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text-generation | transformers | Mistral-7B Japanese [LAPT + FOCUS]
===
## How to use
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/Mistral-7B-v0.1-focus-ja"
)
tokenizer = AutoTokenizer.from_pretrained(
"atsuki-yamaguchi/Mistral-7B-v0.1-focus-ja"
)
# w/ GPU
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/Mistral-7B-v0.1-focus-ja",
device_map="auto",
load_in_8bit=True,
)
```
## Citation
```
@article{yamaguchi2024empirical,
title={An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Generative {LLM} Inference},
author={Atsuki Yamaguchi and Aline Villavicencio and Nikolaos Aletras},
journal={ArXiv},
year={2024},
volume={abs/2402.10712},
url={https://arxiv.org/abs/2402.10712}
}
```
## Link
For more details, please visit https://github.com/gucci-j/llm-cva
| {"language": "ja", "license": "mit"} | atsuki-yamaguchi/Mistral-7B-v0.1-focus-ja | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"ja",
"arxiv:2402.10712",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T16:44:24+00:00 | [
"2402.10712"
] | [
"ja"
] | TAGS
#transformers #safetensors #mistral #text-generation #ja #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Mistral-7B Japanese [LAPT + FOCUS]
===
## How to use
## Link
For more details, please visit URL
| [
"## How to use",
"## Link\nFor more details, please visit URL"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #ja #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How to use",
"## Link\nFor more details, please visit URL"
] |
null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### 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]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
| {"library_name": "peft", "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0"} | bmehrba/TinyLlama-1.1B-Chat-v1.0-fine-tuned-adapters_Gpt4_tiny_Seed105 | null | [
"peft",
"arxiv:1910.09700",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"region:us"
] | null | 2024-04-21T16:44:53+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #arxiv-1910.09700 #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- 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
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
| [
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"## Model Card Contact",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16",
"### Framework versions\n\n\n- PEFT 0.7.0.dev0",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16",
"### Framework versions\n\n\n- PEFT 0.7.0.dev0"
] | [
"TAGS\n#peft #arxiv-1910.09700 #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## 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:",
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"### Model Architecture and Objective",
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"## Model Card Contact",
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"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16",
"### Framework versions\n\n\n- PEFT 0.7.0.dev0"
] |
null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### 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]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
| {"library_name": "peft", "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0"} | bmehrba/TinyLlama-1.1B-Chat-v1.0-fine-tuned_Gpt4_tiny_Seed105 | null | [
"peft",
"arxiv:1910.09700",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"region:us"
] | null | 2024-04-21T16:44:57+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #arxiv-1910.09700 #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- 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
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
| [
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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",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16",
"### Framework versions\n\n\n- PEFT 0.7.0.dev0"
] | [
"TAGS\n#peft #arxiv-1910.09700 #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \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",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16",
"### Framework versions\n\n\n- PEFT 0.7.0.dev0"
] |
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. -->
# gemma-2b-aya-chatml.sinhala
This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on the generator dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.2 | {"license": "gemma", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "google/gemma-2b", "model-index": [{"name": "gemma-2b-aya-chatml", "results": []}]} | Ransaka/gemma-2b-aya-chatml.sinhala | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:google/gemma-2b",
"license:gemma",
"region:us"
] | null | 2024-04-21T16:45:06+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-google/gemma-2b #license-gemma #region-us
|
# gemma-2b-aya-chatml.sinhala
This model is a fine-tuned version of google/gemma-2b on the generator dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.2 | [
"# gemma-2b-aya-chatml.sinhala\n\nThis model is a fine-tuned version of google/gemma-2b on the generator dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.2\n- Pytorch 2.1.2\n- Datasets 2.16.1\n- Tokenizers 0.15.2"
] | [
"TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-google/gemma-2b #license-gemma #region-us \n",
"# gemma-2b-aya-chatml.sinhala\n\nThis model is a fine-tuned version of google/gemma-2b on the generator dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.2\n- Pytorch 2.1.2\n- Datasets 2.16.1\n- Tokenizers 0.15.2"
] |
null | adapter-transformers |
# Adapter `BigTMiami/n_par_bn_v_1_help_class_20_e_adp_lr_0003_S_0` 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/n_par_bn_v_1_help_class_20_e_adp_lr_0003_S_0", 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_MICRO_helpfulness_dataset"]} | BigTMiami/n_par_bn_v_1_help_class_20_e_adp_lr_0003_S_0 | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/amazon_MICRO_helpfulness_dataset",
"region:us"
] | null | 2024-04-21T16:46:07+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/amazon_MICRO_helpfulness_dataset #region-us
|
# Adapter 'BigTMiami/n_par_bn_v_1_help_class_20_e_adp_lr_0003_S_0' 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/n_par_bn_v_1_help_class_20_e_adp_lr_0003_S_0' 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/n_par_bn_v_1_help_class_20_e_adp_lr_0003_S_0' 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 |
# llama3-discolm-orca
is a merge of the following models
* [Locutusque/llama-3-neural-chat-v1-8b](https://huggingface.co/Locutusque/llama-3-neural-chat-v1-8b)
* [Locutusque/Llama-3-Orca-1.0-8B](https://huggingface.co/Locutusque/Llama-3-Orca-1.0-8B)
* [DiscoResearch/Llama3_DiscoLM_German_8b_v0.1_experimental](https://huggingface.co/DiscoResearch/Llama3_DiscoLM_German_8b_v0.1_experimental)
This was mostly a proof of concept test. GGUF 4k quants here: [cstr/llama3-discolm-orca-GGUF](https://huggingface.co/cstr/llama3-discolm-orca-GGUF)
## 🧩 Configuration
[LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing) config:
```yaml
models:
- model: Locutusque/Llama-3-Orca-1.0-8B
# no parameters necessary for base model
- model: Locutusque/llama-3-neural-chat-v1-8b
parameters:
density: 0.60
weight: 0.15
- model: DiscoResearch/Llama3_DiscoLM_German_8b_v0.1_experimental
parameters:
density: 0.65
weight: 0.7
merge_method: dare_ties
base_model: Locutusque/Llama-3-Orca-1.0-8B
parameters:
int8_mask: true
dtype: bfloat16
random_seed: 0
tokenizer_source: base
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "cstr/llama3-discolm-orpo-t2"
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"])
``` | {"tags": ["merge", "mergekit", "lazymergekit", "Locutusque/llama-3-neural-chat-v1-8b", "DiscoResearch/Llama3_DiscoLM_German_8b_v0.1_experimental"], "base_model": ["Locutusque/llama-3-neural-chat-v1-8b", "DiscoResearch/Llama3_DiscoLM_German_8b_v0.1_experimental"]} | cstr/llama3-discolm-orca | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"Locutusque/llama-3-neural-chat-v1-8b",
"DiscoResearch/Llama3_DiscoLM_German_8b_v0.1_experimental",
"conversational",
"base_model:Locutusque/llama-3-neural-chat-v1-8b",
"base_model:DiscoResearch/Llama3_DiscoLM_German_8b_v0.1_experimental",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T16:46:17+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #merge #mergekit #lazymergekit #Locutusque/llama-3-neural-chat-v1-8b #DiscoResearch/Llama3_DiscoLM_German_8b_v0.1_experimental #conversational #base_model-Locutusque/llama-3-neural-chat-v1-8b #base_model-DiscoResearch/Llama3_DiscoLM_German_8b_v0.1_experimental #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# llama3-discolm-orca
is a merge of the following models
* Locutusque/llama-3-neural-chat-v1-8b
* Locutusque/Llama-3-Orca-1.0-8B
* DiscoResearch/Llama3_DiscoLM_German_8b_v0.1_experimental
This was mostly a proof of concept test. GGUF 4k quants here: cstr/llama3-discolm-orca-GGUF
## Configuration
LazyMergekit config:
## Usage
| [
"# llama3-discolm-orca\n\nis a merge of the following models\n* Locutusque/llama-3-neural-chat-v1-8b\n* Locutusque/Llama-3-Orca-1.0-8B\n* DiscoResearch/Llama3_DiscoLM_German_8b_v0.1_experimental\n\nThis was mostly a proof of concept test. GGUF 4k quants here: cstr/llama3-discolm-orca-GGUF",
"## Configuration\n\nLazyMergekit config:",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #merge #mergekit #lazymergekit #Locutusque/llama-3-neural-chat-v1-8b #DiscoResearch/Llama3_DiscoLM_German_8b_v0.1_experimental #conversational #base_model-Locutusque/llama-3-neural-chat-v1-8b #base_model-DiscoResearch/Llama3_DiscoLM_German_8b_v0.1_experimental #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# llama3-discolm-orca\n\nis a merge of the following models\n* Locutusque/llama-3-neural-chat-v1-8b\n* Locutusque/Llama-3-Orca-1.0-8B\n* DiscoResearch/Llama3_DiscoLM_German_8b_v0.1_experimental\n\nThis was mostly a proof of concept test. GGUF 4k quants here: cstr/llama3-discolm-orca-GGUF",
"## Configuration\n\nLazyMergekit config:",
"## Usage"
] |
null | keras |
## 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:
| Hyperparameters | Value |
| :-- | :-- |
| name | Adam |
| weight_decay | None |
| clipnorm | None |
| global_clipnorm | None |
| clipvalue | None |
| use_ema | False |
| ema_momentum | 0.99 |
| ema_overwrite_frequency | None |
| jit_compile | False |
| is_legacy_optimizer | False |
| learning_rate | 0.0010000000474974513 |
| beta_1 | 0.9 |
| beta_2 | 0.999 |
| epsilon | 1e-07 |
| amsgrad | False |
| training_precision | float32 |
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | {"library_name": "keras"} | Hitomiblood/blindness_modelKerasCPU | null | [
"keras",
"region:us"
] | null | 2024-04-21T16:47:41+00:00 | [] | [] | TAGS
#keras #region-us
| 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:
Model Plot
----------
View Model Plot
!Model Image
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] | [
"TAGS\n#keras #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] |
null | null | # Meta-Llama-3-70B-Instruct-gguf
[meta-llamaさんが公開しているMeta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct)のggufフォーマット変換版です。
eot_id対応してます。
imatrixのデータは[TFMC/imatrix-dataset-for-japanese-llm](https://huggingface.co/datasets/TFMC/imatrix-dataset-for-japanese-llm)を使用して作成しました。
## 分割されたファイルについて
q6_kやq8_0のファイルはサイズが大きく分割されているので結合する必要があります。
~~~bash
cat Meta-Llama-3-70B-Instruct-Q5_K_M.gguf.* > Meta-Llama-3-70B-Instruct-Q5_K_M.gguf
~~~
## Usage
```
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
make -j
./main -m 'Meta-Llama-3-70B-Instruct-Q4_0.gguf' -p "<|begin_of_text|><|start_header_id|>user <|end_header_id|>\n\nこんにちわ<|eot_id|><|start_header_id|>assistant <|end_header_id|>\n\n" -n 128
``` | {"language": ["en", "ja"], "license": "other", "tags": ["llama3"], "datasets": ["TFMC/imatrix-dataset-for-japanese-llm"], "license_name": "llama3", "license_link": "https://huggingface.co/meta-llama/Meta-Llama-3-8B/blob/main/LICENSE"} | mmnga/Meta-Llama-3-70B-Instruct-gguf | null | [
"gguf",
"llama3",
"en",
"ja",
"dataset:TFMC/imatrix-dataset-for-japanese-llm",
"license:other",
"region:us"
] | null | 2024-04-21T16:48:06+00:00 | [] | [
"en",
"ja"
] | TAGS
#gguf #llama3 #en #ja #dataset-TFMC/imatrix-dataset-for-japanese-llm #license-other #region-us
| # Meta-Llama-3-70B-Instruct-gguf
meta-llamaさんが公開しているMeta-Llama-3-70B-Instructのggufフォーマット変換版です。
eot_id対応してます。
imatrixのデータはTFMC/imatrix-dataset-for-japanese-llmを使用して作成しました。
## 分割されたファイルについて
q6_kやq8_0のファイルはサイズが大きく分割されているので結合する必要があります。
~~~bash
cat Meta-Llama-3-70B-Instruct-Q5_K_M.gguf.* > Meta-Llama-3-70B-Instruct-Q5_K_M.gguf
~~~
## Usage
| [
"# Meta-Llama-3-70B-Instruct-gguf \nmeta-llamaさんが公開しているMeta-Llama-3-70B-Instructのggufフォーマット変換版です。\n\neot_id対応してます。 \nimatrixのデータはTFMC/imatrix-dataset-for-japanese-llmを使用して作成しました。",
"## 分割されたファイルについて\nq6_kやq8_0のファイルはサイズが大きく分割されているので結合する必要があります。 \n\n~~~bash\ncat Meta-Llama-3-70B-Instruct-Q5_K_M.gguf.* > Meta-Llama-3-70B-Instruct-Q5_K_M.gguf\n~~~",
"## Usage"
] | [
"TAGS\n#gguf #llama3 #en #ja #dataset-TFMC/imatrix-dataset-for-japanese-llm #license-other #region-us \n",
"# Meta-Llama-3-70B-Instruct-gguf \nmeta-llamaさんが公開しているMeta-Llama-3-70B-Instructのggufフォーマット変換版です。\n\neot_id対応してます。 \nimatrixのデータはTFMC/imatrix-dataset-for-japanese-llmを使用して作成しました。",
"## 分割されたファイルについて\nq6_kやq8_0のファイルはサイズが大きく分割されているので結合する必要があります。 \n\n~~~bash\ncat Meta-Llama-3-70B-Instruct-Q5_K_M.gguf.* > Meta-Llama-3-70B-Instruct-Q5_K_M.gguf\n~~~",
"## Usage"
] |
text-to-image | diffusers |
<Gallery />
## Model description
These are leonickson1/grigg_building_uncc LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
LoRA for the text encoder was enabled: False.
Special VAE used for training: None.
## Trigger words
You should use photo of a sks building to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](leonickson1/grigg_building_uncc/tree/main) them in the Files & versions tab.
| {"license": "openrail++", "tags": ["autotrain", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "photo of a sks building"} | leonickson1/grigg_building_uncc | null | [
"diffusers",
"autotrain",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | null | 2024-04-21T16:48:47+00:00 | [] | [] | TAGS
#diffusers #autotrain #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #region-us
|
<Gallery />
## Model description
These are leonickson1/grigg_building_uncc LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
LoRA for the text encoder was enabled: False.
Special VAE used for training: None.
## Trigger words
You should use photo of a sks building to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
| [
"## Model description\n\nThese are leonickson1/grigg_building_uncc LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: None.",
"## Trigger words\n\nYou should use photo of a sks building to trigger the image generation.",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
"TAGS\n#diffusers #autotrain #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #region-us \n",
"## Model description\n\nThese are leonickson1/grigg_building_uncc LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: None.",
"## Trigger words\n\nYou should use photo of a sks building to trigger the image generation.",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] |
text-generation | transformers | TigerBot-7B Japanese [LAPT + CLP]
===
## How to use
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-clp-ja"
)
tokenizer = AutoTokenizer.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-clp-ja"
)
# w/ GPU
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-clp-ja",
device_map="auto",
load_in_8bit=True,
)
```
## Citation
```
@article{yamaguchi2024empirical,
title={An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Generative {LLM} Inference},
author={Atsuki Yamaguchi and Aline Villavicencio and Nikolaos Aletras},
journal={ArXiv},
year={2024},
volume={abs/2402.10712},
url={https://arxiv.org/abs/2402.10712}
}
```
## Link
For more details, please visit https://github.com/gucci-j/llm-cva
| {"language": "ja", "license": "mit"} | atsuki-yamaguchi/tigerbot-7b-base-clp-ja | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"ja",
"arxiv:2402.10712",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T16:49:05+00:00 | [
"2402.10712"
] | [
"ja"
] | TAGS
#transformers #safetensors #llama #text-generation #ja #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| TigerBot-7B Japanese [LAPT + CLP]
===
## How to use
## Link
For more details, please visit URL
| [
"## How to use",
"## Link\nFor more details, please visit URL"
] | [
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"## How to use",
"## Link\nFor more details, please visit URL"
] |
text-generation | transformers |
GPTQ 4-bit Quantized Llama-3 8B Instruct Model
Model Version: 1.0
Model Creator: CollAIborator (https://www.collaiborate.com)
Model Overview: This repo contains 4 Bit quantized GPTQ model files from meta-llama/Meta-Llama-3-8B-Instruct. This model is an optimized version to run on lower config GPUs and comes with a small quality degradation from the original model but the intent was to make Llama-3 available for use in smaller GPUs with maximum improvement in latency and throughput.
Intended Use: The GPTQ 4-bit Quantized Llama-3 8B Instruct Model is intended to be used for tasks involving instructional text comprehension, such as question answering, summarization, and instructional text generation. It can be deployed in applications where understanding and generating instructional content is crucial, including educational platforms, virtual assistants, and content recommendation systems.
Limitations and Considerations: While the GPTQ 4-bit Quantized Llama-3 8B Instruct Model demonstrates strong performance in tasks related to instructional text comprehension, it may not perform optimally in domains or tasks outside its training data distribution. Users should evaluate the model's performance on specific tasks and datasets before deploying it in production environments.
Ethical Considerations: As with any language model, the GPTQ 4-bit Quantized Llama-3 8B Instruct Model can potentially generate biased or inappropriate content based on the input it receives. Users are encouraged to monitor and evaluate the model's outputs to ensure they align with ethical guidelines and do not propagate harmful stereotypes or misinformation.
Disclaimer: The GPTQ 4-bit Quantized Llama-3 8B Instruct Model is provided by CollAIborator and is offered as-is, without any warranty or guarantee of performance. Users are solely responsible for the use and outcomes of the model in their applications.
Developed by: CollAIborator team
Model type: Text Generation
Language(s) (NLP): en
License: llama3
Finetuned from model [optional]: meta-llama/Meta-Llama-3-8B-Instruct
| {"language": ["en"], "license": "llama2", "library_name": "transformers"} | collAIborate/Meta-Llama-3-8B-Instruct-GPTQ-4Bit | null | [
"transformers",
"llama",
"text-generation",
"conversational",
"en",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-21T16:49:43+00:00 | [] | [
"en"
] | TAGS
#transformers #llama #text-generation #conversational #en #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
|
GPTQ 4-bit Quantized Llama-3 8B Instruct Model
Model Version: 1.0
Model Creator: CollAIborator (URL)
Model Overview: This repo contains 4 Bit quantized GPTQ model files from meta-llama/Meta-Llama-3-8B-Instruct. This model is an optimized version to run on lower config GPUs and comes with a small quality degradation from the original model but the intent was to make Llama-3 available for use in smaller GPUs with maximum improvement in latency and throughput.
Intended Use: The GPTQ 4-bit Quantized Llama-3 8B Instruct Model is intended to be used for tasks involving instructional text comprehension, such as question answering, summarization, and instructional text generation. It can be deployed in applications where understanding and generating instructional content is crucial, including educational platforms, virtual assistants, and content recommendation systems.
Limitations and Considerations: While the GPTQ 4-bit Quantized Llama-3 8B Instruct Model demonstrates strong performance in tasks related to instructional text comprehension, it may not perform optimally in domains or tasks outside its training data distribution. Users should evaluate the model's performance on specific tasks and datasets before deploying it in production environments.
Ethical Considerations: As with any language model, the GPTQ 4-bit Quantized Llama-3 8B Instruct Model can potentially generate biased or inappropriate content based on the input it receives. Users are encouraged to monitor and evaluate the model's outputs to ensure they align with ethical guidelines and do not propagate harmful stereotypes or misinformation.
Disclaimer: The GPTQ 4-bit Quantized Llama-3 8B Instruct Model is provided by CollAIborator and is offered as-is, without any warranty or guarantee of performance. Users are solely responsible for the use and outcomes of the model in their applications.
