 | 0.2908 | 0.2395 | 0.4097 | 0.4292 | 0.2154 | 0.3233 | 0.553 | 0.5051 | 0.664 | 0.127 | 0.3557 | 0.1024 | 0.3177 | 0.1333 | 0.4357 | 0.1528 | 0.3729 |
| 1.3658 | 98.9953 | 10543 | 1.7623 | 0.2016 | 0.4641 | 0.1501 | 0.0908 | 0.1219 | 0.2895 | 0.2398 | 0.4019 | 0.4208 | 0.2099 | 0.3157 | 0.5456 | 0.5034 | 0.6598 | 0.1234 | 0.3314 | 0.095 | 0.3115 | 0.1326 | 0.4262 | 0.1536 | 0.3751 |
| 1.3272 | 99.5305 | 10600 | 1.7663 | 0.2022 | 0.4588 | 0.1509 | 0.0948 | 0.1223 | 0.2882 | 0.2396 | 0.405 | 0.4238 | 0.2134 | 0.3177 | 0.5501 | 0.5051 | 0.6628 | 0.1207 | 0.3371 | 0.0983 | 0.3115 | 0.1325 | 0.431 | 0.1545 | 0.3768 |
### Framework versions
- Transformers 4.40.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.18.0
- Tokenizers 0.19.0 | {"tags": ["object-detection", "vision", "generated_from_trainer"], "datasets": ["cppe-5"], "base_model": "jozhang97/deta-resnet-50", "model-index": [{"name": "jozhang97-deta-resnet-50-finetuned-10k-cppe5", "results": []}]} | qubvel-hf/jozhang97-deta-resnet-50-finetuned-10k-cppe5 | null | [
"transformers",
"safetensors",
"deta",
"object-detection",
"vision",
"generated_from_trainer",
"dataset:cppe-5",
"base_model:jozhang97/deta-resnet-50",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T20:41:52+00:00 | [] | [] | TAGS
#transformers #safetensors #deta #object-detection #vision #generated_from_trainer #dataset-cppe-5 #base_model-jozhang97/deta-resnet-50 #endpoints_compatible #region-us
| jozhang97-deta-resnet-50-finetuned-10k-cppe5
============================================
This model is a fine-tuned version of jozhang97/deta-resnet-50 on the cppe-5 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7663
* Map: 0.2022
* Map 50: 0.4588
* Map 75: 0.1509
* Map Small: 0.0948
* Map Medium: 0.1223
* Map Large: 0.2882
* Mar 1: 0.2396
* Mar 10: 0.405
* Mar 100: 0.4238
* Mar Small: 0.2134
* Mar Medium: 0.3177
* Mar Large: 0.5501
* Map Coverall: 0.5051
* Mar 100 Coverall: 0.6628
* Map Face Shield: 0.1207
* Mar 100 Face Shield: 0.3371
* Map Gloves: 0.0983
* Mar 100 Gloves: 0.3115
* Map Goggles: 0.1325
* Mar 100 Goggles: 0.431
* Map Mask: 0.1545
* Mar 100 Mask: 0.3768
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: 1337
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.40.0.dev0
* Pytorch 1.13.0+cu117
* Datasets 2.18.0
* Tokenizers 0.19.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 1337\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: linear\n* num\\_epochs: 100.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0.dev0\n* Pytorch 1.13.0+cu117\n* Datasets 2.18.0\n* Tokenizers 0.19.0"
] | [
"TAGS\n#transformers #safetensors #deta #object-detection #vision #generated_from_trainer #dataset-cppe-5 #base_model-jozhang97/deta-resnet-50 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 1337\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: linear\n* num\\_epochs: 100.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0.dev0\n* Pytorch 1.13.0+cu117\n* Datasets 2.18.0\n* Tokenizers 0.19.0"
] |
feature-extraction | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | alalfi/AraBART-summrize | null | [
"transformers",
"safetensors",
"mbart",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T20:43:54+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #mbart #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
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## Uses
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[optional]
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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### 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 #mbart #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
null | null |
This model is for Speech-to-text for eddcaller.com that transcribes content for phone systems. | {"license": "mit"} | vinniyo/eddcaller | null | [
"license:mit",
"region:us"
] | null | 2024-04-19T20:44:42+00:00 | [] | [] | TAGS
#license-mit #region-us
|
This model is for Speech-to-text for URL that transcribes content for phone systems. | [] | [
"TAGS\n#license-mit #region-us \n"
] |
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