Developed by: CollAIborator team
Model type: Text Generation
Language(s) (NLP): en
License: llama3
Finetuned from model [optional]: meta-llama/Meta-Llama-3-8B-Instruct
| [] | [
"TAGS\n#transformers #llama #text-generation #conversational #en #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n"
] |
null | adapter-transformers |
# Adapter `BigTMiami/n_par_bn_v_1_help_class_5_e_adp_lr_0003_S_1` 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/n_par_bn_v_1_help_class_5_e_adp_lr_0003_S_1", 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_MICRO_helpfulness_dataset"]} | BigTMiami/n_par_bn_v_1_help_class_5_e_adp_lr_0003_S_1 | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/amazon_MICRO_helpfulness_dataset",
"region:us"
] | null | 2024-04-21T16:49:46+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/amazon_MICRO_helpfulness_dataset #region-us
|
# Adapter 'BigTMiami/n_par_bn_v_1_help_class_5_e_adp_lr_0003_S_1' 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/n_par_bn_v_1_help_class_5_e_adp_lr_0003_S_1' 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/n_par_bn_v_1_help_class_5_e_adp_lr_0003_S_1' 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"
] |
object-detection | 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": []} | Charliesgt/pollencounter_detr_resnet50-dc5 | null | [
"transformers",
"safetensors",
"detr",
"object-detection",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T16:49:59+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #detr #object-detection #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #detr #object-detection #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 | Mistral-7B Japanese [LAPT + Random]
===
## How to use
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/Mistral-7B-v0.1-random-ja"
)
tokenizer = AutoTokenizer.from_pretrained(
"atsuki-yamaguchi/Mistral-7B-v0.1-random-ja"
)
# w/ GPU
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/Mistral-7B-v0.1-random-ja",
device_map="auto",
load_in_8bit=True,
)
```
## Citation
```
@article{yamaguchi2024empirical,
title={An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Generative {LLM} Inference},
author={Atsuki Yamaguchi and Aline Villavicencio and Nikolaos Aletras},
journal={ArXiv},
year={2024},
volume={abs/2402.10712},
url={https://arxiv.org/abs/2402.10712}
}
```
## Link
For more details, please visit https://github.com/gucci-j/llm-cva
| {"language": "ja", "license": "mit"} | atsuki-yamaguchi/Mistral-7B-v0.1-random-ja | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"ja",
"arxiv:2402.10712",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T16:50:25+00:00 | [
"2402.10712"
] | [
"ja"
] | TAGS
#transformers #safetensors #mistral #text-generation #ja #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Mistral-7B Japanese [LAPT + Random]
===
## How to use
## Link
For more details, please visit URL
| [
"## How to use",
"## Link\nFor more details, please visit URL"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #ja #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How to use",
"## Link\nFor more details, please visit URL"
] |
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
<!-- 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]
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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]
**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": []} | qiao101660/bert-finetuing-uncased | null | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T16:51:05+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
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- Paper [optional]:
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## Uses
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
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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 #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]",
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"#### Speeds, Sizes, Times [optional]",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
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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 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 0.0_ablation_sample1_4iters_bs256_iter_4
This model is a fine-tuned version of [ZhangShenao/0.0_ablation_sample1_4iters_bs256_iter_3](https://huggingface.co/ZhangShenao/0.0_ablation_sample1_4iters_bs256_iter_3) on the ZhangShenao/0.0_ablation_sample1_4iters_bs256_dataset dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["ZhangShenao/0.0_ablation_sample1_4iters_bs256_dataset"], "base_model": "ZhangShenao/0.0_ablation_sample1_4iters_bs256_iter_3", "model-index": [{"name": "0.0_ablation_sample1_4iters_bs256_iter_4", "results": []}]} | ZhangShenao/0.0_ablation_sample1_4iters_bs256_iter_4 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:ZhangShenao/0.0_ablation_sample1_4iters_bs256_dataset",
"base_model:ZhangShenao/0.0_ablation_sample1_4iters_bs256_iter_3",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T16:53:44+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #alignment-handbook #generated_from_trainer #trl #dpo #conversational #dataset-ZhangShenao/0.0_ablation_sample1_4iters_bs256_dataset #base_model-ZhangShenao/0.0_ablation_sample1_4iters_bs256_iter_3 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# 0.0_ablation_sample1_4iters_bs256_iter_4
This model is a fine-tuned version of ZhangShenao/0.0_ablation_sample1_4iters_bs256_iter_3 on the ZhangShenao/0.0_ablation_sample1_4iters_bs256_dataset dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
| [
"# 0.0_ablation_sample1_4iters_bs256_iter_4\n\nThis model is a fine-tuned version of ZhangShenao/0.0_ablation_sample1_4iters_bs256_iter_3 on the ZhangShenao/0.0_ablation_sample1_4iters_bs256_dataset dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 256\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1",
"### Training results",
"### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.2"
] | [
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"# 0.0_ablation_sample1_4iters_bs256_iter_4\n\nThis model is a fine-tuned version of ZhangShenao/0.0_ablation_sample1_4iters_bs256_iter_3 on the ZhangShenao/0.0_ablation_sample1_4iters_bs256_dataset dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 256\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1",
"### Training results",
"### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.2"
] |
text-generation | transformers | BLOOM-7B Swahili [LAPT + FOCUS]
===
## How to use
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/bloom-7b1-focus-sw"
)
tokenizer = AutoTokenizer.from_pretrained(
"atsuki-yamaguchi/bloom-7b1-focus-sw"
)
# w/ GPU
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/bloom-7b1-focus-sw",
device_map="auto",
load_in_8bit=True,
)
```
## Citation
```
@article{yamaguchi2024empirical,
title={An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Generative {LLM} Inference},
author={Atsuki Yamaguchi and Aline Villavicencio and Nikolaos Aletras},
journal={ArXiv},
year={2024},
volume={abs/2402.10712},
url={https://arxiv.org/abs/2402.10712}
}
```
## Link
For more details, please visit https://github.com/gucci-j/llm-cva
| {"language": "sw", "license": "mit"} | atsuki-yamaguchi/bloom-7b1-focus-sw | null | [
"transformers",
"safetensors",
"bloom",
"text-generation",
"sw",
"arxiv:2402.10712",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T16:54:03+00:00 | [
"2402.10712"
] | [
"sw"
] | TAGS
#transformers #safetensors #bloom #text-generation #sw #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| BLOOM-7B Swahili [LAPT + FOCUS]
===
## How to use
## Link
For more details, please visit URL
| [
"## How to use",
"## Link\nFor more details, please visit URL"
] | [
"TAGS\n#transformers #safetensors #bloom #text-generation #sw #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How to use",
"## Link\nFor more details, please visit URL"
] |
text-generation | transformers | TigerBot-7B Arabic [LAPT + FOCUS]
===
## How to use
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-focus-ar"
)
tokenizer = AutoTokenizer.from_pretrained(
"aubmindlab/aragpt2-base"
)
# w/ GPU
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-focus-ar",
device_map="auto",
load_in_8bit=True,
)
```
## Citation
```
@article{yamaguchi2024empirical,
title={An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Generative {LLM} Inference},
author={Atsuki Yamaguchi and Aline Villavicencio and Nikolaos Aletras},
journal={ArXiv},
year={2024},
volume={abs/2402.10712},
url={https://arxiv.org/abs/2402.10712}
}
```
## Link
For more details, please visit https://github.com/gucci-j/llm-cva
| {"language": "ar", "license": "mit"} | atsuki-yamaguchi/tigerbot-7b-base-focus-ar | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"ar",
"arxiv:2402.10712",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T16:55:08+00:00 | [
"2402.10712"
] | [
"ar"
] | TAGS
#transformers #safetensors #llama #text-generation #ar #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| TigerBot-7B Arabic [LAPT + FOCUS]
===
## How to use
## Link
For more details, please visit URL
| [
"## How to use",
"## Link\nFor more details, please visit URL"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #ar #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How to use",
"## Link\nFor more details, please visit URL"
] |
token-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]
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- **Shared by [optional]:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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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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<!-- 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": []} | KaggleMasterX/BERT_Episode1 | null | [
"transformers",
"safetensors",
"bert",
"token-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T16:55:40+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #bert #token-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- 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]",
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"### 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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"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #bert #token-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]",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
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"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-generation | transformers | Mistral-7B Japanese [LAPT + CLP]
===
## How to use
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/Mistral-7B-v0.1-clp-ja"
)
tokenizer = AutoTokenizer.from_pretrained(
"atsuki-yamaguchi/Mistral-7B-v0.1-clp-ja"
)
# w/ GPU
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/Mistral-7B-v0.1-clp-ja",
device_map="auto",
load_in_8bit=True,
)
```
## Citation
```
@article{yamaguchi2024empirical,
title={An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Generative {LLM} Inference},
author={Atsuki Yamaguchi and Aline Villavicencio and Nikolaos Aletras},
journal={ArXiv},
year={2024},
volume={abs/2402.10712},
url={https://arxiv.org/abs/2402.10712}
}
```
## Link
For more details, please visit https://github.com/gucci-j/llm-cva
| {"language": "ja", "license": "mit"} | atsuki-yamaguchi/Mistral-7B-v0.1-clp-ja | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"ja",
"arxiv:2402.10712",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T16:56:21+00:00 | [
"2402.10712"
] | [
"ja"
] | TAGS
#transformers #safetensors #mistral #text-generation #ja #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Mistral-7B Japanese [LAPT + CLP]
===
## How to use
## Link
For more details, please visit URL
| [
"## How to use",
"## Link\nFor more details, please visit URL"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #ja #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How to use",
"## Link\nFor more details, please visit URL"
] |
null | adapter-transformers |
# Adapter `BigTMiami/n_par_bn_v_1_help_class_20_e_adp_lr_0003_S_1` 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/n_par_bn_v_1_help_class_20_e_adp_lr_0003_S_1", 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_MICRO_helpfulness_dataset"]} | BigTMiami/n_par_bn_v_1_help_class_20_e_adp_lr_0003_S_1 | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/amazon_MICRO_helpfulness_dataset",
"region:us"
] | null | 2024-04-21T16:56:30+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/amazon_MICRO_helpfulness_dataset #region-us
|
# Adapter 'BigTMiami/n_par_bn_v_1_help_class_20_e_adp_lr_0003_S_1' 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/n_par_bn_v_1_help_class_20_e_adp_lr_0003_S_1' 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"
] | [
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"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224
This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1355
## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.84 | 4 | 0.6557 |
| No log | 1.89 | 9 | 0.1355 |
| 0.0289 | 2.95 | 14 | 0.2163 |
| 0.0289 | 4.0 | 19 | 0.1560 |
| 0.0616 | 4.21 | 20 | 0.1556 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "model-index": [{"name": "swin-tiny-patch4-window7-224", "results": []}]} | t1msan/swin-tiny-patch4-window7-224 | null | [
"transformers",
"tensorboard",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T16:56:50+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #swin #image-classification #generated_from_trainer #dataset-imagefolder #autotrain_compatible #endpoints_compatible #region-us
| swin-tiny-patch4-window7-224
============================
This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1355
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: 64
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 5
### Training results
### Framework versions
* Transformers 4.39.3
* Pytorch 2.1.2
* Datasets 2.18.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5",
"### Training results",
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] | [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.1.2\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
text-generation | transformers | BLOOM-7B Swahili [LAPT + Random]
===
## How to use
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/bloom-7b1-random-sw"
)
tokenizer = AutoTokenizer.from_pretrained(
"atsuki-yamaguchi/bloom-7b1-random-sw"
)
# w/ GPU
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/bloom-7b1-random-sw",
device_map="auto",
load_in_8bit=True,
)
```
## Citation
```
@article{yamaguchi2024empirical,
title={An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Generative {LLM} Inference},
author={Atsuki Yamaguchi and Aline Villavicencio and Nikolaos Aletras},
journal={ArXiv},
year={2024},
volume={abs/2402.10712},
url={https://arxiv.org/abs/2402.10712}
}
```
## Link
For more details, please visit https://github.com/gucci-j/llm-cva
| {"language": "sw", "license": "mit"} | atsuki-yamaguchi/bloom-7b1-random-sw | null | [
"transformers",
"safetensors",
"bloom",
"text-generation",
"sw",
"arxiv:2402.10712",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T16:58:31+00:00 | [
"2402.10712"
] | [
"sw"
] | TAGS
#transformers #safetensors #bloom #text-generation #sw #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| BLOOM-7B Swahili [LAPT + Random]
===
## How to use
## Link
For more details, please visit URL
| [
"## How to use",
"## Link\nFor more details, please visit URL"
] | [
"TAGS\n#transformers #safetensors #bloom #text-generation #sw #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How to use",
"## Link\nFor more details, please visit URL"
] |
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. -->
# traductor_es_en
This model is a fine-tuned version of [Tahsin-Mayeesha/squad-bn-mt5-base2](https://huggingface.co/Tahsin-Mayeesha/squad-bn-mt5-base2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5293
- Bleu: 7.0139
- Gen Len: 17.3797
## 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: 5.6e-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 | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| No log | 1.0 | 320 | 1.5821 | 6.4196 | 17.3641 |
| 2.4998 | 2.0 | 640 | 1.5293 | 7.0139 | 17.3797 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["simplification", "generated_from_trainer"], "metrics": ["bleu"], "base_model": "Tahsin-Mayeesha/squad-bn-mt5-base2", "model-index": [{"name": "traductor_es_en", "results": []}]} | frluquba/traductor_es_en | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"simplification",
"generated_from_trainer",
"base_model:Tahsin-Mayeesha/squad-bn-mt5-base2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T17:01:16+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #t5 #text2text-generation #simplification #generated_from_trainer #base_model-Tahsin-Mayeesha/squad-bn-mt5-base2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| traductor\_es\_en
=================
This model is a fine-tuned version of Tahsin-Mayeesha/squad-bn-mt5-base2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5293
* Bleu: 7.0139
* Gen Len: 17.3797
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: 5.6e-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 2.2.1+cu121
* Datasets 2.19.0
* Tokenizers 0.19.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-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 2.2.1+cu121\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: 5.6e-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 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1"
] |
text-generation | transformers | TigerBot-7B Arabic [LAPT + Random]
===
## How to use
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-random-ar"
)
tokenizer = AutoTokenizer.from_pretrained(
"aubmindlab/aragpt2-base"
)
# w/ GPU
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-random-ar",
device_map="auto",
load_in_8bit=True,
)
```
## Citation
```
@article{yamaguchi2024empirical,
title={An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Generative {LLM} Inference},
author={Atsuki Yamaguchi and Aline Villavicencio and Nikolaos Aletras},
journal={ArXiv},
year={2024},
volume={abs/2402.10712},
url={https://arxiv.org/abs/2402.10712}
}
```
## Link
For more details, please visit https://github.com/gucci-j/llm-cva
| {"language": "ar", "license": "mit"} | atsuki-yamaguchi/tigerbot-7b-base-random-ar | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"ar",
"arxiv:2402.10712",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T17:01:19+00:00 | [
"2402.10712"
] | [
"ar"
] | TAGS
#transformers #safetensors #llama #text-generation #ar #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| TigerBot-7B Arabic [LAPT + Random]
===
## How to use
## Link
For more details, please visit URL
| [
"## How to use",
"## Link\nFor more details, please visit URL"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #ar #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How to use",
"## Link\nFor more details, please visit URL"
] |
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
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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]
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## Technical Specifications [optional]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | mryannugent/self-finetune-miscondetect-bloomz-3b | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T17:01:29+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:
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- Shared by [optional]:
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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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### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
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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 #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 |
The original Llama 3 8b (base) special token weights are zero, which might cause NaN gradients. This version re-initialized the weights of all the following special tokens to alleviate the problem.
```
<|eot_id|>
<|start_header_id|>
<|end_header_id|>
```
We set the weights of these tokens in `embed` and `lm_head` to be the mean of all other tokens.
Code for making this model:
```python
import argparse
import transformers
import torch
def init_eot_embedding_llama3(model_path, output_dir, special_tokens=["<|eot_id|>", "<|start_header_id|>", "<|end_header_id|>"], mean_cutoff=128000, dtype=torch.bfloat16):
tokenizer = transformers.AutoTokenizer.from_pretrained(model_path)
model = transformers.AutoModelForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, torch_dtype=dtype)
assert model.model.embed_tokens.weight.shape[0] >= mean_cutoff
assert model.lm_head.weight.shape[0] >= mean_cutoff
with torch.no_grad():
for token in special_tokens:
token_id = tokenizer.convert_tokens_to_ids(token)
print (f"Token {token} ID {token_id}")
model.model.embed_tokens.weight[token_id] = torch.mean(model.model.embed_tokens.weight[:mean_cutoff].to(torch.float32), dim=0).to(dtype)
model.lm_head.weight[token_id] = torch.mean(model.lm_head.weight[:mean_cutoff].to(torch.float32), dim=0).to(dtype)
# Save
tokenizer.save_pretrained(output_dir)
model.save_pretrained(output_dir)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--model-path",
help="Location of model, or HuggingFace repo ID",
)
parser.add_argument(
"--output-dir",
help="Location to write resulting model and tokenizer",
)
init_eot_embedding_llama3(**vars(parser.parse_args()))
if __name__ == "__main__":
main()
``` | {"license": "other", "license_name": "llama3", "license_link": "LICENSE"} | imone/Llama-3-8B-fixed-special-embedding | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T17:01:58+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
The original Llama 3 8b (base) special token weights are zero, which might cause NaN gradients. This version re-initialized the weights of all the following special tokens to alleviate the problem.
We set the weights of these tokens in 'embed' and 'lm_head' to be the mean of all other tokens.
Code for making this model:
| [] | [
"TAGS\n#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
CodeLlama-70b-hf - bnb 4bits
- Model creator: https://huggingface.co/meta-llama/
- Original model: https://huggingface.co/meta-llama/CodeLlama-70b-hf/
Original model description:
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language:
- code
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
license: llama2
---
# **Code Llama**
Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the base 70B version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom.
| | Base Model | Python | Instruct |
| --- | ----------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- |
| 7B | [meta-llama/CodeLlama-7b-hf](https://huggingface.co/meta-llama/CodeLlama-7b-hf) | [meta-llama/CodeLlama-7b-Python-hf](https://huggingface.co/meta-llama/CodeLlama-7b-Python-hf) | [meta-llama/CodeLlama-7b-Instruct-hf](https://huggingface.co/meta-llama/CodeLlama-7b-Instruct-hf) |
| 13B | [meta-llama/CodeLlama-13b-hf](https://huggingface.co/meta-llama/CodeLlama-13b-hf) | [meta-llama/CodeLlama-13b-Python-hf](https://huggingface.co/meta-llama/CodeLlama-13b-Python-hf) | [meta-llama/CodeLlama-13b-Instruct-hf](https://huggingface.co/meta-llama/CodeLlama-13b-Instruct-hf) |
| 34B | [meta-llama/CodeLlama-34b-hf](https://huggingface.co/meta-llama/CodeLlama-34b-hf) | [meta-llama/CodeLlama-34b-Python-hf](https://huggingface.co/meta-llama/CodeLlama-34b-Python-hf) | [meta-llama/CodeLlama-34b-Instruct-hf](https://huggingface.co/meta-llama/CodeLlama-34b-Instruct-hf) |
| 70B | [meta-llama/CodeLlama-70b-hf](https://huggingface.co/meta-llama/CodeLlama-70b-hf) | [meta-llama/CodeLlama-70b-Python-hf](https://huggingface.co/meta-llama/CodeLlama-70b-Python-hf) | [meta-llama/CodeLlama-70b-Instruct-hf](https://huggingface.co/meta-llama/CodeLlama-70b-Instruct-hf) |
## Model Use
To use this model, please make sure to install `transformers`:
```bash
pip install transformers accelerate
```
Model capabilities:
- [x] Code completion.
- [ ] Infilling.
- [ ] Instructions / chat.
- [ ] Python specialist.
## Model Details
*Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs).
**Model Developers** Meta
**Variations** Code Llama comes in four model sizes, and three variants:
* Code Llama: base models designed for general code synthesis and understanding
* Code Llama - Python: designed specifically for Python
* Code Llama - Instruct: for instruction following and safer deployment
All variants are available in sizes of 7B, 13B, 34B, and 70B parameters.
**This repository contains the base version of the 70B parameters model.**
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Code Llama is an auto-regressive language model that uses an optimized transformer architecture. It was fine-tuned with up to 16k tokens and supports up to 100k tokens at inference time.
**Model Dates** Code Llama and its variants have been trained between January 2023 and January 2024.
**Status** This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
**Research Paper** More information can be found in the paper "[Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/)" or its [arXiv page](https://arxiv.org/abs/2308.12950).
## Intended Use
**Intended Use Cases** Code Llama and its variants are intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications.
**Out-of-Scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants.
## Hardware and Software
**Training Factors** We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster.
**Carbon Footprint** In aggregate, training all 12 Code Llama models required 1400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 228.55 tCO2eq, 100% of which were offset by Meta’s sustainability program.
## Evaluation Results
See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper.
## Ethical Considerations and Limitations
Code Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available available at [https://ai.meta.com/llama/responsible-use-guide](https://ai.meta.com/llama/responsible-use-guide).
| {} | RichardErkhov/meta-llama_-_CodeLlama-70b-hf-4bits | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:2308.12950",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-21T17:03:14+00:00 | [
"2308.12950"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #arxiv-2308.12950 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
| Quantization made by Richard Erkhov.
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CodeLlama-70b-hf - bnb 4bits
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### Llama 2 Acceptable Use Policy
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```
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* code
pipeline\_tag: text-generation
tags:
* facebook
* meta
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license: llama2
---
Code Llama
==========
Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the base 70B version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom.
Model Use
---------
To use this model, please make sure to install 'transformers':
Model capabilities:
* [x] Code completion.
* [ ] Infilling.
* [ ] Instructions / chat.
* [ ] Python specialist.
Model Details
-------------
\*Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs).
Model Developers Meta
Variations Code Llama comes in four model sizes, and three variants:
* Code Llama: base models designed for general code synthesis and understanding
* Code Llama - Python: designed specifically for Python
* Code Llama - Instruct: for instruction following and safer deployment
All variants are available in sizes of 7B, 13B, 34B, and 70B parameters.
This repository contains the base version of the 70B parameters model.
Input Models input text only.
Output Models generate text only.
Model Architecture Code Llama is an auto-regressive language model that uses an optimized transformer architecture. It was fine-tuned with up to 16k tokens and supports up to 100k tokens at inference time.
Model Dates Code Llama and its variants have been trained between January 2023 and January 2024.
Status This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback.
License A custom commercial license is available at: URL
Research Paper More information can be found in the paper "Code Llama: Open Foundation Models for Code" or its arXiv page.
Intended Use
------------
Intended Use Cases Code Llama and its variants are intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications.
Out-of-Scope Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants.
Hardware and Software
---------------------
Training Factors We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster.
Carbon Footprint In aggregate, training all 12 Code Llama models required 1400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 228.55 tCO2eq, 100% of which were offset by Meta’s sustainability program.
Evaluation Results
------------------
See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper.
Ethical Considerations and Limitations
--------------------------------------
Code Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available available at URL
| [
"### LLAMA 2 COMMUNITY LICENSE AGREEMENT\n\n\n\"Agreement\" means the terms and conditions for use, reproduction, distribution\nand modification of the Llama Materials set forth herein.\n\"Documentation\" means the specifications, manuals and documentation\naccompanying Llama 2 distributed by Meta at\nURL \n\n\"Licensee\" or \"you\" means you, or your employer or any other person or entity\n(if you are entering into this Agreement on such person or entity's behalf),\nof the age required under applicable laws, rules or regulations to provide\nlegal consent and that has legal authority to bind your employer or such other\nperson or entity if you are entering in this Agreement on their behalf.\n\"Llama 2\" means the foundational large language models and software and\nalgorithms, including machine-learning model code, trained model weights,\ninference-enabling code, training-enabling code, fine-tuning enabling code and\nother elements of the foregoing distributed by Meta at\nURL\n\"Llama Materials\" means, collectively, Meta's proprietary Llama 2 and\ndocumentation (and any portion thereof) made available under this Agreement.\n\"Meta\" or \"we\" means Meta Platforms Ireland Limited (if you are located in or,\nif you are an entity, your principal place of business is in the EEA or\nSwitzerland) and Meta Platforms, Inc. (if you are located outside of the EEA\nor Switzerland).\nBy clicking \"I Accept\" below or by using or distributing any portion or\nelement of the Llama Materials, you agree to be bound by this Agreement.\n\n\n1. License Rights and Redistribution.\na. Grant of Rights. You are granted a non-exclusive, worldwide, non-\ntransferable and royalty-free limited license under Meta's intellectual\nproperty or other rights owned by Meta embodied in the Llama Materials to\nuse, reproduce, distribute, copy, create derivative works of, and make\nmodifications to the Llama Materials.\n\n\nb. Redistribution and Use. \n\ni. If you distribute or make the Llama Materials, or any derivative works\nthereof, available to a third party, you shall provide a copy of this\nAgreement to such third party.\nii. If you receive Llama Materials, or any derivative works thereof, from a\nLicensee as part of an integrated end user product, then Section 2 of this\nAgreement will not apply to you.\niii. You must retain in all copies of the Llama Materials that you distribute\nthe following attribution notice within a \"Notice\" text file distributed as a\npart of such copies: \"Llama 2 is licensed under the LLAMA 2 Community\nLicense, Copyright (c) Meta Platforms, Inc. All Rights Reserved.\"\niv. Your use of the Llama Materials must comply with applicable laws and\nregulations (including trade compliance laws and regulations) and adhere to\nthe Acceptable Use Policy for the Llama Materials (available at\nURL which is hereby incorporated by\nreference into this Agreement.\nv. You will not use the Llama Materials or any output or results of the Llama\nMaterials to improve any other large language model (excluding Llama 2 or\nderivative works thereof). \n\n2. Additional Commercial Terms. If, on the Llama 2 version release date, the\nmonthly active users of the products or services made available by or for\nLicensee, or Licensee's affiliates, is greater than 700 million monthly\nactive users in the preceding calendar month, you must request a license from\nMeta, which Meta may grant to you in its sole discretion, and you are not\nauthorized to exercise any of the rights under this Agreement unless or until\nMeta otherwise expressly grants you such rights.\n3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA\nMATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN \"AS IS\"\nBASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING,\nWITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT,\nMERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY\nRESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING\nTHE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE\nLLAMA MATERIALS AND ANY OUTPUT AND RESULTS.\n4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE\nUNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE,\nPRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST\nPROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR\nPUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE\nPOSSIBILITY OF ANY OF THE FOREGOING.\n5. Intellectual Property.\na. No trademark licenses are granted under this Agreement, and in connection\nwith the Llama Materials, neither Meta nor Licensee may use any name or mark\nowned by or associated with the other or any of its affiliates, except as\nrequired for reasonable and customary use in describing and redistributing\nthe Llama Materials.\nb. Subject to Meta's ownership of Llama Materials and derivatives made by or\nfor Meta, with respect to any derivative works and modifications of the Llama\nMaterials that are made by you, as between you and Meta, you are and will be\nthe owner of such derivative works and modifications.\nc. If you institute litigation or other proceedings against Meta or any\nentity (including a cross-claim or counterclaim in a lawsuit) alleging that\nthe Llama Materials or Llama 2 outputs or results, or any portion of any of\nthe foregoing, constitutes infringement of intellectual property or other\nrights owned or licensable by you, then any licenses granted to you under\nthis Agreement shall terminate as of the date such litigation or claim is\nfiled or instituted. You will indemnify and hold harmless Meta from and\nagainst any claim by any third party arising out of or related to your use or\ndistribution of the Llama Materials.\n6. Term and Termination. The term of this Agreement will commence upon your\nacceptance of this Agreement or access to the Llama Materials and will\ncontinue in full force and effect until terminated in accordance with the\nterms and conditions herein. Meta may terminate this Agreement if you are in\nbreach of any term or condition of this Agreement. Upon termination of this\nAgreement, you shall delete and cease use of the Llama Materials. Sections 3,\n4 and 7 shall survive the termination of this Agreement.\n7. Governing Law and Jurisdiction. This Agreement will be governed and\nconstrued under the laws of the State of California without regard to choice\nof law principles, and the UN Convention on Contracts for the International\nSale of Goods does not apply to this Agreement. The courts of California\nshall have exclusive jurisdiction of any dispute arising out of this\nAgreement.\nUSE POLICY",
"### Llama 2 Acceptable Use Policy\n\n\nMeta is committed to promoting safe and fair use of its tools and features,\nincluding Llama 2. If you access or use Llama 2, you agree to this Acceptable\nUse Policy (“Policy”). The most recent copy of this policy can be found at\nURL",
"#### Prohibited Uses\n\n\nWe want everyone to use Llama 2 safely and responsibly. You agree you will not\nuse, or allow others to use, Llama 2 to:\n\n\n1. Violate the law or others’ rights, including to:\n1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:\n\t1. Violence or terrorism\n\t2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material\n\t3. Human trafficking, exploitation, and sexual violence\n\t4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.\n\t5. Sexual solicitation\n\t6. Any other criminal activity\n\n\n\n```\n2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals\n3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services\n4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices \n5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws\n6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials\n7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system \n\n```\n\n2. Engage in, promote, incite, facilitate, or assist in the planning or\ndevelopment of activities that present a risk of death or bodily harm to\nindividuals, including use of Llama 2 related to the following:\n1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State\n2. Guns and illegal weapons (including weapon development)\n3. Illegal drugs and regulated/controlled substances\n4. Operation of critical infrastructure, transportation technologies, or heavy machinery\n5. Self-harm or harm to others, including suicide, cutting, and eating disorders\n6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual\n3. Intentionally deceive or mislead others, including use of Llama 2 related\nto the following:\n1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation\n2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content\n3. Generating, promoting, or further distributing spam\n4. Impersonating another individual without consent, authorization, or legal right\n5. Representing that the use of Llama 2 or outputs are human-generated\n6. 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 “bug,” or other problems\nthat could lead to a violation of this Policy through one of the following\nmeans:\n\n\n* Reporting issues with the model:\nURL\n* Reporting risky content generated by the model:\nURL\n* Reporting bugs and security concerns:\nURL\n* Reporting violations of the Acceptable Use Policy or unlicensed uses of\nLlama: LlamaUseReport@URL\nextra\\_gated\\_fields:\nFirst Name: text\nLast Name: text\nDate of birth: date\\_picker\nCountry: country\nAffiliation: text\ngeo: ip\\_location \n\nBy 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\nextra\\_gated\\_description: The information you provide will be collected, stored, processed and shared in accordance with the Meta Privacy Policy.\nextra\\_gated\\_button\\_content: Submit\nlanguage:\n\n\n* code\npipeline\\_tag: text-generation\ntags:\n* facebook\n* meta\n* pytorch\n* llama\n* llama-2\nlicense: llama2\n\n\n\n\n---\n\n\nCode Llama\n==========\n\n\nCode Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the base 70B version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom.\n\n\n\nModel Use\n---------\n\n\nTo use this model, please make sure to install 'transformers':\n\n\nModel capabilities:\n\n\n* [x] Code completion.\n* [ ] Infilling.\n* [ ] Instructions / chat.\n* [ ] Python specialist.\n\n\nModel Details\n-------------\n\n\n\\*Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs).\n\n\nModel Developers Meta\n\n\nVariations Code Llama comes in four model sizes, and three variants:\n\n\n* Code Llama: base models designed for general code synthesis and understanding\n* Code Llama - Python: designed specifically for Python\n* Code Llama - Instruct: for instruction following and safer deployment\n\n\nAll variants are available in sizes of 7B, 13B, 34B, and 70B parameters.\n\n\nThis repository contains the base version of the 70B parameters model.\n\n\nInput Models input text only.\n\n\nOutput Models generate text only.\n\n\nModel Architecture Code Llama is an auto-regressive language model that uses an optimized transformer architecture. It was fine-tuned with up to 16k tokens and supports up to 100k tokens at inference time.\n\n\nModel Dates Code Llama and its variants have been trained between January 2023 and January 2024.\n\n\nStatus This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback.\n\n\nLicense A custom commercial license is available at: URL\n\n\nResearch Paper More information can be found in the paper \"Code Llama: Open Foundation Models for Code\" or its arXiv page.\n\n\nIntended Use\n------------\n\n\nIntended Use Cases Code Llama and its variants are intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications.\n\n\nOut-of-Scope Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants.\n\n\nHardware and Software\n---------------------\n\n\nTraining Factors We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster.\n\n\nCarbon Footprint In aggregate, training all 12 Code Llama models required 1400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 228.55 tCO2eq, 100% of which were offset by Meta’s sustainability program.\n\n\nEvaluation Results\n------------------\n\n\nSee evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper.\n\n\nEthical Considerations and Limitations\n--------------------------------------\n\n\nCode Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model.\n\n\nPlease see the Responsible Use Guide available available at URL"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #arxiv-2308.12950 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n",
"### LLAMA 2 COMMUNITY LICENSE AGREEMENT\n\n\n\"Agreement\" means the terms and conditions for use, reproduction, distribution\nand modification of the Llama Materials set forth herein.\n\"Documentation\" means the specifications, manuals and documentation\naccompanying Llama 2 distributed by Meta at\nURL \n\n\"Licensee\" or \"you\" means you, or your employer or any other person or entity\n(if you are entering into this Agreement on such person or entity's behalf),\nof the age required under applicable laws, rules or regulations to provide\nlegal consent and that has legal authority to bind your employer or such other\nperson or entity if you are entering in this Agreement on their behalf.\n\"Llama 2\" means the foundational large language models and software and\nalgorithms, including machine-learning model code, trained model weights,\ninference-enabling code, training-enabling code, fine-tuning enabling code and\nother elements of the foregoing distributed by Meta at\nURL\n\"Llama Materials\" means, collectively, Meta's proprietary Llama 2 and\ndocumentation (and any portion thereof) made available under this Agreement.\n\"Meta\" or \"we\" means Meta Platforms Ireland Limited (if you are located in or,\nif you are an entity, your principal place of business is in the EEA or\nSwitzerland) and Meta Platforms, Inc. 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If, on the Llama 2 version release date, the\nmonthly active users of the products or services made available by or for\nLicensee, or Licensee's affiliates, is greater than 700 million monthly\nactive users in the preceding calendar month, you must request a license from\nMeta, which Meta may grant to you in its sole discretion, and you are not\nauthorized to exercise any of the rights under this Agreement unless or until\nMeta otherwise expressly grants you such rights.\n3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA\nMATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN \"AS IS\"\nBASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING,\nWITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT,\nMERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. 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Subject to Meta's ownership of Llama Materials and derivatives made by or\nfor Meta, with respect to any derivative works and modifications of the Llama\nMaterials that are made by you, as between you and Meta, you are and will be\nthe owner of such derivative works and modifications.\nc. If you institute litigation or other proceedings against Meta or any\nentity (including a cross-claim or counterclaim in a lawsuit) alleging that\nthe Llama Materials or Llama 2 outputs or results, or any portion of any of\nthe foregoing, constitutes infringement of intellectual property or other\nrights owned or licensable by you, then any licenses granted to you under\nthis Agreement shall terminate as of the date such litigation or claim is\nfiled or instituted. You will indemnify and hold harmless Meta from and\nagainst any claim by any third party arising out of or related to your use or\ndistribution of the Llama Materials.\n6. Term and Termination. The term of this Agreement will commence upon your\nacceptance of this Agreement or access to the Llama Materials and will\ncontinue in full force and effect until terminated in accordance with the\nterms and conditions herein. Meta may terminate this Agreement if you are in\nbreach of any term or condition of this Agreement. Upon termination of this\nAgreement, you shall delete and cease use of the Llama Materials. Sections 3,\n4 and 7 shall survive the termination of this Agreement.\n7. Governing Law and Jurisdiction. This Agreement will be governed and\nconstrued under the laws of the State of California without regard to choice\nof law principles, and the UN Convention on Contracts for the International\nSale of Goods does not apply to this Agreement. The courts of California\nshall have exclusive jurisdiction of any dispute arising out of this\nAgreement.\nUSE POLICY",
"### Llama 2 Acceptable Use Policy\n\n\nMeta is committed to promoting safe and fair use of its tools and features,\nincluding Llama 2. If you access or use Llama 2, you agree to this Acceptable\nUse Policy (“Policy”). The most recent copy of this policy can be found at\nURL",
"#### Prohibited Uses\n\n\nWe want everyone to use Llama 2 safely and responsibly. You agree you will not\nuse, or allow others to use, Llama 2 to:\n\n\n1. Violate the law or others’ rights, including to:\n1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:\n\t1. Violence or terrorism\n\t2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material\n\t3. Human trafficking, exploitation, and sexual violence\n\t4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.\n\t5. Sexual solicitation\n\t6. Any other criminal activity\n\n\n\n```\n2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals\n3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services\n4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices \n5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws\n6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials\n7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system \n\n```\n\n2. Engage in, promote, incite, facilitate, or assist in the planning or\ndevelopment of activities that present a risk of death or bodily harm to\nindividuals, including use of Llama 2 related to the following:\n1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State\n2. Guns and illegal weapons (including weapon development)\n3. Illegal drugs and regulated/controlled substances\n4. Operation of critical infrastructure, transportation technologies, or heavy machinery\n5. Self-harm or harm to others, including suicide, cutting, and eating disorders\n6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual\n3. Intentionally deceive or mislead others, including use of Llama 2 related\nto the following:\n1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation\n2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content\n3. Generating, promoting, or further distributing spam\n4. Impersonating another individual without consent, authorization, or legal right\n5. Representing that the use of Llama 2 or outputs are human-generated\n6. 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 “bug,” or other problems\nthat could lead to a violation of this Policy through one of the following\nmeans:\n\n\n* Reporting issues with the model:\nURL\n* Reporting risky content generated by the model:\nURL\n* Reporting bugs and security concerns:\nURL\n* Reporting violations of the Acceptable Use Policy or unlicensed uses of\nLlama: LlamaUseReport@URL\nextra\\_gated\\_fields:\nFirst Name: text\nLast Name: text\nDate of birth: date\\_picker\nCountry: country\nAffiliation: text\ngeo: ip\\_location \n\nBy 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\nextra\\_gated\\_description: The information you provide will be collected, stored, processed and shared in accordance with the Meta Privacy Policy.\nextra\\_gated\\_button\\_content: Submit\nlanguage:\n\n\n* code\npipeline\\_tag: text-generation\ntags:\n* facebook\n* meta\n* pytorch\n* llama\n* llama-2\nlicense: llama2\n\n\n\n\n---\n\n\nCode Llama\n==========\n\n\nCode Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the base 70B version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom.\n\n\n\nModel Use\n---------\n\n\nTo use this model, please make sure to install 'transformers':\n\n\nModel capabilities:\n\n\n* [x] Code completion.\n* [ ] Infilling.\n* [ ] Instructions / chat.\n* [ ] Python specialist.\n\n\nModel Details\n-------------\n\n\n\\*Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs).\n\n\nModel Developers Meta\n\n\nVariations Code Llama comes in four model sizes, and three variants:\n\n\n* Code Llama: base models designed for general code synthesis and understanding\n* Code Llama - Python: designed specifically for Python\n* Code Llama - Instruct: for instruction following and safer deployment\n\n\nAll variants are available in sizes of 7B, 13B, 34B, and 70B parameters.\n\n\nThis repository contains the base version of the 70B parameters model.\n\n\nInput Models input text only.\n\n\nOutput Models generate text only.\n\n\nModel Architecture Code Llama is an auto-regressive language model that uses an optimized transformer architecture. It was fine-tuned with up to 16k tokens and supports up to 100k tokens at inference time.\n\n\nModel Dates Code Llama and its variants have been trained between January 2023 and January 2024.\n\n\nStatus This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback.\n\n\nLicense A custom commercial license is available at: URL\n\n\nResearch Paper More information can be found in the paper \"Code Llama: Open Foundation Models for Code\" or its arXiv page.\n\n\nIntended Use\n------------\n\n\nIntended Use Cases Code Llama and its variants are intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications.\n\n\nOut-of-Scope Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants.\n\n\nHardware and Software\n---------------------\n\n\nTraining Factors We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster.\n\n\nCarbon Footprint In aggregate, training all 12 Code Llama models required 1400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 228.55 tCO2eq, 100% of which were offset by Meta’s sustainability program.\n\n\nEvaluation Results\n------------------\n\n\nSee evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper.\n\n\nEthical Considerations and Limitations\n--------------------------------------\n\n\nCode Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model.\n\n\nPlease see the Responsible Use Guide available available at URL"
] |
text-generation | transformers | BLOOM-7B Swahili [LAPT + CLP]
===
## How to use
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/bloom-7b1-clp-sw"
)
tokenizer = AutoTokenizer.from_pretrained(
"atsuki-yamaguchi/bloom-7b1-clp-sw"
)
# w/ GPU
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/bloom-7b1-clp-sw",
device_map="auto",
load_in_8bit=True,
)
```
## Citation
```
@article{yamaguchi2024empirical,
title={An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Generative {LLM} Inference},
author={Atsuki Yamaguchi and Aline Villavicencio and Nikolaos Aletras},
journal={ArXiv},
year={2024},
volume={abs/2402.10712},
url={https://arxiv.org/abs/2402.10712}
}
```
## Link
For more details, please visit https://github.com/gucci-j/llm-cva
| {"language": "sw", "license": "mit"} | atsuki-yamaguchi/bloom-7b1-clp-sw | null | [
"transformers",
"safetensors",
"bloom",
"text-generation",
"sw",
"arxiv:2402.10712",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T17:03:21+00:00 | [
"2402.10712"
] | [
"sw"
] | TAGS
#transformers #safetensors #bloom #text-generation #sw #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| BLOOM-7B Swahili [LAPT + CLP]
===
## How to use
## Link
For more details, please visit URL
| [
"## How to use",
"## Link\nFor more details, please visit URL"
] | [
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"## How to use",
"## Link\nFor more details, please visit URL"
] |
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]
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<!-- Provide the basic links for the model. -->
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## Uses
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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
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": []} | SkwarczynskiP/bert-base-uncased-finetuned-vedantgaur-human-generated | null | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2024-04-21T17:03:52+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### 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 #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## 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/n_par_bn_v_1_help_class_5_e_adp_lr_0003_S_2` 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/n_par_bn_v_1_help_class_5_e_adp_lr_0003_S_2", 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_MICRO_helpfulness_dataset"]} | BigTMiami/n_par_bn_v_1_help_class_5_e_adp_lr_0003_S_2 | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/amazon_MICRO_helpfulness_dataset",
"region:us"
] | null | 2024-04-21T17:04:07+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/amazon_MICRO_helpfulness_dataset #region-us
|
# Adapter 'BigTMiami/n_par_bn_v_1_help_class_5_e_adp_lr_0003_S_2' 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/n_par_bn_v_1_help_class_5_e_adp_lr_0003_S_2' 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/n_par_bn_v_1_help_class_5_e_adp_lr_0003_S_2' 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 | TigerBot-7B Swahili [LAPT + FOCUS]
===
## How to use
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-focus-sw"
)
tokenizer = AutoTokenizer.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-focus-sw"
)
# w/ GPU
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-focus-sw",
device_map="auto",
load_in_8bit=True,
)
```
## Citation
```
@article{yamaguchi2024empirical,
title={An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Generative {LLM} Inference},
author={Atsuki Yamaguchi and Aline Villavicencio and Nikolaos Aletras},
journal={ArXiv},
year={2024},
volume={abs/2402.10712},
url={https://arxiv.org/abs/2402.10712}
}
```
## Link
For more details, please visit https://github.com/gucci-j/llm-cva
| {"language": "sw", "license": "mit"} | atsuki-yamaguchi/tigerbot-7b-base-focus-sw | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"sw",
"arxiv:2402.10712",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T17:04:35+00:00 | [
"2402.10712"
] | [
"sw"
] | TAGS
#transformers #safetensors #llama #text-generation #sw #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| TigerBot-7B Swahili [LAPT + FOCUS]
===
## How to use
## Link
For more details, please visit URL
| [
"## How to use",
"## Link\nFor more details, please visit URL"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #sw #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How to use",
"## Link\nFor more details, please visit URL"
] |
text-generation | transformers | TigerBot-7B Arabic [LAPT + CLP]
===
## How to use
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-clp-ar"
)
tokenizer = AutoTokenizer.from_pretrained(
"aubmindlab/aragpt2-base"
)
# w/ GPU
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-clp-ar",
device_map="auto",
load_in_8bit=True,
)
```
## Citation
```
@article{yamaguchi2024empirical,
title={An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Generative {LLM} Inference},
author={Atsuki Yamaguchi and Aline Villavicencio and Nikolaos Aletras},
journal={ArXiv},
year={2024},
volume={abs/2402.10712},
url={https://arxiv.org/abs/2402.10712}
}
```
## Link
For more details, please visit https://github.com/gucci-j/llm-cva
| {"language": "ar", "license": "mit"} | atsuki-yamaguchi/tigerbot-7b-base-clp-ar | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"ar",
"arxiv:2402.10712",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T17:07:14+00:00 | [
"2402.10712"
] | [
"ar"
] | TAGS
#transformers #safetensors #llama #text-generation #ar #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| TigerBot-7B Arabic [LAPT + CLP]
===
## How to use
## Link
For more details, please visit URL
| [
"## How to use",
"## Link\nFor more details, please visit URL"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #ar #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How to use",
"## Link\nFor more details, please visit URL"
] |
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.
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
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<!-- 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]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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<!-- This section describes the evaluation protocols and provides the results. -->
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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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": "diffusers"} | Niggendar/eponymPonydiffusionv6_v40 | null | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | null | 2024-04-21T17:07:51+00:00 | [
"1910.09700"
] | [] | TAGS
#diffusers #safetensors #arxiv-1910.09700 #endpoints_compatible #diffusers-StableDiffusionXLPipeline #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a diffusers 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-StableDiffusionXLPipeline #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"
] |
token-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]
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- **Shared by [optional]:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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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
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[More Information Needed]
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[More Information Needed]
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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]
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<!-- 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]
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
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<!-- 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]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | KaggleMasterX/BERT_Episode2 | null | [
"transformers",
"safetensors",
"bert",
"token-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T17:08:37+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #bert #token-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- 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",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #bert #token-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-generation | transformers |
# Uploaded model
- **Developed by:** Ricky080811
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-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", "sft"], "base_model": "unsloth/mistral-7b-v0.2-bnb-4bit"} | Ricky080811/CompliAI_TestFullModel2 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/mistral-7b-v0.2-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T17:09:52+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #mistral #text-generation #text-generation-inference #unsloth #trl #sft #en #base_model-unsloth/mistral-7b-v0.2-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: Ricky080811
- License: apache-2.0
- Finetuned from model : unsloth/mistral-7b-v0.2-bnb-4bit
This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
| [
"# Uploaded model\n\n- Developed by: Ricky080811\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-v0.2-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #text-generation-inference #unsloth #trl #sft #en #base_model-unsloth/mistral-7b-v0.2-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Uploaded model\n\n- Developed by: Ricky080811\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-v0.2-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] |
text-generation | transformers | TigerBot-7B Swahili [LAPT + Random]
===
## How to use
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-random-sw"
)
tokenizer = AutoTokenizer.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-random-sw"
)
# w/ GPU
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-random-sw",
device_map="auto",
load_in_8bit=True,
)
```
## Citation
```
@article{yamaguchi2024empirical,
title={An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Generative {LLM} Inference},
author={Atsuki Yamaguchi and Aline Villavicencio and Nikolaos Aletras},
journal={ArXiv},
year={2024},
volume={abs/2402.10712},
url={https://arxiv.org/abs/2402.10712}
}
```
## Link
For more details, please visit https://github.com/gucci-j/llm-cva
| {"language": "sw", "license": "mit"} | atsuki-yamaguchi/tigerbot-7b-base-random-sw | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"sw",
"arxiv:2402.10712",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T17:10:01+00:00 | [
"2402.10712"
] | [
"sw"
] | TAGS
#transformers #safetensors #llama #text-generation #sw #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| TigerBot-7B Swahili [LAPT + Random]
===
## How to use
## Link
For more details, please visit URL
| [
"## How to use",
"## Link\nFor more details, please visit URL"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #sw #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How to use",
"## Link\nFor more details, please visit URL"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-small-rw
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the common_voice_11_0 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.0770
- eval_wer: 75.1837
- eval_runtime: 4720.417
- eval_samples_per_second: 3.435
- eval_steps_per_second: 0.429
- epoch: 1.0
- step: 1000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### 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"], "datasets": ["common_voice_11_0"], "base_model": "openai/whisper-small", "model-index": [{"name": "whisper-small-rw", "results": []}]} | diane20000000000000/whisper-small-rw | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_11_0",
"base_model:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T17:10:26+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_11_0 #base_model-openai/whisper-small #license-apache-2.0 #endpoints_compatible #region-us
|
# whisper-small-rw
This model is a fine-tuned version of openai/whisper-small on the common_voice_11_0 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.0770
- eval_wer: 75.1837
- eval_runtime: 4720.417
- eval_samples_per_second: 3.435
- eval_steps_per_second: 0.429
- epoch: 1.0
- step: 1000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| [
"# whisper-small-rw\n\nThis model is a fine-tuned version of openai/whisper-small on the common_voice_11_0 dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 1.0770\n- eval_wer: 75.1837\n- eval_runtime: 4720.417\n- eval_samples_per_second: 3.435\n- eval_steps_per_second: 0.429\n- epoch: 1.0\n- step: 1000",
"## 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: 16\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- training_steps: 4000\n- mixed_precision_training: Native AMP",
"### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.2.1+cu121\n- Datasets 2.19.0\n- Tokenizers 0.19.1"
] | [
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"# whisper-small-rw\n\nThis model is a fine-tuned version of openai/whisper-small on the common_voice_11_0 dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 1.0770\n- eval_wer: 75.1837\n- eval_runtime: 4720.417\n- eval_samples_per_second: 3.435\n- eval_steps_per_second: 0.429\n- epoch: 1.0\n- step: 1000",
"## 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: 16\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- training_steps: 4000\n- mixed_precision_training: Native AMP",
"### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.2.1+cu121\n- Datasets 2.19.0\n- Tokenizers 0.19.1"
] |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga aegis301 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga aegis301 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga aegis301
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
| {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFrameskip-v4", "type": "SpaceInvadersNoFrameskip-v4"}, "metrics": [{"type": "mean_reward", "value": "663.00 +/- 143.97", "name": "mean_reward", "verified": false}]}]}]} | aegis301/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2024-04-21T17:10:32+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: URL
SB3: URL
SB3 Contrib: URL
Install the RL Zoo (with SB3 and SB3-Contrib):
If you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:
## Training (with the RL Zoo)
## Hyperparameters
# Environment Arguments
| [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
"## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:",
"## Training (with the RL Zoo)",
"## Hyperparameters",
"# Environment Arguments"
] | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
"## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:",
"## Training (with the RL Zoo)",
"## Hyperparameters",
"# Environment Arguments"
] |
text-to-audio | 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": []} | procit001/female_english_voice_v1.3 | null | [
"transformers",
"safetensors",
"vits",
"text-to-audio",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T17:11:43+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #vits #text-to-audio #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #vits #text-to-audio #arxiv-1910.09700 #endpoints_compatible #region-us \n",
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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"
] |
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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### 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).
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[More Information Needed] | {"library_name": "transformers", "tags": []} | 0x0son0/xz_1 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T17:12:07+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]:
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- Language(s) (NLP):
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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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## 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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"## Training Details",
"### Training Data",
"### Training Procedure",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
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"## Model Card Contact"
] | [
"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",
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"## Bias, Risks, and Limitations",
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"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
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"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
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]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | selimyagci/bert-misogyny-english | null | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T17:12:25+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- 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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"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
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"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #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]:",
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"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
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"## Training Details",
"### Training Data",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
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"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-to-image | diffusers |
<Gallery />
## Model description
These are leonickson1/science_building_uncc LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
LoRA for the text encoder was enabled: False.
Special VAE used for training: None.
## Trigger words
You should use photo of a sks building to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](leonickson1/science_building_uncc/tree/main) them in the Files & versions tab.
| {"license": "openrail++", "tags": ["autotrain", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "photo of a sks building"} | leonickson1/science_building_uncc | null | [
"diffusers",
"autotrain",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | null | 2024-04-21T17:13:20+00:00 | [] | [] | TAGS
#diffusers #autotrain #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #region-us
|
<Gallery />
## Model description
These are leonickson1/science_building_uncc LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
LoRA for the text encoder was enabled: False.
Special VAE used for training: None.
## Trigger words
You should use photo of a sks building to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
| [
"## Model description\n\nThese are leonickson1/science_building_uncc LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: None.",
"## Trigger words\n\nYou should use photo of a sks building to trigger the image generation.",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
"TAGS\n#diffusers #autotrain #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #region-us \n",
"## Model description\n\nThese are leonickson1/science_building_uncc LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: None.",
"## Trigger words\n\nYou should use photo of a sks building to trigger the image generation.",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] |
text-generation | transformers | Mistral-7B Swahili [LAPT + FOCUS]
===
## How to use
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/Mistral-7B-v0.1-focus-sw"
)
tokenizer = AutoTokenizer.from_pretrained(
"atsuki-yamaguchi/Mistral-7B-v0.1-focus-sw"
)
# w/ GPU
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/Mistral-7B-v0.1-focus-sw",
device_map="auto",
load_in_8bit=True,
)
```
## Citation
```
@article{yamaguchi2024empirical,
title={An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Generative {LLM} Inference},
author={Atsuki Yamaguchi and Aline Villavicencio and Nikolaos Aletras},
journal={ArXiv},
year={2024},
volume={abs/2402.10712},
url={https://arxiv.org/abs/2402.10712}
}
```
## Link
For more details, please visit https://github.com/gucci-j/llm-cva
| {"language": "sw", "license": "mit"} | atsuki-yamaguchi/Mistral-7B-v0.1-focus-sw | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"sw",
"arxiv:2402.10712",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T17:13:57+00:00 | [
"2402.10712"
] | [
"sw"
] | TAGS
#transformers #safetensors #mistral #text-generation #sw #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Mistral-7B Swahili [LAPT + FOCUS]
===
## How to use
## Link
For more details, please visit URL
| [
"## How to use",
"## Link\nFor more details, please visit URL"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #sw #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How to use",
"## Link\nFor more details, please visit URL"
] |
text-generation | transformers | TigerBot-7B Swahili [LAPT + CLP]
===
## How to use
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-clp-sw"
)
tokenizer = AutoTokenizer.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-clp-sw"
)
# w/ GPU
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-clp-sw",
device_map="auto",
load_in_8bit=True,
)
```
## Citation
```
@article{yamaguchi2024empirical,
title={An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Generative {LLM} Inference},
author={Atsuki Yamaguchi and Aline Villavicencio and Nikolaos Aletras},
journal={ArXiv},
year={2024},
volume={abs/2402.10712},
url={https://arxiv.org/abs/2402.10712}
}
```
## Link
For more details, please visit https://github.com/gucci-j/llm-cva
| {"language": "sw", "license": "mit"} | atsuki-yamaguchi/tigerbot-7b-base-clp-sw | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"sw",
"arxiv:2402.10712",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T17:15:30+00:00 | [
"2402.10712"
] | [
"sw"
] | TAGS
#transformers #safetensors #llama #text-generation #sw #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| TigerBot-7B Swahili [LAPT + CLP]
===
## How to use
## Link
For more details, please visit URL
| [
"## How to use",
"## Link\nFor more details, please visit URL"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #sw #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How to use",
"## Link\nFor more details, please visit URL"
] |
text-to-image | diffusers |
# AutoTrain SDXL LoRA DreamBooth - leonickson1/hauser_alumni_pavilion_uncc
<Gallery />
## Model description
These are leonickson1/hauser_alumni_pavilion_uncc LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: None.
## Trigger words
You should use photo of a sks building to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](leonickson1/hauser_alumni_pavilion_uncc/tree/main) them in the Files & versions tab.
| {"license": "openrail++", "tags": ["autotrain", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "photo of a sks building"} | leonickson1/hauser_alumni_pavilion_uncc | null | [
"diffusers",
"autotrain",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | null | 2024-04-21T17:16:14+00:00 | [] | [] | TAGS
#diffusers #autotrain #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #region-us
|
# AutoTrain SDXL LoRA DreamBooth - leonickson1/hauser_alumni_pavilion_uncc
<Gallery />
## Model description
These are leonickson1/hauser_alumni_pavilion_uncc LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using DreamBooth.
LoRA for the text encoder was enabled: False.
Special VAE used for training: None.
## Trigger words
You should use photo of a sks building to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
| [
"# AutoTrain SDXL LoRA DreamBooth - leonickson1/hauser_alumni_pavilion_uncc\n\n<Gallery />",
"## Model description\n\nThese are leonickson1/hauser_alumni_pavilion_uncc LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: None.",
"## Trigger words\n\nYou should use photo of a sks building to trigger the image generation.",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
"TAGS\n#diffusers #autotrain #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #region-us \n",
"# AutoTrain SDXL LoRA DreamBooth - leonickson1/hauser_alumni_pavilion_uncc\n\n<Gallery />",
"## Model description\n\nThese are leonickson1/hauser_alumni_pavilion_uncc LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: None.",
"## Trigger words\n\nYou should use photo of a sks building to trigger the image generation.",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] |
null | adapter-transformers |
# Adapter `BigTMiami/n_par_bn_v_1_help_class_20_e_adp_lr_0003_S_2` 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/n_par_bn_v_1_help_class_20_e_adp_lr_0003_S_2", 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_MICRO_helpfulness_dataset"]} | BigTMiami/n_par_bn_v_1_help_class_20_e_adp_lr_0003_S_2 | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/amazon_MICRO_helpfulness_dataset",
"region:us"
] | null | 2024-04-21T17:16:44+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/amazon_MICRO_helpfulness_dataset #region-us
|
# Adapter 'BigTMiami/n_par_bn_v_1_help_class_20_e_adp_lr_0003_S_2' 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/n_par_bn_v_1_help_class_20_e_adp_lr_0003_S_2' 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/n_par_bn_v_1_help_class_20_e_adp_lr_0003_S_2' 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"
] |
text2text-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": []} | sataayu/molt5-augmented-default-600-small-smiles2caption | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T17:18:16+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #t5 #text2text-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 #t5 #text2text-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 | Mistral-7B Swahili [LAPT + Random]
===
## How to use
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/Mistral-7B-v0.1-random-sw"
)
tokenizer = AutoTokenizer.from_pretrained(
"atsuki-yamaguchi/Mistral-7B-v0.1-random-sw"
)
# w/ GPU
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/Mistral-7B-v0.1-random-sw",
device_map="auto",
load_in_8bit=True,
)
```
## Citation
```
@article{yamaguchi2024empirical,
title={An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Generative {LLM} Inference},
author={Atsuki Yamaguchi and Aline Villavicencio and Nikolaos Aletras},
journal={ArXiv},
year={2024},
volume={abs/2402.10712},
url={https://arxiv.org/abs/2402.10712}
}
```
## Link
For more details, please visit https://github.com/gucci-j/llm-cva
| {"language": "sw", "license": "mit"} | atsuki-yamaguchi/Mistral-7B-v0.1-random-sw | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"sw",
"arxiv:2402.10712",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T17:19:50+00:00 | [
"2402.10712"
] | [
"sw"
] | TAGS
#transformers #safetensors #mistral #text-generation #sw #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Mistral-7B Swahili [LAPT + Random]
===
## How to use
## Link
For more details, please visit URL
| [
"## How to use",
"## Link\nFor more details, please visit URL"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #sw #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How to use",
"## Link\nFor more details, please visit URL"
] |
text-generation | transformers | TigerBot-7B German [LAPT + CLP+ (Untied)]
===
## How to use
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-clpp-untied-de"
)
tokenizer = AutoTokenizer.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-clpp-untied-de"
)
# w/ GPU
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-clpp-untied-de",
device_map="auto",
load_in_8bit=True,
)
```
## Citation
```
@article{yamaguchi2024empirical,
title={An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Generative {LLM} Inference},
author={Atsuki Yamaguchi and Aline Villavicencio and Nikolaos Aletras},
journal={ArXiv},
year={2024},
volume={abs/2402.10712},
url={https://arxiv.org/abs/2402.10712}
}
```
## Link
For more details, please visit https://github.com/gucci-j/llm-cva
| {"language": "de", "license": "mit"} | atsuki-yamaguchi/tigerbot-7b-base-clpp-untied-de | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"de",
"arxiv:2402.10712",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T17:20:39+00:00 | [
"2402.10712"
] | [
"de"
] | TAGS
#transformers #safetensors #llama #text-generation #de #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| TigerBot-7B German [LAPT + CLP+ (Untied)]
===
## How to use
## Link
For more details, please visit URL
| [
"## How to use",
"## Link\nFor more details, please visit URL"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #de #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How to use",
"## Link\nFor more details, please visit URL"
] |
token-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
<!-- 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": []} | KaggleMasterX/BERT_Episode3 | null | [
"transformers",
"safetensors",
"bert",
"token-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T17:21:15+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #bert #token-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- 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 #bert #token-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
null | adapter-transformers |
# Adapter `BigTMiami/n_par_bn_v_1_help_class_5_e_adp_lr_0003_S_3` 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/n_par_bn_v_1_help_class_5_e_adp_lr_0003_S_3", 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_MICRO_helpfulness_dataset"]} | BigTMiami/n_par_bn_v_1_help_class_5_e_adp_lr_0003_S_3 | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/amazon_MICRO_helpfulness_dataset",
"region:us"
] | null | 2024-04-21T17:22:25+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/amazon_MICRO_helpfulness_dataset #region-us
|
# Adapter 'BigTMiami/n_par_bn_v_1_help_class_5_e_adp_lr_0003_S_3' 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/n_par_bn_v_1_help_class_5_e_adp_lr_0003_S_3' 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/n_par_bn_v_1_help_class_5_e_adp_lr_0003_S_3' 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"
] |
null | mlx |
# mlx-community/dolphin-2.9-llama3-8b-unquantized
This model was converted to MLX format from [`cognitivecomputations/dolphin-2.9-llama3-8b`]() using mlx-lm version **0.10.0**.
Refer to the [original model card](https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/dolphin-2.9-llama3-8b-unquantized")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
| {"license": "other", "tags": ["generated_from_trainer", "mlx"], "datasets": ["cognitivecomputations/Dolphin-2.9", "teknium/OpenHermes-2.5", "m-a-p/CodeFeedback-Filtered-Instruction", "cognitivecomputations/dolphin-coder", "cognitivecomputations/samantha-data", "HuggingFaceH4/ultrachat_200k", "microsoft/orca-math-word-problems-200k", "abacusai/SystemChat-1.1", "Locutusque/function-calling-chatml", "internlm/Agent-FLAN"], "base_model": "meta-llama/Meta-Llama-3-8B", "model-index": [{"name": "out", "results": []}]} | mlx-community/dolphin-2.9-llama3-8b-unquantized | null | [
"mlx",
"safetensors",
"llama",
"generated_from_trainer",
"dataset:cognitivecomputations/Dolphin-2.9",
"dataset:teknium/OpenHermes-2.5",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"dataset:cognitivecomputations/dolphin-coder",
"dataset:cognitivecomputations/samantha-data",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:abacusai/SystemChat-1.1",
"dataset:Locutusque/function-calling-chatml",
"dataset:internlm/Agent-FLAN",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:other",
"region:us"
] | null | 2024-04-21T17:24:07+00:00 | [] | [] | TAGS
#mlx #safetensors #llama #generated_from_trainer #dataset-cognitivecomputations/Dolphin-2.9 #dataset-teknium/OpenHermes-2.5 #dataset-m-a-p/CodeFeedback-Filtered-Instruction #dataset-cognitivecomputations/dolphin-coder #dataset-cognitivecomputations/samantha-data #dataset-HuggingFaceH4/ultrachat_200k #dataset-microsoft/orca-math-word-problems-200k #dataset-abacusai/SystemChat-1.1 #dataset-Locutusque/function-calling-chatml #dataset-internlm/Agent-FLAN #base_model-meta-llama/Meta-Llama-3-8B #license-other #region-us
|
# mlx-community/dolphin-2.9-llama3-8b-unquantized
This model was converted to MLX format from ['cognitivecomputations/dolphin-2.9-llama3-8b']() using mlx-lm version 0.10.0.
Refer to the original model card for more details on the model.
## Use with mlx
| [
"# mlx-community/dolphin-2.9-llama3-8b-unquantized\nThis model was converted to MLX format from ['cognitivecomputations/dolphin-2.9-llama3-8b']() using mlx-lm version 0.10.0.\nRefer to the original model card for more details on the model.",
"## Use with mlx"
] | [
"TAGS\n#mlx #safetensors #llama #generated_from_trainer #dataset-cognitivecomputations/Dolphin-2.9 #dataset-teknium/OpenHermes-2.5 #dataset-m-a-p/CodeFeedback-Filtered-Instruction #dataset-cognitivecomputations/dolphin-coder #dataset-cognitivecomputations/samantha-data #dataset-HuggingFaceH4/ultrachat_200k #dataset-microsoft/orca-math-word-problems-200k #dataset-abacusai/SystemChat-1.1 #dataset-Locutusque/function-calling-chatml #dataset-internlm/Agent-FLAN #base_model-meta-llama/Meta-Llama-3-8B #license-other #region-us \n",
"# mlx-community/dolphin-2.9-llama3-8b-unquantized\nThis model was converted to MLX format from ['cognitivecomputations/dolphin-2.9-llama3-8b']() using mlx-lm version 0.10.0.\nRefer to the original model card for more details on the model.",
"## Use with mlx"
] |
text-generation | transformers | Mistral-7B Arabic [LAPT + FOCUS]
===
## How to use
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/Mistral-7B-v0.1-focus-ar"
)
tokenizer = AutoTokenizer.from_pretrained(
"aubmindlab/aragpt2-base"
)
# w/ GPU
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/Mistral-7B-v0.1-focus-ar",
device_map="auto",
load_in_8bit=True,
)
```
## Citation
```
@article{yamaguchi2024empirical,
title={An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Generative {LLM} Inference},
author={Atsuki Yamaguchi and Aline Villavicencio and Nikolaos Aletras},
journal={ArXiv},
year={2024},
volume={abs/2402.10712},
url={https://arxiv.org/abs/2402.10712}
}
```
## Link
For more details, please visit https://github.com/gucci-j/llm-cva
| {"language": "ar", "license": "mit"} | atsuki-yamaguchi/Mistral-7B-v0.1-focus-ar | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"ar",
"arxiv:2402.10712",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T17:24:36+00:00 | [
"2402.10712"
] | [
"ar"
] | TAGS
#transformers #safetensors #mistral #text-generation #ar #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Mistral-7B Arabic [LAPT + FOCUS]
===
## How to use
## Link
For more details, please visit URL
| [
"## How to use",
"## Link\nFor more details, please visit URL"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #ar #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How to use",
"## Link\nFor more details, please visit URL"
] |
text-generation | transformers |
## Exllama v2 Quantizations of OrpoLlama-3-8B
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.19">turboderp's ExLlamaV2 v0.0.19</a> for quantization.
<b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b>
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Original model: https://huggingface.co/mlabonne/OrpoLlama-3-8B
## Prompt format
```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Available sizes
| Branch | Bits | lm_head bits | VRAM (4k) | VRAM (8K) | VRAM (16k) | VRAM (32k) | Description |
| ----- | ---- | ------- | ------ | ------ | ------ | ------ | ------------ |
| [8_0](https://huggingface.co/bartowski/OrpoLlama-3-8B-exl2/tree/8_0) | 8.0 | 8.0 | 10.1 GB | 10.5 GB | 11.5 GB | 13.6 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
| [6_5](https://huggingface.co/bartowski/OrpoLlama-3-8B-exl2/tree/6_5) | 6.5 | 8.0 | 8.9 GB | 9.3 GB | 10.3 GB | 12.4 GB | Very similar to 8.0, good tradeoff of size vs performance, **recommended**. |
| [5_0](https://huggingface.co/bartowski/OrpoLlama-3-8B-exl2/tree/5_0) | 5.0 | 6.0 | 7.7 GB | 8.1 GB | 9.1 GB | 11.2 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. |
| [4_25](https://huggingface.co/bartowski/OrpoLlama-3-8B-exl2/tree/4_25) | 4.25 | 6.0 | 7.0 GB | 7.4 GB | 8.4 GB | 10.5 GB | GPTQ equivalent bits per weight, slightly higher quality. |
| [3_5](https://huggingface.co/bartowski/OrpoLlama-3-8B-exl2/tree/3_5) | 3.5 | 6.0 | 6.4 GB | 6.8 GB | 7.8 GB | 9.9 GB | Lower quality, only use if you have to. |
## Download instructions
With git:
```shell
git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/OrpoLlama-3-8B-exl2 OrpoLlama-3-8B-exl2-6_5
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download a specific branch, use the `--revision` parameter. For example, to download the 6.5 bpw branch:
Linux:
```shell
huggingface-cli download bartowski/OrpoLlama-3-8B-exl2 --revision 6_5 --local-dir OrpoLlama-3-8B-exl2-6_5 --local-dir-use-symlinks False
```
Windows (which apparently doesn't like _ in folders sometimes?):
```shell
huggingface-cli download bartowski/OrpoLlama-3-8B-exl2 --revision 6_5 --local-dir OrpoLlama-3-8B-exl2-6.5 --local-dir-use-symlinks False
```
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
| {"language": ["en"], "license": "other", "library_name": "transformers", "tags": ["orpo", "llama 3", "rlhf", "sft"], "datasets": ["mlabonne/orpo-dpo-mix-40k"], "quantized_by": "bartowski", "pipeline_tag": "text-generation"} | bartowski/OrpoLlama-3-8B-exl2 | null | [
"transformers",
"orpo",
"llama 3",
"rlhf",
"sft",
"text-generation",
"en",
"dataset:mlabonne/orpo-dpo-mix-40k",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T17:24:43+00:00 | [] | [
"en"
] | TAGS
#transformers #orpo #llama 3 #rlhf #sft #text-generation #en #dataset-mlabonne/orpo-dpo-mix-40k #license-other #endpoints_compatible #region-us
| Exllama v2 Quantizations of OrpoLlama-3-8B
------------------------------------------
Using <a href="URL ExLlamaV2 v0.0.19 for quantization.
**The "main" branch only contains the URL, download one of the other branches for the model (see below)**
Each branch contains an individual bits per weight, with the main one containing only the URL for further conversions.
Original model: URL
Prompt format
-------------
Available sizes
---------------
Download instructions
---------------------
With git:
With huggingface hub (credit to TheBloke for instructions):
To download a specific branch, use the '--revision' parameter. For example, to download the 6.5 bpw branch:
Linux:
Windows (which apparently doesn't like \_ in folders sometimes?):
Want to support my work? Visit my ko-fi page here: URL
| [] | [
"TAGS\n#transformers #orpo #llama 3 #rlhf #sft #text-generation #en #dataset-mlabonne/orpo-dpo-mix-40k #license-other #endpoints_compatible #region-us \n"
] |
text-to-image | diffusers |
# Model Card for Model ID
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This is the model card of a 🧨 diffusers 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": "diffusers"} | Sofoklis/rna_bp_nl | null | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | null | 2024-04-21T17:25:03+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:
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- Shared by [optional]:
- Model type:
- Language(s) (NLP):
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### Model Sources [optional]
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## Uses
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
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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]
### 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]",
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"### 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"
] |
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. -->
# distilbert-mouse-enhancers
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6932
- Accuracy: 0.5
## 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-06
- 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| No log | 1.0 | 242 | 0.6932 | 0.5 |
| No log | 2.0 | 484 | 0.6949 | 0.5 |
| 0.693 | 3.0 | 726 | 0.6931 | 0.5 |
| 0.693 | 4.0 | 968 | 0.6931 | 0.5 |
| 0.694 | 5.0 | 1210 | 0.6932 | 0.5 |
| 0.694 | 6.0 | 1452 | 0.6935 | 0.5 |
| 0.6954 | 7.0 | 1694 | 0.6933 | 0.5 |
| 0.6954 | 8.0 | 1936 | 0.6932 | 0.5 |
| 0.6937 | 9.0 | 2178 | 0.6932 | 0.5 |
| 0.6937 | 10.0 | 2420 | 0.6932 | 0.5 |
| 0.6935 | 11.0 | 2662 | 0.6932 | 0.5 |
| 0.6935 | 12.0 | 2904 | 0.6934 | 0.5 |
| 0.6955 | 13.0 | 3146 | 0.6932 | 0.5 |
| 0.6955 | 14.0 | 3388 | 0.6931 | 0.5 |
| 0.6941 | 15.0 | 3630 | 0.6931 | 0.5 |
| 0.6941 | 16.0 | 3872 | 0.6932 | 0.5 |
| 0.6953 | 17.0 | 4114 | 0.6932 | 0.5 |
| 0.6953 | 18.0 | 4356 | 0.6931 | 0.5 |
| 0.6932 | 19.0 | 4598 | 0.6932 | 0.5 |
| 0.6932 | 20.0 | 4840 | 0.6931 | 0.5 |
| 0.6945 | 21.0 | 5082 | 0.6933 | 0.5 |
| 0.6945 | 22.0 | 5324 | 0.6932 | 0.5 |
| 0.6939 | 23.0 | 5566 | 0.6931 | 0.5 |
| 0.6939 | 24.0 | 5808 | 0.6931 | 0.5 |
| 0.6951 | 25.0 | 6050 | 0.6932 | 0.5 |
| 0.6951 | 26.0 | 6292 | 0.6931 | 0.5 |
| 0.6943 | 27.0 | 6534 | 0.6932 | 0.5 |
| 0.6943 | 28.0 | 6776 | 0.6931 | 0.5 |
| 0.6944 | 29.0 | 7018 | 0.6931 | 0.5 |
| 0.6944 | 30.0 | 7260 | 0.6932 | 0.5 |
| 0.6955 | 31.0 | 7502 | 0.6931 | 0.5 |
| 0.6955 | 32.0 | 7744 | 0.6933 | 0.5 |
| 0.6955 | 33.0 | 7986 | 0.6932 | 0.5 |
| 0.694 | 34.0 | 8228 | 0.6931 | 0.5 |
| 0.694 | 35.0 | 8470 | 0.6932 | 0.5 |
| 0.6937 | 36.0 | 8712 | 0.6932 | 0.5 |
| 0.6937 | 37.0 | 8954 | 0.6931 | 0.5 |
| 0.6923 | 38.0 | 9196 | 0.6932 | 0.5 |
| 0.6923 | 39.0 | 9438 | 0.6932 | 0.5 |
| 0.6931 | 40.0 | 9680 | 0.6931 | 0.5 |
| 0.6931 | 41.0 | 9922 | 0.6932 | 0.5 |
| 0.6937 | 42.0 | 10164 | 0.6932 | 0.5 |
| 0.6937 | 43.0 | 10406 | 0.6932 | 0.5 |
| 0.6936 | 44.0 | 10648 | 0.6932 | 0.5 |
| 0.6936 | 45.0 | 10890 | 0.6932 | 0.5 |
| 0.6933 | 46.0 | 11132 | 0.6932 | 0.5 |
| 0.6933 | 47.0 | 11374 | 0.6932 | 0.5 |
| 0.6924 | 48.0 | 11616 | 0.6932 | 0.5 |
| 0.6924 | 49.0 | 11858 | 0.6932 | 0.5 |
| 0.6929 | 50.0 | 12100 | 0.6932 | 0.5 |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.0+cu117
- Datasets 2.19.0
- Tokenizers 0.13.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-mouse-enhancers", "results": []}]} | addykan/distilbert-mouse-enhancers | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T17:25:38+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-mouse-enhancers
==========================
This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6932
* Accuracy: 0.5
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-06
* 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: 50
### Training results
### Framework versions
* Transformers 4.26.1
* Pytorch 2.0.0+cu117
* Datasets 2.19.0
* Tokenizers 0.13.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-06\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50",
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.26.1\n* Pytorch 2.0.0+cu117\n* Datasets 2.19.0\n* Tokenizers 0.13.3"
] |
text-generation | transformers | TigerBot-7B Arabic [LAPT + CLP+ (Untied)]
===
## How to use
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-clpp-untied-ar"
)
tokenizer = AutoTokenizer.from_pretrained(
"aubmindlab/aragpt2-base"
)
# w/ GPU
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-clpp-untied-ar",
device_map="auto",
load_in_8bit=True,
)
```
## Citation
```
@article{yamaguchi2024empirical,
title={An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Generative {LLM} Inference},
author={Atsuki Yamaguchi and Aline Villavicencio and Nikolaos Aletras},
journal={ArXiv},
year={2024},
volume={abs/2402.10712},
url={https://arxiv.org/abs/2402.10712}
}
```
## Link
For more details, please visit https://github.com/gucci-j/llm-cva
| {"language": "ar", "license": "mit"} | atsuki-yamaguchi/tigerbot-7b-base-clpp-untied-ar | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"ar",
"arxiv:2402.10712",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T17:25:43+00:00 | [
"2402.10712"
] | [
"ar"
] | TAGS
#transformers #safetensors #llama #text-generation #ar #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| TigerBot-7B Arabic [LAPT + CLP+ (Untied)]
===
## How to use
## Link
For more details, please visit URL
| [
"## How to use",
"## Link\nFor more details, please visit URL"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #ar #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How to use",
"## Link\nFor more details, please visit URL"
] |
text-generation | transformers | Mistral-7B Swahili [LAPT + CLP]
===
## How to use
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/Mistral-7B-v0.1-clp-sw"
)
tokenizer = AutoTokenizer.from_pretrained(
"atsuki-yamaguchi/Mistral-7B-v0.1-clp-sw"
)
# w/ GPU
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/Mistral-7B-v0.1-clp-sw",
device_map="auto",
load_in_8bit=True,
)
```
## Citation
```
@article{yamaguchi2024empirical,
title={An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Generative {LLM} Inference},
author={Atsuki Yamaguchi and Aline Villavicencio and Nikolaos Aletras},
journal={ArXiv},
year={2024},
volume={abs/2402.10712},
url={https://arxiv.org/abs/2402.10712}
}
```
## Link
For more details, please visit https://github.com/gucci-j/llm-cva
| {"language": "sw", "license": "mit"} | atsuki-yamaguchi/Mistral-7B-v0.1-clp-sw | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"sw",
"arxiv:2402.10712",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T17:25:51+00:00 | [
"2402.10712"
] | [
"sw"
] | TAGS
#transformers #safetensors #mistral #text-generation #sw #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Mistral-7B Swahili [LAPT + CLP]
===
## How to use
## Link
For more details, please visit URL
| [
"## How to use",
"## Link\nFor more details, please visit URL"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #sw #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How to use",
"## Link\nFor more details, please visit URL"
] |
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
<!-- 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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[More Information Needed] | {"library_name": "transformers", "tags": []} | AlGatone21/SwissFinBERT_v1 | null | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T17:26:27+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #distilbert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"## Training Details",
"### Training Data",
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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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"## 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"
] |
text-generation | transformers | TigerBot-7B German [LAPT + Heuristics (Untied)]
===
## How to use
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-heuristics-untied-de"
)
tokenizer = AutoTokenizer.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-heuristics-untied-de"
)
# w/ GPU
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-heuristics-untied-de",
device_map="auto",
load_in_8bit=True,
)
```
## Citation
```
@article{yamaguchi2024empirical,
title={An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Generative {LLM} Inference},
author={Atsuki Yamaguchi and Aline Villavicencio and Nikolaos Aletras},
journal={ArXiv},
year={2024},
volume={abs/2402.10712},
url={https://arxiv.org/abs/2402.10712}
}
```
## Link
For more details, please visit https://github.com/gucci-j/llm-cva
| {"language": "de", "license": "mit"} | atsuki-yamaguchi/tigerbot-7b-base-heuristics-untied-de | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"de",
"arxiv:2402.10712",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T17:26:38+00:00 | [
"2402.10712"
] | [
"de"
] | TAGS
#transformers #safetensors #llama #text-generation #de #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| TigerBot-7B German [LAPT + Heuristics (Untied)]
===
## How to use
## Link
For more details, please visit URL
| [
"## How to use",
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] | [
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"## How to use",
"## Link\nFor more details, please visit URL"
] |
null | transformers |
# Uploaded model
- **Developed by:** feliphe-galiza
- **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"} | feliphe-galiza/llama-3-italian-hypernyms | 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 | 2024-04-21T17:26:47+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: feliphe-galiza
- 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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] | [
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] |
text-generation | transformers | AI Model Name: Llama 3 8B "Built with Meta Llama 3" https://llama.meta.com/llama3/license/
Baseline evaluation results:
```
hf (pretrained=meta-llama/Meta-Llama-3-8B-Instruct), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 16
| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr|
|-------------|------:|------|-----:|--------|-----:|---|-----:|
|winogrande | 1|none | 0|acc |0.7198|± |0.0126|
|piqa | 1|none | 0|acc |0.7873|± |0.0095|
| | |none | 0|acc_norm|0.7867|± |0.0096|
|hellaswag | 1|none | 0|acc |0.5767|± |0.0049|
| | |none | 0|acc_norm|0.7585|± |0.0043|
|arc_easy | 1|none | 0|acc |0.8140|± |0.0080|
| | |none | 0|acc_norm|0.7971|± |0.0083|
|arc_challenge| 1|none | 0|acc |0.5290|± |0.0146|
| | |none | 0|acc_norm|0.5674|± |0.0145|
```
This repo evaluation results (AQLM with no global fine-tuning):
```
hf (pretrained=catid/cat-llama-3-8b-instruct-aqlm-noft), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 16
| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr|
|-------------|------:|------|-----:|--------|-----:|---|-----:|
|winogrande | 1|none | 0|acc |0.7119|± |0.0127|
|piqa | 1|none | 0|acc |0.7807|± |0.0097|
| | |none | 0|acc_norm|0.7824|± |0.0096|
|hellaswag | 1|none | 0|acc |0.5716|± |0.0049|
| | |none | 0|acc_norm|0.7539|± |0.0043|
|arc_easy | 1|none | 0|acc |0.8152|± |0.0080|
| | |none | 0|acc_norm|0.7866|± |0.0084|
|arc_challenge| 1|none | 0|acc |0.5043|± |0.0146|
| | |none | 0|acc_norm|0.5555|± |0.0145|
```
To reproduce evaluation results:
```bash
git clone https://github.com/EleutherAI/lm-evaluation-harness
cd lm-evaluation-harness
conda create -n lmeval python=3.10 -y && conda activate lmeval
pip install -e .
pip install accelerate aqlm"[gpu,cpu]"
accelerate launch lm_eval --model hf \
--model_args pretrained=catid/cat-llama-3-8b-instruct-aqlm-noft \
--tasks winogrande,piqa,hellaswag,arc_easy,arc_challenge \
--batch_size 16
```
You can run this model as a `transformers` model using https://github.com/oobabooga/text-generation-webui
| {} | catid/cat-llama-3-8b-instruct-aqlm-noft | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T17:29:49+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| AI Model Name: Llama 3 8B "Built with Meta Llama 3" URL
Baseline evaluation results:
This repo evaluation results (AQLM with no global fine-tuning):
To reproduce evaluation results:
You can run this model as a 'transformers' model using URL
| [] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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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#### Summary
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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]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed] | {"library_name": "transformers", "tags": []} | akankshya107/my-finetuned-bert | null | [
"transformers",
"safetensors",
"bert",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T17:30:38+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #bert #feature-extraction #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]
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## Evaluation
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#### Testing Data
#### Factors
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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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[optional]
BibTeX:
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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-generation | transformers | Mistral-7B Arabic [LAPT + Random]
===
## How to use
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/Mistral-7B-v0.1-random-ar"
)
tokenizer = AutoTokenizer.from_pretrained(
"aubmindlab/aragpt2-base"
)
# w/ GPU
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/Mistral-7B-v0.1-random-ar",
device_map="auto",
load_in_8bit=True,
)
```
## Citation
```
@article{yamaguchi2024empirical,
title={An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Generative {LLM} Inference},
author={Atsuki Yamaguchi and Aline Villavicencio and Nikolaos Aletras},
journal={ArXiv},
year={2024},
volume={abs/2402.10712},
url={https://arxiv.org/abs/2402.10712}
}
```
## Link
For more details, please visit https://github.com/gucci-j/llm-cva
| {"language": "ar", "license": "mit"} | atsuki-yamaguchi/Mistral-7B-v0.1-random-ar | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"ar",
"arxiv:2402.10712",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T17:31:26+00:00 | [
"2402.10712"
] | [
"ar"
] | TAGS
#transformers #safetensors #mistral #text-generation #ar #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Mistral-7B Arabic [LAPT + Random]
===
## How to use
## Link
For more details, please visit URL
| [
"## How to use",
"## Link\nFor more details, please visit URL"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #ar #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How to use",
"## Link\nFor more details, please visit URL"
] |
text-generation | transformers | TigerBot-7B Arabic [LAPT + Heuristics (Untied)]
===
## How to use
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-heuristics-untied-ar"
)
tokenizer = AutoTokenizer.from_pretrained(
"aubmindlab/aragpt2-base"
)
# w/ GPU
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-heuristics-untied-ar",
device_map="auto",
load_in_8bit=True,
)
```
## Citation
```
@article{yamaguchi2024empirical,
title={An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Generative {LLM} Inference},
author={Atsuki Yamaguchi and Aline Villavicencio and Nikolaos Aletras},
journal={ArXiv},
year={2024},
volume={abs/2402.10712},
url={https://arxiv.org/abs/2402.10712}
}
```
## Link
For more details, please visit https://github.com/gucci-j/llm-cva
| {"language": "ar", "license": "mit"} | atsuki-yamaguchi/tigerbot-7b-base-heuristics-untied-ar | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"ar",
"arxiv:2402.10712",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T17:31:41+00:00 | [
"2402.10712"
] | [
"ar"
] | TAGS
#transformers #safetensors #llama #text-generation #ar #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| TigerBot-7B Arabic [LAPT + Heuristics (Untied)]
===
## How to use
## Link
For more details, please visit URL
| [
"## How to use",
"## Link\nFor more details, please visit URL"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #ar #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How to use",
"## Link\nFor more details, please visit URL"
] |
text-generation | transformers | TigerBot-7B Japanese [LAPT + CLP+ (Untied)]
===
## How to use
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-clpp-untied-ja"
)
tokenizer = AutoTokenizer.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-clpp-untied-ja"
)
# w/ GPU
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-clpp-untied-ja",
device_map="auto",
load_in_8bit=True,
)
```
## Citation
```
@article{yamaguchi2024empirical,
title={An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Generative {LLM} Inference},
author={Atsuki Yamaguchi and Aline Villavicencio and Nikolaos Aletras},
journal={ArXiv},
year={2024},
volume={abs/2402.10712},
url={https://arxiv.org/abs/2402.10712}
}
```
## Link
For more details, please visit https://github.com/gucci-j/llm-cva
| {"language": "ja", "license": "mit"} | atsuki-yamaguchi/tigerbot-7b-base-clpp-untied-ja | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"ja",
"arxiv:2402.10712",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T17:32:35+00:00 | [
"2402.10712"
] | [
"ja"
] | TAGS
#transformers #safetensors #llama #text-generation #ja #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| TigerBot-7B Japanese [LAPT + CLP+ (Untied)]
===
## How to use
## Link
For more details, please visit URL
| [
"## How to use",
"## Link\nFor more details, please visit URL"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #ja #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How to use",
"## Link\nFor more details, please visit URL"
] |
text-generation | transformers |
# Uploaded model
- **Developed by:** reallad
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-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-Instruct-bnb-4bit"} | reallad/blopsy-1.4 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T17:32:51+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #llama #text-generation #text-generation-inference #unsloth #trl #sft #conversational #en #base_model-unsloth/llama-3-8b-Instruct-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: reallad
- License: apache-2.0
- Finetuned from model : unsloth/llama-3-8b-Instruct-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: reallad\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-Instruct-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 #pytorch #llama #text-generation #text-generation-inference #unsloth #trl #sft #conversational #en #base_model-unsloth/llama-3-8b-Instruct-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Uploaded model\n\n- Developed by: reallad\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-Instruct-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-classification | setfit |
# SetFit with sentence-transformers/paraphrase-MiniLM-L3-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-MiniLM-L3-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L3-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/paraphrase-MiniLM-L3-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L3-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 128 tokens
- **Number of Classes:** 47 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 28 | <ul><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Header CHWR Temp'</li><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Header CHWR Temperature'</li><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Header CHWR Temperature'</li></ul> |
| 6 | <ul><li>'Tiong Bahru Plaza, DDC-L1-3, AHU-L2-02 modulating valve feedback'</li><li>'Tiong Bahru Plaza, DDC-L2-2, AHU-L2-05 modulating valve feedback'</li><li>'Tiong Bahru Plaza, DDC-L2-5, PAU-L2-04 modulating valve feedback'</li></ul> |
| 15 | <ul><li>'Tiong Bahru Plaza, VAV 19-6, Discharge Air Flow (Units: m3/h)'</li><li>'Tiong Bahru Plaza, VAV 18-17, Discharge Air Flow (Units: m3/h)'</li><li>'Tiong Bahru Plaza, VAV 19-10, Discharge Air Flow (Units: m3/h)'</li></ul> |
| 43 | <ul><li>'Tiong Bahru Plaza, UC800_101001_CH_1, Chilled Water Setpoint (Units: ¬?C)'</li><li>'Tiong Bahru Plaza, UC800_101001_Chiller_1, Chilled Water Setpoint (Units: ¬?C)'</li><li>'Tiong Bahru Plaza, UC800_101001_Chiller_1, Chilled Water Setpoint (Units: ¬?C)'</li></ul> |
| 4 | <ul><li>'Tiong Bahru Plaza, DDC L12, AHU 10-1 FLOW'</li><li>'Tiong Bahru Plaza, DDC L14-1, AHU 12-1 Flow (Units: Pa)'</li><li>'Tiong Bahru Plaza, DDC-L6, AHU 4-1 Flow'</li></ul> |
| 0 | <ul><li>'Tiong Bahru Plaza, DDC-L20, Co2 Level 18'</li><li>'Tiong Bahru Plaza, DDC L14-1, AHU 15-1 CO2 Reading (Units: ppm).1'</li><li>'Tiong Bahru Plaza, DDC-L6, AHU 4-1 CO2.1'</li></ul> |
| 10 | <ul><li>'Tiong Bahru Plaza, DDC-9-1, AHU7-1 Start/Stop Control'</li><li>'Tiong Bahru Plaza, DDC L14-1, AHU13-1 Start/Stop'</li><li>'Tiong Bahru Plaza, DDC-L1-4, PAU-L1-05 Start/Stop Control'</li></ul> |
| 40 | <ul><li>'Tiong Bahru Plaza, VAV 19-21, Air Valve Position (Units: %)'</li><li>'Tiong Bahru Plaza, VAV 18-17, Air Valve Position (Units: %)'</li><li>'Tiong Bahru Plaza, VAV 19-12, Air Valve Position (Units: %)'</li></ul> |
| 26 | <ul><li>'Tiong Bahru Plaza, MB-1-S3, Active Power kW'</li><li>'Tiong Bahru Plaza, MB-1-S2, Active Power kW'</li><li>'Tiong Bahru Plaza, LCP-AC-ACCH-01, Active Power kW'</li></ul> |
| 5 | <ul><li>'Tiong Bahru Plaza, DDC L12, AHU 10-1 VALVE CONTROL (Units: %)'</li><li>'Tiong Bahru Plaza, DDC-9-1, AHU 6-3 Valve Control'</li><li>'Tiong Bahru Plaza, DDC-L1-4, PAU-L1-05 valve control (Units: %)'</li></ul> |
| 2 | <ul><li>'Tiong Bahru Plaza, DDC-L20, L19 Fresh air damper feedback (Units: %)'</li><li>'Tiong Bahru Plaza, DDC-L1-3, AHU-L2-02 FAD feedback'</li><li>'Tiong Bahru Plaza, DDC L14-1, AHU 13-1 FAD Feedback'</li></ul> |
| 32 | <ul><li>'CT 3-1 Switch Mode'</li><li>'Tiong Bahru Plaza, UC800_3, Operating Mode'</li><li>'Tiong Bahru Plaza, UC800_102002_Chiller_2, Operating Mode'</li></ul> |
| 34 | <ul><li>'Tiong Bahru Plaza, DDC-CH-2, CH 2 CHWR TEMPERATURE (Units: ¬?C)'</li><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Chiller 3 CHWR Temperature'</li><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Chiller 1 CHWR Temperature'</li></ul> |
| 24 | <ul><li>'Tiong Bahru Plaza, DDC-L1-5, PAU-L1-06 VSD control'</li><li>'Tiong Bahru Plaza, DDC-L3-01, PAU-L3-03 VSD control'</li><li>'Tiong Bahru Plaza, DDC-L3-2, PAU-L3-01 VSD control'</li></ul> |
| 39 | <ul><li>'Tiong Bahru Plaza, TBP_UNO_Server, Chiller 1_CWRT'</li><li>'Tiong Bahru Plaza, TBP_UNO_Server, CH1_CWRT'</li><li>'Tiong Bahru Plaza, UC800_102005, Cond Entering Water Temp (Units: °C)'</li></ul> |
| 13 | <ul><li>'Tiong Bahru Plaza, DDC L14-1, AHU 14-1 TRIP ALARM'</li><li>'Tiong Bahru Plaza, DDC B1-3, Pau-B1-02-Trip Alarm'</li><li>'Tiong Bahru Plaza, DDC-B1-5, AHU-B1-2-Trip'</li></ul> |
| 9 | <ul><li>'Tiong Bahru Plaza, DDC-L3-2, AHU-6-2A VSD feedback'</li><li>'Tiong Bahru Plaza, DDC-L1-5, AHU-L3-04A VSD feedback'</li><li>'Tiong Bahru Plaza, DDC-L1-1, PAU-L1-01 VSD feedback'</li></ul> |
| 17 | <ul><li>'Tiong Bahru Plaza, DDC-L1-3, PAU-L1-02 switch mode'</li><li>'Tiong Bahru Plaza, DDC-L2-6, PAU-L2-01 switch mode'</li><li>'Tiong Bahru Plaza, DDC B1-3, Pau-B1-02-Switch Mode'</li></ul> |
| 14 | <ul><li>'Tiong Bahru Plaza, VAV 19-21, Space Temperature (Units: °C)'</li><li>'Tiong Bahru Plaza, VAV 19-11, Space Temperature (Units: °C)'</li><li>'Tiong Bahru Plaza, VAV-19-3, Space Temperature (Units: °C)'</li></ul> |
| 45 | <ul><li>'Tiong Bahru Plaza, DDC-CH-4, CHWP 4 VSD Feedback'</li><li>'Tiong Bahru Plaza, DDC-CH-4, Cooling Tower 4-1 VSD Feedback'</li><li>'Tiong Bahru Plaza, DDC-CH-4, Condenser Water Pump 4 VSD Feedback'</li></ul> |
| 27 | <ul><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Chiller 1 CHW Flowrate'</li><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Chiller 4 CHW Flowrate'</li><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Chiller 3 CHW Flowrate'</li></ul> |
| 21 | <ul><li>'Tiong Bahru Plaza, VAV 19-6, Active Setpoint (Units: °C)'</li><li>'Tiong Bahru Plaza, VAV 19-15, Active Setpoint (Units: °C)'</li><li>'Tiong Bahru Plaza, VAV 19-13, Active Setpoint (Units: °C)'</li></ul> |
| 7 | <ul><li>'Tiong Bahru Plaza, DDC-L2-5, AHU-L2-03 returm air temperature (Units: °C)'</li><li>'Tiong Bahru Plaza, DDC-L3-2, AHU-6-2A returm air temperature (Units: °C)'</li><li>'Tiong Bahru Plaza, DDC-L1-5, AHU-L3-04A returm air temperature (Units: °C)'</li></ul> |
| 29 | <ul><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Header CWS Temp'</li><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Header CWS Temperature'</li><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Head CWS Temp'</li></ul> |
| 1 | <ul><li>'Tiong Bahru Plaza, DDC B1-4, PAU-B1-1 Static pressure'</li><li>'Tiong Bahru Plaza, DDC-L3-2, PAU-L3-01 static pressure (Units: Pa)'</li><li>'Tiong Bahru Plaza, DDC-L1-4, PAU-L1-05 Static pressure (Units: Pa)'</li></ul> |
| 46 | <ul><li>'Tiong Bahru Plaza, SC-10, Chiller SC, System Condenser Water Supply Temperature Setpoint'</li><li>'Tiong Bahru Plaza, SC-10, Chiller SC, System Condenser Water Supply Temperature Setpoint'</li><li>'Tiong Bahru Plaza, SC-10, Chiller SC, System Condenser Water Supply Temperature Setpoint'</li></ul> |
| 11 | <ul><li>'Tiong Bahru Plaza, DDC-L17, AHU 16-1 On/Off Status'</li><li>'Tiong Bahru Plaza, DDC L14-1, AHU 14-1 ON/OFF STATUS'</li><li>'Tiong Bahru Plaza, DDC-9-1, AHU 8-1 On/Off Status'</li></ul> |
| 33 | <ul><li>'Tiong Bahru Plaza, UC800_102002_Chiller_2, Condenser Saturated Refrigerant Temperature Circuit 1 (Units: °C)'</li><li>'Tiong Bahru Plaza, UC800_102002_Chiller_2, Evaporator Saturated Refrigerant Temperature - Circuit 1 (Units: °C)'</li><li>'Tiong Bahru Plaza, UC800_102004, Cond Saturated Refrigerant Temp Sensor Chiller'</li></ul> |
| 8 | <ul><li>'Tiong Bahru Plaza, DDC L4-1, PAU-L4-03 supply air temperature (Units: °C)'</li><li>'Tiong Bahru Plaza, DDC L4-1, PAU-L4-03 supply air temperature (Units: °C).1'</li><li>'Tiong Bahru Plaza, DDC L4-1, PAU-L4-02 supply air temperature (Units: °C).1'</li></ul> |
| 16 | <ul><li>'Tiong Bahru Plaza, VAV 19-11, Discharge Air Temperature (Units: °C)'</li><li>'Tiong Bahru Plaza, VAV 19-9, Discharge Air Temperature (Units: °C)'</li><li>'Tiong Bahru Plaza, VAV 18-2, Discharge Air Temperature (Units: °C)'</li></ul> |
| 35 | <ul><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Chiller 3 CHWS Temperature'</li><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Chiller 4 CHWS Temperature'</li><li>'Tiong Bahru Plaza, DDC-CH-2, CH 2 CHWS TEMPERATURE (Units: ¬?C)'</li></ul> |
| 30 | <ul><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Header CWR Temperature'</li><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Head CWR Temp'</li><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Header CWR Temp'</li></ul> |
| 3 | <ul><li>'Tiong Bahru Plaza, DDC-L2-2, AHU-L2-05 FAD control (Units: %)'</li><li>'Tiong Bahru Plaza, DDC-L1-5, AHU-L3-04A FAD control'</li><li>'Tiong Bahru Plaza, DDC-L6, AHU 4-1 FAD Control'</li></ul> |
| 44 | <ul><li>'Tiong Bahru Plaza, DDC-CH-1, Chiller 1 VSD Control'</li><li>'Tiong Bahru Plaza, DDC-CH-4, CWP 4 VSD Control'</li><li>'Tiong Bahru Plaza, DDC-CH-4, Condenser Water Pump 4 VSD Control'</li></ul> |
| 20 | <ul><li>'Tiong Bahru Plaza, VAV 19-13, Air Flow Setpoint Active (Units: m3/h)'</li><li>'Tiong Bahru Plaza, VAV 19-17A, Air Flow Setpoint Active (Units: m3/h)'</li><li>'Tiong Bahru Plaza, VAV-19-20, Air Flow Setpoint Active (Units: m3/h)'</li></ul> |
| 23 | <ul><li>'Tiong Bahru Plaza, DDC-L20, PAHU TR-1 TEMPERATURE (Units: °C)'</li></ul> |
| 41 | <ul><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Total System Heat Balance'</li><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Chiller 1 Effficiency'</li><li>'Tiong Bahru Plaza, SC-10, Chiller SC, System Cooling Load'</li></ul> |
| 25 | <ul><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Header CHWS Temp'</li><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Header CHWS Temp'</li><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Header CHWS Temperature'</li></ul> |
| 36 | <ul><li>'Tiong Bahru Plaza, UC800_101001_Chiller_1, Entering Condenser Water (Units: °C)'</li><li>'Tiong Bahru Plaza, UC800_3, Entering Condenser Water (Units: °C)'</li><li>'Tiong Bahru Plaza, UC800_102002_Chiller_2, Entering Condenser Water (Units: °C)'</li></ul> |
| 12 | <ul><li>'Tiong Bahru Plaza, DDC-L20, PAHU TR-1(R-1) SMOKE ALARM'</li><li>'Tiong Bahru Plaza, DDC-9-1, AHU 7-1 Smoke Alarm'</li><li>'Tiong Bahru Plaza, DDC-L3-3, AHU-L3-1 smoke alarm'</li></ul> |
| 37 | <ul><li>'Wet Bulb Temperature'</li><li>'Tiong Bahru Plaza, SC-4, wet bulb'</li></ul> |
| 31 | <ul><li>'Tiong Bahru Plaza, UC800_102005, Cond Water Flow'</li><li>'Tiong Bahru Plaza, UC800_102005, Condenser Water Flow'</li><li>'Tiong Bahru Plaza, UC800_102004, Cond Water Flow'</li></ul> |
| 38 | <ul><li>'Tiong Bahru Plaza, SC-10, Chiller SC, Header Differential Pressure'</li><li>' Chiller SC:Header Differential Pressure'</li></ul> |
| 18 | <ul><li>'Tiong Bahru Plaza, DDC_L4-3, Outdoor humidity (Units: %)'</li><li>'Tiong Bahru Plaza, DDC_L4-3, Outdoor humidity (Units: %).1'</li><li>'Tiong Bahru Plaza, DDC_L4-3, Outdoor humidity (Units: %).2'</li></ul> |
| 19 | <ul><li>'Tiong Bahru Plaza, DDC_L4-3, Outdoor temperature (Units: °C).1'</li><li>'Tiong Bahru Plaza, DDC_L4-3, Outdoor temperature (Units: °C)'</li><li>'Tiong Bahru Plaza, DDC_L4-3, Outdoor temperature (Units: °C).2'</li></ul> |
| 42 | <ul><li>'Tiong Bahru Plaza, TBP Chiller Plant, Chilled Water Setpoint (Units: ¬?C)'</li><li>'Tiong Bahru Plaza, TBP Chiller Plant, Chilled Water Setpoint (Units: ¬?C)'</li></ul> |
| 22 | <ul><li>'Tiong Bahru Plaza, DDC B1-3, Pau-B1-02-DP Sensor'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.8104 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("Varun1010/all-MiniLM-L6-v2-polaris-tb-new")
# Run inference
preds = model("Tiong Bahru Plaza, DDC-L2-5, AHU-L2-03 trip alarm")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 5 | 8.4138 | 14 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 10 |
| 1 | 10 |
| 2 | 10 |
| 3 | 10 |
| 4 | 10 |
| 5 | 10 |
| 6 | 10 |
| 7 | 10 |
| 8 | 10 |
| 9 | 10 |
| 10 | 10 |
| 11 | 10 |
| 12 | 10 |
| 13 | 10 |
| 14 | 10 |
| 15 | 10 |
| 16 | 10 |
| 17 | 10 |
| 18 | 3 |
| 19 | 3 |
| 20 | 10 |
| 21 | 10 |
| 22 | 1 |
| 23 | 1 |
| 24 | 10 |
| 25 | 4 |
| 26 | 10 |
| 27 | 8 |
| 28 | 4 |
| 29 | 3 |
| 30 | 3 |
| 31 | 4 |
| 32 | 4 |
| 33 | 9 |
| 34 | 5 |
| 35 | 4 |
| 36 | 3 |
| 37 | 2 |
| 38 | 2 |
| 39 | 9 |
| 40 | 10 |
| 41 | 4 |
| 42 | 2 |
| 43 | 3 |
| 44 | 8 |
| 45 | 8 |
| 46 | 3 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 16)
- max_steps: 500
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0006 | 1 | 0.1561 | - |
| 0.0302 | 50 | 0.1223 | - |
| 0.0604 | 100 | 0.0757 | - |
| 0.0905 | 150 | 0.0607 | - |
| 0.1207 | 200 | 0.0505 | - |
| 0.1509 | 250 | 0.0571 | - |
| 0.1811 | 300 | 0.0287 | - |
| 0.2112 | 350 | 0.0362 | - |
| 0.2414 | 400 | 0.0214 | - |
| 0.2716 | 450 | 0.0239 | - |
| 0.3018 | 500 | 0.0315 | - |
| 0.3319 | 550 | 0.0174 | - |
| 0.3621 | 600 | 0.0316 | - |
| 0.3923 | 650 | 0.016 | - |
| 0.4225 | 700 | 0.0342 | - |
| 0.4526 | 750 | 0.0245 | - |
| 0.4828 | 800 | 0.0229 | - |
| 0.5130 | 850 | 0.0098 | - |
| 0.5432 | 900 | 0.0114 | - |
| 0.5733 | 950 | 0.0097 | - |
| 0.6035 | 1000 | 0.0149 | - |
| 0.6337 | 1050 | 0.0115 | - |
| 0.6639 | 1100 | 0.0107 | - |
| 0.6940 | 1150 | 0.0092 | - |
| 0.7242 | 1200 | 0.0068 | - |
| 0.7544 | 1250 | 0.0097 | - |
| 0.7846 | 1300 | 0.0305 | - |
| 0.8147 | 1350 | 0.0055 | - |
| 0.8449 | 1400 | 0.0068 | - |
| 0.8751 | 1450 | 0.0066 | - |
| 0.9053 | 1500 | 0.0101 | - |
| 0.9354 | 1550 | 0.0085 | - |
| 0.9656 | 1600 | 0.0056 | - |
| 0.9958 | 1650 | 0.0041 | - |
| 1.0260 | 1700 | 0.0117 | - |
| 1.0561 | 1750 | 0.0178 | - |
| 1.0863 | 1800 | 0.005 | - |
| 1.1165 | 1850 | 0.0086 | - |
| 1.1467 | 1900 | 0.0188 | - |
| 1.1768 | 1950 | 0.0106 | - |
| 1.2070 | 2000 | 0.0084 | - |
| 1.2372 | 2050 | 0.0054 | - |
| 1.2674 | 2100 | 0.0058 | - |
| 1.2975 | 2150 | 0.0089 | - |
| 1.3277 | 2200 | 0.0167 | - |
| 1.3579 | 2250 | 0.009 | - |
| 1.3881 | 2300 | 0.0108 | - |
| 1.4182 | 2350 | 0.0026 | - |
| 1.4484 | 2400 | 0.0076 | - |
| 1.4786 | 2450 | 0.003 | - |
| 1.5088 | 2500 | 0.0065 | - |
| 1.5389 | 2550 | 0.0066 | - |
| 1.5691 | 2600 | 0.0026 | - |
| 1.5993 | 2650 | 0.0156 | - |
| 1.6295 | 2700 | 0.0037 | - |
| 1.6596 | 2750 | 0.0021 | - |
| 1.6898 | 2800 | 0.0026 | - |
| 1.7200 | 2850 | 0.0059 | - |
| 1.7502 | 2900 | 0.0022 | - |
| 1.7803 | 2950 | 0.0109 | - |
| 1.8105 | 3000 | 0.0043 | - |
| 1.8407 | 3050 | 0.0032 | - |
| 1.8709 | 3100 | 0.0065 | - |
| 1.9010 | 3150 | 0.0043 | - |
| 1.9312 | 3200 | 0.0021 | - |
| 1.9614 | 3250 | 0.0018 | - |
| 1.9916 | 3300 | 0.0078 | - |
| 2.0217 | 3350 | 0.0058 | - |
| 2.0519 | 3400 | 0.0018 | - |
| 2.0821 | 3450 | 0.0032 | - |
| 2.1123 | 3500 | 0.0149 | - |
| 2.1424 | 3550 | 0.0078 | - |
| 2.1726 | 3600 | 0.0065 | - |
| 2.2028 | 3650 | 0.0026 | - |
| 2.2330 | 3700 | 0.0048 | - |
| 2.2631 | 3750 | 0.0027 | - |
| 2.2933 | 3800 | 0.003 | - |
| 2.3235 | 3850 | 0.0014 | - |
| 2.3537 | 3900 | 0.0031 | - |
| 2.3838 | 3950 | 0.0023 | - |
| 2.4140 | 4000 | 0.0021 | - |
| 2.4442 | 4050 | 0.002 | - |
| 2.4744 | 4100 | 0.002 | - |
| 2.5045 | 4150 | 0.0022 | - |
| 2.5347 | 4200 | 0.0063 | - |
| 2.5649 | 4250 | 0.004 | - |
| 2.5951 | 4300 | 0.0032 | - |
| 2.6252 | 4350 | 0.0022 | - |
| 2.6554 | 4400 | 0.0017 | - |
| 2.6856 | 4450 | 0.0014 | - |
| 2.7158 | 4500 | 0.0023 | - |
| 2.7459 | 4550 | 0.002 | - |
| 2.7761 | 4600 | 0.0021 | - |
| 2.8063 | 4650 | 0.0047 | - |
| 2.8365 | 4700 | 0.0026 | - |
| 2.8666 | 4750 | 0.0015 | - |
| 2.8968 | 4800 | 0.0014 | - |
| 2.9270 | 4850 | 0.0035 | - |
| 2.9572 | 4900 | 0.0012 | - |
| 2.9873 | 4950 | 0.0035 | - |
| 3.0175 | 5000 | 0.0011 | - |
| 3.0477 | 5050 | 0.0015 | - |
| 3.0779 | 5100 | 0.0012 | - |
| 3.1080 | 5150 | 0.0018 | - |
| 3.1382 | 5200 | 0.0013 | - |
| 3.1684 | 5250 | 0.0009 | - |
| 3.1986 | 5300 | 0.0176 | - |
| 3.2287 | 5350 | 0.014 | - |
| 3.2589 | 5400 | 0.0011 | - |
| 3.2891 | 5450 | 0.0013 | - |
| 3.3193 | 5500 | 0.0015 | - |
| 3.3494 | 5550 | 0.003 | - |
| 3.3796 | 5600 | 0.0043 | - |
| 3.4098 | 5650 | 0.001 | - |
| 3.4400 | 5700 | 0.0013 | - |
| 3.4701 | 5750 | 0.0021 | - |
| 3.5003 | 5800 | 0.0011 | - |
| 3.5305 | 5850 | 0.0011 | - |
| 3.5607 | 5900 | 0.0017 | - |
| 3.5908 | 5950 | 0.0019 | - |
| 3.6210 | 6000 | 0.0023 | - |
| 3.6512 | 6050 | 0.0012 | - |
| 3.6814 | 6100 | 0.0025 | - |
| 3.7115 | 6150 | 0.0033 | - |
| 3.7417 | 6200 | 0.0025 | - |
| 3.7719 | 6250 | 0.0009 | - |
| 3.8021 | 6300 | 0.0033 | - |
| 3.8322 | 6350 | 0.0031 | - |
| 3.8624 | 6400 | 0.0017 | - |
| 3.8926 | 6450 | 0.001 | - |
| 3.9228 | 6500 | 0.0015 | - |
| 3.9529 | 6550 | 0.001 | - |
| 3.9831 | 6600 | 0.0151 | - |
| 4.0133 | 6650 | 0.0015 | - |
| 4.0435 | 6700 | 0.0011 | - |
| 4.0736 | 6750 | 0.0012 | - |
| 4.1038 | 6800 | 0.0056 | - |
| 4.1340 | 6850 | 0.0029 | - |
| 4.1642 | 6900 | 0.0009 | - |
| 4.1943 | 6950 | 0.0014 | - |
| 4.2245 | 7000 | 0.0009 | - |
| 4.2547 | 7050 | 0.001 | - |
| 4.2849 | 7100 | 0.0017 | - |
| 4.3150 | 7150 | 0.0011 | - |
| 4.3452 | 7200 | 0.0013 | - |
| 4.3754 | 7250 | 0.0009 | - |
| 4.4056 | 7300 | 0.0012 | - |
| 4.4357 | 7350 | 0.0011 | - |
| 4.4659 | 7400 | 0.015 | - |
| 4.4961 | 7450 | 0.0009 | - |
| 4.5263 | 7500 | 0.001 | - |
| 4.5564 | 7550 | 0.0008 | - |
| 4.5866 | 7600 | 0.0053 | - |
| 4.6168 | 7650 | 0.0011 | - |
| 4.6470 | 7700 | 0.0009 | - |
| 4.6771 | 7750 | 0.0009 | - |
| 4.7073 | 7800 | 0.0011 | - |
| 4.7375 | 7850 | 0.0013 | - |
| 4.7677 | 7900 | 0.0009 | - |
| 4.7978 | 7950 | 0.0009 | - |
| 4.8280 | 8000 | 0.0011 | - |
| 4.8582 | 8050 | 0.0026 | - |
| 4.8884 | 8100 | 0.0012 | - |
| 4.9185 | 8150 | 0.0009 | - |
| 4.9487 | 8200 | 0.0027 | - |
| 4.9789 | 8250 | 0.0011 | - |
| 0.0020 | 1 | 0.9072 | - |
| 0.0998 | 50 | 0.4281 | - |
| 0.1996 | 100 | 0.0055 | - |
| 0.2994 | 150 | 0.0013 | - |
| 0.3992 | 200 | 0.0009 | - |
| 0.4990 | 250 | 0.0006 | - |
| 0.5988 | 300 | 0.0003 | - |
| 0.6986 | 350 | 0.0004 | - |
| 0.7984 | 400 | 0.0003 | - |
| 0.8982 | 450 | 0.0003 | - |
| 0.9980 | 500 | 0.0004 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.38.2
- PyTorch: 2.2.1+cu121
- Datasets: 2.19.0
- Tokenizers: 0.15.2
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> | {"library_name": "setfit", "tags": ["setfit", "sentence-transformers", "text-classification", "generated_from_setfit_trainer"], "metrics": ["accuracy"], "base_model": "sentence-transformers/paraphrase-MiniLM-L3-v2", "widget": [{"text": "Tiong Bahru Plaza, DDC-L2-5, AHU-L2-03 trip alarm"}, {"text": "Tiong Bahru Plaza, DDC L4-1, PAU-L4-03 supply air temperature (Units: \u00c2\u00b0C).2"}, {"text": "Tiong Bahru Plaza, DDC-L2-5, AHU-L2-03 VSD control"}, {"text": "Tiong Bahru Plaza, VAV 19-7, Discharge Air Flow (Units: m3/h)"}, {"text": "Tiong Bahru Plaza, DDC-L1-4, PAU-L1-05 VSD control"}], "pipeline_tag": "text-classification", "inference": true, "model-index": [{"name": "SetFit with sentence-transformers/paraphrase-MiniLM-L3-v2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "Unknown", "type": "unknown", "split": "test"}, "metrics": [{"type": "accuracy", "value": 0.8104265402843602, "name": "Accuracy"}]}]}]} | Varun1010/all-MiniLM-L6-v2-polaris-tb-new | null | [
"setfit",
"safetensors",
"bert",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:sentence-transformers/paraphrase-MiniLM-L3-v2",
"model-index",
"region:us"
] | null | 2024-04-21T17:32:59+00:00 | [
"2209.11055"
] | [] | TAGS
#setfit #safetensors #bert #sentence-transformers #text-classification #generated_from_setfit_trainer #arxiv-2209.11055 #base_model-sentence-transformers/paraphrase-MiniLM-L3-v2 #model-index #region-us
| SetFit with sentence-transformers/paraphrase-MiniLM-L3-v2
=========================================================
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-MiniLM-L3-v2 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a Sentence Transformer with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
Model Details
-------------
### Model Description
* Model Type: SetFit
* Sentence Transformer body: sentence-transformers/paraphrase-MiniLM-L3-v2
* Classification head: a LogisticRegression instance
* Maximum Sequence Length: 128 tokens
* Number of Classes: 47 classes
### Model Sources
* Repository: SetFit on GitHub
* Paper: Efficient Few-Shot Learning Without Prompts
* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
### Model Labels
Evaluation
----------
### Metrics
Uses
----
### Direct Use for Inference
First install the SetFit library:
Then you can load this model and run inference.
Training Details
----------------
### Training Set Metrics
### Training Hyperparameters
* batch\_size: (16, 16)
* num\_epochs: (1, 16)
* max\_steps: 500
* sampling\_strategy: oversampling
* body\_learning\_rate: (2e-05, 1e-05)
* head\_learning\_rate: 0.01
* loss: CosineSimilarityLoss
* distance\_metric: cosine\_distance
* margin: 0.25
* end\_to\_end: False
* use\_amp: False
* warmup\_proportion: 0.1
* seed: 42
* eval\_max\_steps: -1
* load\_best\_model\_at\_end: False
### Training Results
### Framework Versions
* Python: 3.10.12
* SetFit: 1.0.3
* Sentence Transformers: 2.7.0
* Transformers: 4.38.2
* PyTorch: 2.2.1+cu121
* Datasets: 2.19.0
* Tokenizers: 0.15.2
### BibTeX
| [
"### Model Description\n\n\n* Model Type: SetFit\n* Sentence Transformer body: sentence-transformers/paraphrase-MiniLM-L3-v2\n* Classification head: a LogisticRegression instance\n* Maximum Sequence Length: 128 tokens\n* Number of Classes: 47 classes",
"### Model Sources\n\n\n* Repository: SetFit on GitHub\n* Paper: Efficient Few-Shot Learning Without Prompts\n* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts",
"### Model Labels\n\n\n\nEvaluation\n----------",
"### Metrics\n\n\n\nUses\n----",
"### Direct Use for Inference\n\n\nFirst install the SetFit library:\n\n\nThen you can load this model and run inference.\n\n\nTraining Details\n----------------",
"### Training Set Metrics",
"### Training Hyperparameters\n\n\n* batch\\_size: (16, 16)\n* num\\_epochs: (1, 16)\n* max\\_steps: 500\n* sampling\\_strategy: oversampling\n* body\\_learning\\_rate: (2e-05, 1e-05)\n* head\\_learning\\_rate: 0.01\n* loss: CosineSimilarityLoss\n* distance\\_metric: cosine\\_distance\n* margin: 0.25\n* end\\_to\\_end: False\n* use\\_amp: False\n* warmup\\_proportion: 0.1\n* seed: 42\n* eval\\_max\\_steps: -1\n* load\\_best\\_model\\_at\\_end: False",
"### Training Results",
"### Framework Versions\n\n\n* Python: 3.10.12\n* SetFit: 1.0.3\n* Sentence Transformers: 2.7.0\n* Transformers: 4.38.2\n* PyTorch: 2.2.1+cu121\n* Datasets: 2.19.0\n* Tokenizers: 0.15.2",
"### BibTeX"
] | [
"TAGS\n#setfit #safetensors #bert #sentence-transformers #text-classification #generated_from_setfit_trainer #arxiv-2209.11055 #base_model-sentence-transformers/paraphrase-MiniLM-L3-v2 #model-index #region-us \n",
"### Model Description\n\n\n* Model Type: SetFit\n* Sentence Transformer body: sentence-transformers/paraphrase-MiniLM-L3-v2\n* Classification head: a LogisticRegression instance\n* Maximum Sequence Length: 128 tokens\n* Number of Classes: 47 classes",
"### Model Sources\n\n\n* Repository: SetFit on GitHub\n* Paper: Efficient Few-Shot Learning Without Prompts\n* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts",
"### Model Labels\n\n\n\nEvaluation\n----------",
"### Metrics\n\n\n\nUses\n----",
"### Direct Use for Inference\n\n\nFirst install the SetFit library:\n\n\nThen you can load this model and run inference.\n\n\nTraining Details\n----------------",
"### Training Set Metrics",
"### Training Hyperparameters\n\n\n* batch\\_size: (16, 16)\n* num\\_epochs: (1, 16)\n* max\\_steps: 500\n* sampling\\_strategy: oversampling\n* body\\_learning\\_rate: (2e-05, 1e-05)\n* head\\_learning\\_rate: 0.01\n* loss: CosineSimilarityLoss\n* distance\\_metric: cosine\\_distance\n* margin: 0.25\n* end\\_to\\_end: False\n* use\\_amp: False\n* warmup\\_proportion: 0.1\n* seed: 42\n* eval\\_max\\_steps: -1\n* load\\_best\\_model\\_at\\_end: False",
"### Training Results",
"### Framework Versions\n\n\n* Python: 3.10.12\n* SetFit: 1.0.3\n* Sentence Transformers: 2.7.0\n* Transformers: 4.38.2\n* PyTorch: 2.2.1+cu121\n* Datasets: 2.19.0\n* Tokenizers: 0.15.2",
"### BibTeX"
] |
null | adapter-transformers |
# Adapter `BigTMiami/n_par_bn_v_1_help_class_20_e_adp_lr_0003_S_3` 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/n_par_bn_v_1_help_class_20_e_adp_lr_0003_S_3", 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_MICRO_helpfulness_dataset"]} | BigTMiami/n_par_bn_v_1_help_class_20_e_adp_lr_0003_S_3 | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/amazon_MICRO_helpfulness_dataset",
"region:us"
] | null | 2024-04-21T17:33:09+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/amazon_MICRO_helpfulness_dataset #region-us
|
# Adapter 'BigTMiami/n_par_bn_v_1_help_class_20_e_adp_lr_0003_S_3' 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/n_par_bn_v_1_help_class_20_e_adp_lr_0003_S_3' 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/n_par_bn_v_1_help_class_20_e_adp_lr_0003_S_3' 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"
] |
null | transformers | A safetensors conversion of t5xxl for a personal project | {} | HDiffusion/t5-v1_1-xxl_safetensors | null | [
"transformers",
"safetensors",
"t5",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T17:33:37+00:00 | [] | [] | TAGS
#transformers #safetensors #t5 #endpoints_compatible #text-generation-inference #region-us
| A safetensors conversion of t5xxl for a personal project | [] | [
"TAGS\n#transformers #safetensors #t5 #endpoints_compatible #text-generation-inference #region-us \n"
] |
feature-extraction | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | Ehsanl/m3-ranker-fl18-e1 | null | [
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T17:35:44+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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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
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] | [
"TAGS\n#transformers #safetensors #xlm-roberta #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",
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"## Glossary [optional]",
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] |
text-generation | transformers | # eq90parsedanube
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
First one that's shown promising capability improvement over the base model `h2o-danube2-1.8b-base`.
Training methodology ... is a bit of a mess, trying out different things.
I'm adding the datasets used at any point, but I don't think replicating the recipe is doable or sensible.
Original upscale at Lambent/danube2-upscale-1, duplicating layers 16-21. Various training methods attempted to repair.
Linear merge is of the 4 that were at least 90% parseable by the EQ-Bench benchmark.
| Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
|-------------------------------------------------------------------------|------:|------:|---------:|-------:|------:|
|[danube2-upscale-1.7](https://huggingface.co/Lambent/danube2-upscale-1.7)| 27.97| 62.16| 42.2| 32.2| 41.13|
| Model |EQ-Bench|Average|
|-------------------------------------------------------------------------|-------:|------:|
|[danube2-upscale-1.7](https://huggingface.co/Lambent/danube2-upscale-1.7)| 15.52| 15.52|
### EQ-Bench
| Task |Version| Metric | Value | |Stderr|
|--------|------:|-----------------------------|--------|---|------|
|eq_bench| 2.1|eqbench,none | 15.52| | |
| | |eqbench_stderr,none | 2.77| | |
| | |percent_parseable,none | 100| | |
| | |percent_parseable_stderr,none| 0| | |
| | |alias |eq_bench| | |
Average: 15.52%
Average score: 15.52%
## Merge Details
### Merge Method
This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method.
### Models Merged
The following models were included in the merge:
* [Lambent/danube2-upscale-1.53lisa](https://huggingface.co/Lambent/danube2-upscale-1.53lisa)
* [Lambent/danube2-upscale-1.51galore](https://huggingface.co/Lambent/danube2-upscale-1.51galore)
* [Lambent/danube2-upscale-1.531qlora](https://huggingface.co/Lambent/danube2-upscale-1.531qlora)
* [Lambent/danube2-upscale-1.51qlora](https://huggingface.co/Lambent/danube2-upscale-1.51qlora)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: Lambent/danube2-upscale-1.531qlora
parameters:
weight: 1.0
- model: Lambent/danube2-upscale-1.53lisa
parameters:
weight: 1.0
- model: Lambent/danube2-upscale-1.51galore
parameters:
weight: 1.0
- model: Lambent/danube2-upscale-1.51qlora
parameters:
weight: 1.0
merge_method: linear
dtype: float16
``` | {"license": "apache-2.0", "library_name": "transformers", "tags": ["mergekit", "merge"], "datasets": ["HuggingFaceTB/cosmopedia-100k", "Vezora/Tested-22k-Python-Alpaca", "sordonia/redpajama-sample_from_valid_all", "nampdn-ai/tiny-bridgedict", "teknium/GPTeacher-General-Instruct", "Severian/Internal-Knowledge-Map", "Severian/Internal-Knowledge-Map-StoryWriter-RolePlaying"], "base_model": ["Lambent/danube2-upscale-1.53lisa", "Lambent/danube2-upscale-1.51galore", "Lambent/danube2-upscale-1.531qlora", "Lambent/danube2-upscale-1.51qlora"]} | Lambent/danube2-upscale-1.7 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"dataset:HuggingFaceTB/cosmopedia-100k",
"dataset:Vezora/Tested-22k-Python-Alpaca",
"dataset:sordonia/redpajama-sample_from_valid_all",
"dataset:nampdn-ai/tiny-bridgedict",
"dataset:teknium/GPTeacher-General-Instruct",
"dataset:Severian/Internal-Knowledge-Map",
"dataset:Severian/Internal-Knowledge-Map-StoryWriter-RolePlaying",
"arxiv:2203.05482",
"base_model:Lambent/danube2-upscale-1.53lisa",
"base_model:Lambent/danube2-upscale-1.51galore",
"base_model:Lambent/danube2-upscale-1.531qlora",
"base_model:Lambent/danube2-upscale-1.51qlora",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T17:35:46+00:00 | [
"2203.05482"
] | [] | TAGS
#transformers #safetensors #mistral #text-generation #mergekit #merge #dataset-HuggingFaceTB/cosmopedia-100k #dataset-Vezora/Tested-22k-Python-Alpaca #dataset-sordonia/redpajama-sample_from_valid_all #dataset-nampdn-ai/tiny-bridgedict #dataset-teknium/GPTeacher-General-Instruct #dataset-Severian/Internal-Knowledge-Map #dataset-Severian/Internal-Knowledge-Map-StoryWriter-RolePlaying #arxiv-2203.05482 #base_model-Lambent/danube2-upscale-1.53lisa #base_model-Lambent/danube2-upscale-1.51galore #base_model-Lambent/danube2-upscale-1.531qlora #base_model-Lambent/danube2-upscale-1.51qlora #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| eq90parsedanube
===============
This is a merge of pre-trained language models created using mergekit.
First one that's shown promising capability improvement over the base model 'h2o-danube2-1.8b-base'.
Training methodology ... is a bit of a mess, trying out different things.
I'm adding the datasets used at any point, but I don't think replicating the recipe is doable or sensible.
Original upscale at Lambent/danube2-upscale-1, duplicating layers 16-21. Various training methods attempted to repair.
Linear merge is of the 4 that were at least 90% parseable by the EQ-Bench benchmark.
### EQ-Bench
Average: 15.52%
Average score: 15.52%
Merge Details
-------------
### Merge Method
This model was merged using the linear merge method.
### Models Merged
The following models were included in the merge:
* Lambent/danube2-upscale-1.53lisa
* Lambent/danube2-upscale-1.51galore
* Lambent/danube2-upscale-1.531qlora
* Lambent/danube2-upscale-1.51qlora
### Configuration
The following YAML configuration was used to produce this model:
| [
"### EQ-Bench\n\n\n\nAverage: 15.52%\n\n\nAverage score: 15.52%\n\n\nMerge Details\n-------------",
"### Merge Method\n\n\nThis model was merged using the linear merge method.",
"### Models Merged\n\n\nThe following models were included in the merge:\n\n\n* Lambent/danube2-upscale-1.53lisa\n* Lambent/danube2-upscale-1.51galore\n* Lambent/danube2-upscale-1.531qlora\n* Lambent/danube2-upscale-1.51qlora",
"### Configuration\n\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #dataset-HuggingFaceTB/cosmopedia-100k #dataset-Vezora/Tested-22k-Python-Alpaca #dataset-sordonia/redpajama-sample_from_valid_all #dataset-nampdn-ai/tiny-bridgedict #dataset-teknium/GPTeacher-General-Instruct #dataset-Severian/Internal-Knowledge-Map #dataset-Severian/Internal-Knowledge-Map-StoryWriter-RolePlaying #arxiv-2203.05482 #base_model-Lambent/danube2-upscale-1.53lisa #base_model-Lambent/danube2-upscale-1.51galore #base_model-Lambent/danube2-upscale-1.531qlora #base_model-Lambent/danube2-upscale-1.51qlora #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### EQ-Bench\n\n\n\nAverage: 15.52%\n\n\nAverage score: 15.52%\n\n\nMerge Details\n-------------",
"### Merge Method\n\n\nThis model was merged using the linear merge method.",
"### Models Merged\n\n\nThe following models were included in the merge:\n\n\n* Lambent/danube2-upscale-1.53lisa\n* Lambent/danube2-upscale-1.51galore\n* Lambent/danube2-upscale-1.531qlora\n* Lambent/danube2-upscale-1.51qlora",
"### Configuration\n\n\nThe following YAML configuration was used to produce this model:"
] |
text2text-generation | transformers |
# Model Card for Model ID
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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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| {"library_name": "transformers", "tags": []} | khairi/ProtNLA_t12x12_terms_v2 | null | [
"transformers",
"safetensors",
"encoder-decoder",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T17:36:13+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #encoder-decoder #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
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"## 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",
"## 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 #encoder-decoder #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### 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 | Mistral-7B Arabic [LAPT + CLP+ (Untied)]
===
## How to use
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/Mistral-7B-v0.1-clpp-untied-ar"
)
tokenizer = AutoTokenizer.from_pretrained(
"aubmindlab/aragpt2-base"
)
# w/ GPU
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/Mistral-7B-v0.1-clpp-untied-ar",
device_map="auto",
load_in_8bit=True,
)
```
## Citation
```
@article{yamaguchi2024empirical,
title={An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Generative {LLM} Inference},
author={Atsuki Yamaguchi and Aline Villavicencio and Nikolaos Aletras},
journal={ArXiv},
year={2024},
volume={abs/2402.10712},
url={https://arxiv.org/abs/2402.10712}
}
```
## Link
For more details, please visit https://github.com/gucci-j/llm-cva
| {"language": "ar", "license": "mit"} | atsuki-yamaguchi/Mistral-7B-v0.1-clpp-untied-ar | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"ar",
"arxiv:2402.10712",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T17:37:25+00:00 | [
"2402.10712"
] | [
"ar"
] | TAGS
#transformers #safetensors #mistral #text-generation #ar #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Mistral-7B Arabic [LAPT + CLP+ (Untied)]
===
## How to use
## Link
For more details, please visit URL
| [
"## How to use",
"## Link\nFor more details, please visit URL"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #ar #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How to use",
"## Link\nFor more details, please visit URL"
] |
text-generation | transformers | Mistral-7B Arabic [LAPT + CLP]
===
## How to use
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/Mistral-7B-v0.1-clp-ar"
)
tokenizer = AutoTokenizer.from_pretrained(
"aubmindlab/aragpt2-base"
)
# w/ GPU
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/Mistral-7B-v0.1-clp-ar",
device_map="auto",
load_in_8bit=True,
)
```
## Citation
```
@article{yamaguchi2024empirical,
title={An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Generative {LLM} Inference},
author={Atsuki Yamaguchi and Aline Villavicencio and Nikolaos Aletras},
journal={ArXiv},
year={2024},
volume={abs/2402.10712},
url={https://arxiv.org/abs/2402.10712}
}
```
## Link
For more details, please visit https://github.com/gucci-j/llm-cva
| {"language": "ar", "license": "mit"} | atsuki-yamaguchi/Mistral-7B-v0.1-clp-ar | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"ar",
"arxiv:2402.10712",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T17:37:46+00:00 | [
"2402.10712"
] | [
"ar"
] | TAGS
#transformers #safetensors #mistral #text-generation #ar #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Mistral-7B Arabic [LAPT + CLP]
===
## How to use
## Link
For more details, please visit URL
| [
"## How to use",
"## Link\nFor more details, please visit URL"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #ar #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How to use",
"## Link\nFor more details, please visit URL"
] |
text-generation | transformers | TigerBot-7B Japanese [LAPT + Heuristics (Untied)]
===
## How to use
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-heuristics-untied-ja"
)
tokenizer = AutoTokenizer.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-heuristics-untied-ja"
)
# w/ GPU
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-heuristics-untied-ja",
device_map="auto",
load_in_8bit=True,
)
```
## Citation
```
@article{yamaguchi2024empirical,
title={An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Generative {LLM} Inference},
author={Atsuki Yamaguchi and Aline Villavicencio and Nikolaos Aletras},
journal={ArXiv},
year={2024},
volume={abs/2402.10712},
url={https://arxiv.org/abs/2402.10712}
}
```
## Link
For more details, please visit https://github.com/gucci-j/llm-cva
| {"language": "ja", "license": "mit"} | atsuki-yamaguchi/tigerbot-7b-base-heuristics-untied-ja | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"ja",
"arxiv:2402.10712",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T17:38:06+00:00 | [
"2402.10712"
] | [
"ja"
] | TAGS
#transformers #safetensors #llama #text-generation #ja #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| TigerBot-7B Japanese [LAPT + Heuristics (Untied)]
===
## How to use
## Link
For more details, please visit URL
| [
"## How to use",
"## Link\nFor more details, please visit URL"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #ja #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How to use",
"## Link\nFor more details, please visit URL"
] |
text-generation | transformers |
# Uploaded model
- **Developed by:** Mandalor09
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-2-7b-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-2-7b-bnb-4bit"} | Mandalor09/Bargening-Model-llama2 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/llama-2-7b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T17:38:42+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #llama #text-generation #text-generation-inference #unsloth #trl #sft #en #base_model-unsloth/llama-2-7b-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: Mandalor09
- License: apache-2.0
- Finetuned from model : unsloth/llama-2-7b-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: Mandalor09\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-2-7b-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 #pytorch #llama #text-generation #text-generation-inference #unsloth #trl #sft #en #base_model-unsloth/llama-2-7b-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Uploaded model\n\n- Developed by: Mandalor09\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-2-7b-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-to-speech | speechbrain | <iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# Text-to-Speech (TTS) with Transformer trained on LJSpeech
This repository provides all the necessary tools for Text-to-Speech (TTS) with SpeechBrain using a [Transformer](https://arxiv.org/pdf/1809.08895.pdf) pretrained on [LJSpeech](https://keithito.com/LJ-Speech-Dataset/).
The pre-trained model takes in input a short text and produces a spectrogram in output. One can get the final waveform by applying a vocoder (e.g., HiFIGAN) on top of the generated spectrogram.
### Perform Text-to-Speech (TTS) - Running Inference
To run model inference pull the interface directory as shown in the cell below
Note: Run on T4-GPU for faster inference
```
!pip install --upgrade --no-cache-dir gdown
!gdown 1oy8Y5zwkLel7diA63GNCD-6cfoBV4tq7
!unzip inference.zip
```
```python
%%capture
!pip install speechbrain
%cd inference
```
```python
import torchaudio
from TTSModel import TTSModel
from IPython.display import Audio
from speechbrain.inference.vocoders import HIFIGAN
texts = ["This is a sample text for synthesis."]
model_source_path = "/content/inference"
# Intialize TTS (Transformer) and Vocoder (HiFIGAN)
my_tts_model = TTSModel.from_hparams(source=model_source_path)
hifi_gan = HIFIGAN.from_hparams(source="speechbrain/tts-hifigan-ljspeech", savedir="tmpdir_vocoder")
# Running the TTS
mel_output = my_tts_model.encode_text(texts)
# Running Vocoder (spectrogram-to-waveform)
waveforms = hifi_gan.decode_batch(mel_output)
# Save the waverform
torchaudio.save('example_TTS.wav',waveforms.squeeze(1), 22050)
print("Saved the audio file!")
```
If you want to generate multiple sentences in one-shot, pass the sentences as items in a list.
### Inference on GPU
To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.
Note: For Training the model please visit this [TTS_Training_Inference](https://colab.research.google.com/drive/1VYu4kXdgpv7f742QGquA1G4ipD2Kg0kT?usp=sharing) notebook
### Limitations
The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
# **About SpeechBrain**
- Website: https://speechbrain.github.io/
- Code: https://github.com/speechbrain/speechbrain/
- HuggingFace: https://huggingface.co/speechbrain/
# **Citing SpeechBrain**
Please, cite SpeechBrain if you use it for your research or business.
```bibtex
@misc{speechbrain,
title={{SpeechBrain}: A General-Purpose Speech Toolkit},
author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio},
year={2021},
eprint={2106.04624},
archivePrefix={arXiv},
primaryClass={eess.AS},
note={arXiv:2106.04624}
}
``` | {"language": "en", "license": "apache-2.0", "library_name": "speechbrain", "tags": ["text-to-speech", "TTS", "speech-synthesis", "Tacotron2", "speechbrain"], "datasets": ["LJSpeech"], "metrics": ["mos"], "pipeline_tag": "text-to-speech"} | Krisshvamsi/TTS | null | [
"speechbrain",
"text-to-speech",
"TTS",
"speech-synthesis",
"Tacotron2",
"en",
"dataset:LJSpeech",
"arxiv:1809.08895",
"arxiv:2106.04624",
"license:apache-2.0",
"region:us"
] | null | 2024-04-21T17:40:28+00:00 | [
"1809.08895",
"2106.04624"
] | [
"en"
] | TAGS
#speechbrain #text-to-speech #TTS #speech-synthesis #Tacotron2 #en #dataset-LJSpeech #arxiv-1809.08895 #arxiv-2106.04624 #license-apache-2.0 #region-us
| <iframe src="URL frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# Text-to-Speech (TTS) with Transformer trained on LJSpeech
This repository provides all the necessary tools for Text-to-Speech (TTS) with SpeechBrain using a Transformer pretrained on LJSpeech.
The pre-trained model takes in input a short text and produces a spectrogram in output. One can get the final waveform by applying a vocoder (e.g., HiFIGAN) on top of the generated spectrogram.
### Perform Text-to-Speech (TTS) - Running Inference
To run model inference pull the interface directory as shown in the cell below
Note: Run on T4-GPU for faster inference
If you want to generate multiple sentences in one-shot, pass the sentences as items in a list.
### Inference on GPU
To perform inference on the GPU, add 'run_opts={"device":"cuda"}' when calling the 'from_hparams' method.
Note: For Training the model please visit this TTS_Training_Inference notebook
### Limitations
The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
# About SpeechBrain
- Website: URL
- Code: URL
- HuggingFace: URL
# Citing SpeechBrain
Please, cite SpeechBrain if you use it for your research or business.
| [
"# Text-to-Speech (TTS) with Transformer trained on LJSpeech\n\nThis repository provides all the necessary tools for Text-to-Speech (TTS) with SpeechBrain using a Transformer pretrained on LJSpeech.\n\nThe pre-trained model takes in input a short text and produces a spectrogram in output. One can get the final waveform by applying a vocoder (e.g., HiFIGAN) on top of the generated spectrogram.",
"### Perform Text-to-Speech (TTS) - Running Inference\nTo run model inference pull the interface directory as shown in the cell below\n\nNote: Run on T4-GPU for faster inference\n\n\n\n\n\nIf you want to generate multiple sentences in one-shot, pass the sentences as items in a list.",
"### Inference on GPU\nTo perform inference on the GPU, add 'run_opts={\"device\":\"cuda\"}' when calling the 'from_hparams' method.\n\nNote: For Training the model please visit this TTS_Training_Inference notebook",
"### Limitations\nThe SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.",
"# About SpeechBrain\n- Website: URL\n- Code: URL\n- HuggingFace: URL",
"# Citing SpeechBrain\nPlease, cite SpeechBrain if you use it for your research or business."
] | [
"TAGS\n#speechbrain #text-to-speech #TTS #speech-synthesis #Tacotron2 #en #dataset-LJSpeech #arxiv-1809.08895 #arxiv-2106.04624 #license-apache-2.0 #region-us \n",
"# Text-to-Speech (TTS) with Transformer trained on LJSpeech\n\nThis repository provides all the necessary tools for Text-to-Speech (TTS) with SpeechBrain using a Transformer pretrained on LJSpeech.\n\nThe pre-trained model takes in input a short text and produces a spectrogram in output. One can get the final waveform by applying a vocoder (e.g., HiFIGAN) on top of the generated spectrogram.",
"### Perform Text-to-Speech (TTS) - Running Inference\nTo run model inference pull the interface directory as shown in the cell below\n\nNote: Run on T4-GPU for faster inference\n\n\n\n\n\nIf you want to generate multiple sentences in one-shot, pass the sentences as items in a list.",
"### Inference on GPU\nTo perform inference on the GPU, add 'run_opts={\"device\":\"cuda\"}' when calling the 'from_hparams' method.\n\nNote: For Training the model please visit this TTS_Training_Inference notebook",
"### Limitations\nThe SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.",
"# About SpeechBrain\n- Website: URL\n- Code: URL\n- HuggingFace: URL",
"# Citing SpeechBrain\nPlease, cite SpeechBrain if you use it for your research or business."
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="cuckookernel/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "FrozenLake-v1-4x4-no_slippery"}, "metrics": [{"type": "mean_reward", "value": "1.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]} | cuckookernel/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2024-04-21T17:40:49+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing1 FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] |
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": []} | akankshya107/llava_pt_1 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T17:42:08+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
null | adapter-transformers |
# Adapter `BigTMiami/n_par_bn_v_1_help_class_5_e_adp_lr_0003_S_4` 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/n_par_bn_v_1_help_class_5_e_adp_lr_0003_S_4", 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_MICRO_helpfulness_dataset"]} | BigTMiami/n_par_bn_v_1_help_class_5_e_adp_lr_0003_S_4 | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/amazon_MICRO_helpfulness_dataset",
"region:us"
] | null | 2024-04-21T17:42:40+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/amazon_MICRO_helpfulness_dataset #region-us
|
# Adapter 'BigTMiami/n_par_bn_v_1_help_class_5_e_adp_lr_0003_S_4' 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/n_par_bn_v_1_help_class_5_e_adp_lr_0003_S_4' 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/n_par_bn_v_1_help_class_5_e_adp_lr_0003_S_4' 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 | TigerBot-7B Swahili [LAPT + CLP+ (Untied)]
===
## How to use
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-clpp-untied-sw"
)
tokenizer = AutoTokenizer.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-clpp-untied-sw"
)
# w/ GPU
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-clpp-untied-sw",
device_map="auto",
load_in_8bit=True,
)
```
## Citation
```
@article{yamaguchi2024empirical,
title={An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Generative {LLM} Inference},
author={Atsuki Yamaguchi and Aline Villavicencio and Nikolaos Aletras},
journal={ArXiv},
year={2024},
volume={abs/2402.10712},
url={https://arxiv.org/abs/2402.10712}
}
```
## Link
For more details, please visit https://github.com/gucci-j/llm-cva
| {"language": "sw", "license": "mit"} | atsuki-yamaguchi/tigerbot-7b-base-clpp-untied-sw | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"sw",
"arxiv:2402.10712",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T17:43:58+00:00 | [
"2402.10712"
] | [
"sw"
] | TAGS
#transformers #safetensors #llama #text-generation #sw #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| TigerBot-7B Swahili [LAPT + CLP+ (Untied)]
===
## How to use
## Link
For more details, please visit URL
| [
"## How to use",
"## Link\nFor more details, please visit URL"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #sw #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How to use",
"## Link\nFor more details, please visit URL"
] |
text-generation | transformers | Mistral-7B Arabic [LAPT + Heuristics (Untied)]
===
## How to use
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/Mistral-7B-v0.1-heuristics-untied-ar"
)
tokenizer = AutoTokenizer.from_pretrained(
"aubmindlab/aragpt2-base"
)
# w/ GPU
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/Mistral-7B-v0.1-heuristics-untied-ar",
device_map="auto",
load_in_8bit=True,
)
```
## Citation
```
@article{yamaguchi2024empirical,
title={An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Generative {LLM} Inference},
author={Atsuki Yamaguchi and Aline Villavicencio and Nikolaos Aletras},
journal={ArXiv},
year={2024},
volume={abs/2402.10712},
url={https://arxiv.org/abs/2402.10712}
}
```
## Link
For more details, please visit https://github.com/gucci-j/llm-cva
| {"language": "ar", "license": "mit"} | atsuki-yamaguchi/Mistral-7B-v0.1-heuristics-untied-ar | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"ar",
"arxiv:2402.10712",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T17:44:23+00:00 | [
"2402.10712"
] | [
"ar"
] | TAGS
#transformers #safetensors #mistral #text-generation #ar #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Mistral-7B Arabic [LAPT + Heuristics (Untied)]
===
## How to use
## Link
For more details, please visit URL
| [
"## How to use",
"## Link\nFor more details, please visit URL"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #ar #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How to use",
"## Link\nFor more details, please visit URL"
] |
null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.8.2 | {"library_name": "peft", "base_model": "deepseek-ai/deepseek-coder-1.3b-instruct"} | CMU-AIR2/math-deepseek-lora-arith-simple-hard-5-step | null | [
"peft",
"safetensors",
"llama",
"arxiv:1910.09700",
"base_model:deepseek-ai/deepseek-coder-1.3b-instruct",
"region:us"
] | null | 2024-04-21T17:44:35+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #llama #arxiv-1910.09700 #base_model-deepseek-ai/deepseek-coder-1.3b-instruct #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
### Framework versions
- PEFT 0.8.2 | [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\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",
"### Framework versions\n\n- PEFT 0.8.2"
] | [
"TAGS\n#peft #safetensors #llama #arxiv-1910.09700 #base_model-deepseek-ai/deepseek-coder-1.3b-instruct #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\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",
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] |
null | null | # LLaMA3 License and Usage
## Introduction
The LLaMA3 model is equipped to deliver superior results in machine learning applications. This model is particularly effective when used in conjunction with the IF_AI_tools custom node for ComfyUI and the IF_PromptMKr, my extension for A1111 Forge and Next platforms.
## Model Training
LLaMA3 has been meticulously trained on a synthetic dataset comprising over 50,000 high-quality, stable diffusion prompts, ensuring robustness and high performance across various tasks.
## Useful Links
- [IF Prompt MKR](https://github.com/if-ai/IF_prompt_MKR)
- [ComfyUI-IF_AI_tools](https://github.com/if-ai/ComfyUI-IF_AI_tools)
## Support
Your support is invaluable in continuing the development and enhancement of tools like these. If you find this tool useful, please consider extending your support by:
- **Starring the repository** on GitHub: [Star ComfyUI-IF_AI_tools](https://github.com/if-ai/ComfyUI-IF_AI_tools)
- **Subscribing** to my YouTube channel: [Impact Frames on YouTube](https://youtube.com/@impactframes?si=DrBu3tOAC2-YbEvc)
- **Donating** on Ko-fi: [Support Impact Frames on Ko-fi](https://ko-fi.com/impactframes)
- **Becoming a patron** on Patreon: [Support via Patreon](https://patreon.com/ImpactFrames)
Thank you for your interest and support!
- **Developed by:** impactframes
- **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)
| {} | impactframes/llama3_if_ai_sdpromptmkr_q4km | null | [
"gguf",
"region:us"
] | null | 2024-04-21T17:45:05+00:00 | [] | [] | TAGS
#gguf #region-us
| # LLaMA3 License and Usage
!Model Visualization
## Introduction
The LLaMA3 model is equipped to deliver superior results in machine learning applications. This model is particularly effective when used in conjunction with the IF_AI_tools custom node for ComfyUI and the IF_PromptMKr, my extension for A1111 Forge and Next platforms.
## Model Training
LLaMA3 has been meticulously trained on a synthetic dataset comprising over 50,000 high-quality, stable diffusion prompts, ensuring robustness and high performance across various tasks.
## Useful Links
- IF Prompt MKR
- ComfyUI-IF_AI_tools
## Support
Your support is invaluable in continuing the development and enhancement of tools like these. If you find this tool useful, please consider extending your support by:
- Starring the repository on GitHub: Star ComfyUI-IF_AI_tools
- Subscribing to my YouTube channel: Impact Frames on YouTube
- Donating on Ko-fi: Support Impact Frames on Ko-fi
- Becoming a patron on Patreon: Support via Patreon
Thank you for your interest and support!
- Developed by: impactframes
- 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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"## Model Training\nLLaMA3 has been meticulously trained on a synthetic dataset comprising over 50,000 high-quality, stable diffusion prompts, ensuring robustness and high performance across various tasks.",
"## Useful Links\n- IF Prompt MKR\n- ComfyUI-IF_AI_tools",
"## Support\nYour support is invaluable in continuing the development and enhancement of tools like these. If you find this tool useful, please consider extending your support by:\n- Starring the repository on GitHub: Star ComfyUI-IF_AI_tools\n- Subscribing to my YouTube channel: Impact Frames on YouTube\n- Donating on Ko-fi: Support Impact Frames on Ko-fi\n- Becoming a patron on Patreon: Support via Patreon\n\nThank you for your interest and support!\n\n- Developed by: impactframes\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\"/>"
] |
feature-extraction | 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 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]
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[More Information Needed]
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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]
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## 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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## 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": []} | stvhuang/rcr-run-5pqr6lwp-90396-master-0_20240402T105012-ep27 | null | [
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T17:45:19+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #xlm-roberta #feature-extraction #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
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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
<!-- 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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### 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
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[More Information Needed]
## Training Details
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[More Information Needed]
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[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. -->
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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]
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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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[More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | Grayx/sad_llama_18.0 | null | [
"transformers",
"safetensors",
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"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]:
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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
#### 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
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[optional]
BibTeX:
APA:
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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
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| {"library_name": "transformers", "tags": []} | Enagamirzayev/whisper-small-llm-lingo-adapters_tt | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T17:49:09+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]:
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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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## Evaluation
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## Environmental Impact
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- Hardware Type:
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- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
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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: 0.9664
## 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: 3
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.0407 | 1.0 | 3334 | 0.9664 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "facebook/detr-resnet-50", "model-index": [{"name": "detr", "results": []}]} | Teele/detr | null | [
"transformers",
"tensorboard",
"safetensors",
"detr",
"object-detection",
"generated_from_trainer",
"base_model:facebook/detr-resnet-50",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T17:49:32+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: 0.9664
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: 3
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.38.2
* Pytorch 2.2.1+cu121
* Datasets 2.19.0
* Tokenizers 0.15.2
| [
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"### 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 | TigerBot-7B Swahili [LAPT + Heuristics (Untied)]
===
## How to use
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-heuristics-untied-sw"
)
tokenizer = AutoTokenizer.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-heuristics-untied-sw"
)
# w/ GPU
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-heuristics-untied-sw",
device_map="auto",
load_in_8bit=True,
)
```
## Citation
```
@article{yamaguchi2024empirical,
title={An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Generative {LLM} Inference},
author={Atsuki Yamaguchi and Aline Villavicencio and Nikolaos Aletras},
journal={ArXiv},
year={2024},
volume={abs/2402.10712},
url={https://arxiv.org/abs/2402.10712}
}
```
## Link
For more details, please visit https://github.com/gucci-j/llm-cva
| {"language": "sw", "license": "mit"} | atsuki-yamaguchi/tigerbot-7b-base-heuristics-untied-sw | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"sw",
"arxiv:2402.10712",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T17:50:06+00:00 | [
"2402.10712"
] | [
"sw"
] | TAGS
#transformers #safetensors #llama #text-generation #sw #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| TigerBot-7B Swahili [LAPT + Heuristics (Untied)]
===
## How to use
## Link
For more details, please visit URL
| [
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"## Link\nFor more details, please visit URL"
] |
text-classification | 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. -->
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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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.
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[More Information Needed]
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[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
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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]
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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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[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. -->
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[More Information Needed]
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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] | {"library_name": "transformers", "tags": []} | SkwarczynskiP/bert-base-uncased-finetuned-vedantgaur-AI-generated | null | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2024-04-21T17:50:30+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### 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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"#### 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"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# asr_wav2vec2_base_1.4
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2687
- Wer: 0.2410
- Cer: 0.0769
## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 4.4108 | 1.12 | 700 | 1.2046 | 0.7513 | 0.3423 |
| 0.9111 | 2.24 | 1400 | 0.4008 | 0.3362 | 0.1116 |
| 0.5359 | 3.36 | 2100 | 0.2947 | 0.2700 | 0.0857 |
| 0.397 | 4.48 | 2800 | 0.2687 | 0.2410 | 0.0769 |
### 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"], "metrics": ["wer"], "base_model": "facebook/wav2vec2-base", "model-index": [{"name": "asr_wav2vec2_base_1.4", "results": []}]} | Myriam123/asr_wav2vec2_base_1.4 | null | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T17:50:46+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-facebook/wav2vec2-base #license-apache-2.0 #endpoints_compatible #region-us
| asr\_wav2vec2\_base\_1.4
========================
This model is a fine-tuned version of facebook/wav2vec2-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2687
* Wer: 0.2410
* Cer: 0.0769
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: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 5
### Training results
### Framework versions
* Transformers 4.39.3
* Pytorch 2.1.2
* Datasets 2.18.0
* Tokenizers 0.15.2
| [
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] |
null | null |
# Orpo-GutenLlama-3-8B-v2-GGUF
[Original Repo](https://huggingface.co/macadeliccc/Orpo-GutenLlama-3-8B-v2) | {"license": "other", "datasets": ["mlabonne/orpo-dpo-mix-40k", "jondurbin/gutenberg-dpo-v0.1"], "license_name": "llama3", "license_link": "https://huggingface.co/meta-llama/Meta-Llama-3-8B/blob/main/LICENSE"} | macadeliccc/Orpo-GutenLlama-3-8B-v2-GGUF | null | [
"gguf",
"dataset:mlabonne/orpo-dpo-mix-40k",
"dataset:jondurbin/gutenberg-dpo-v0.1",
"license:other",
"region:us"
] | null | 2024-04-21T17:50:51+00:00 | [] | [] | TAGS
#gguf #dataset-mlabonne/orpo-dpo-mix-40k #dataset-jondurbin/gutenberg-dpo-v0.1 #license-other #region-us
|
# Orpo-GutenLlama-3-8B-v2-GGUF
Original Repo | [
"# Orpo-GutenLlama-3-8B-v2-GGUF\n\nOriginal Repo"
] | [
"TAGS\n#gguf #dataset-mlabonne/orpo-dpo-mix-40k #dataset-jondurbin/gutenberg-dpo-v0.1 #license-other #region-us \n",
"# Orpo-GutenLlama-3-8B-v2-GGUF\n\nOriginal Repo"
] |
null | adapter-transformers |
# Adapter `BigTMiami/n_par_bn_v_1_help_class_20_e_adp_lr_0003_S_4` 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/n_par_bn_v_1_help_class_20_e_adp_lr_0003_S_4", 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_MICRO_helpfulness_dataset"]} | BigTMiami/n_par_bn_v_1_help_class_20_e_adp_lr_0003_S_4 | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/amazon_MICRO_helpfulness_dataset",
"region:us"
] | null | 2024-04-21T17:51:23+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/amazon_MICRO_helpfulness_dataset #region-us
|
# Adapter 'BigTMiami/n_par_bn_v_1_help_class_20_e_adp_lr_0003_S_4' 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
| [
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"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] | [
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"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
token-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
<!-- 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": []} | KaggleMasterX/BERT_AllTasks | null | [
"transformers",
"safetensors",
"bert",
"token-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T17:52:59+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #bert #token-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
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"## Training Details",
"### Training Data",
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"## Technical Specifications [optional]",
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] | [
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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",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
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"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-generation | transformers | Mistral-7B German [LAPT + Heuristics (Untied)]
===
## How to use
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/Mistral-7B-v0.1-heuristics-untied-de"
)
tokenizer = AutoTokenizer.from_pretrained(
"atsuki-yamaguchi/Mistral-7B-v0.1-heuristics-untied-de"
)
# w/ GPU
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/Mistral-7B-v0.1-heuristics-untied-de",
device_map="auto",
load_in_8bit=True,
)
```
## Citation
```
@article{yamaguchi2024empirical,
title={An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Generative {LLM} Inference},
author={Atsuki Yamaguchi and Aline Villavicencio and Nikolaos Aletras},
journal={ArXiv},
year={2024},
volume={abs/2402.10712},
url={https://arxiv.org/abs/2402.10712}
}
```
## Link
For more details, please visit https://github.com/gucci-j/llm-cva
| {"language": "de", "license": "mit"} | atsuki-yamaguchi/Mistral-7B-v0.1-heuristics-untied-de | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"de",
"arxiv:2402.10712",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T17:55:59+00:00 | [
"2402.10712"
] | [
"de"
] | TAGS
#transformers #safetensors #mistral #text-generation #de #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Mistral-7B German [LAPT + Heuristics (Untied)]
===
## How to use
## Link
For more details, please visit URL
| [
"## How to use",
"## Link\nFor more details, please visit URL"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #de #arxiv-2402.10712 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## How to use",
"## Link\nFor more details, please visit URL"
] |
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]
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| {"library_name": "transformers", "tags": []} | arusso/tn_fine_tuned_text_classification_llama2_7b_children_s48_20240305_1_merged | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T17:58:09+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
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| [
"# Model Card for Model ID",
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] | [
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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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] |
automatic-speech-recognition | 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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| {"library_name": "transformers", "tags": []} | Enagamirzayev/whisper-small-llm-lingo_tt | null | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T17:58:36+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
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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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## Evaluation
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## Environmental Impact
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- Hardware Type:
- Hours used:
- Cloud Provider:
- 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
| [
"# 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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"#### Metrics",
"### Results",
"#### Summary",
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"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
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"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #whisper #automatic-speech-recognition #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]:",
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"### Training Data",
"### Training Procedure",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
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"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
reinforcement-learning | null |
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="cuckookernel/hf-drl-unit-2-taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "hf-drl-unit-2-taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/- 2.71", "name": "mean_reward", "verified": false}]}]}]} | cuckookernel/hf-drl-unit-2-taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2024-04-21T17:58:51+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing1 Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] |
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