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# DavidAU/LWM-Text-Chat-512K-Q8_0-GGUF
This model was converted to GGUF format from [`LargeWorldModel/LWM-Text-Chat-512K`](https://huggingface.co/LargeWorldModel/LWM-Text-Chat-512K) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/LargeWorldModel/LWM-Text-Chat-512K) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo DavidAU/LWM-Text-Chat-512K-Q8_0-GGUF --model lwm-text-chat-512k.Q8_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo DavidAU/LWM-Text-Chat-512K-Q8_0-GGUF --model lwm-text-chat-512k.Q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m lwm-text-chat-512k.Q8_0.gguf -n 128
```
| {"tags": ["llama-cpp", "gguf-my-repo"], "inference": false} | DavidAU/LWM-Text-Chat-512K-Q8_0-GGUF | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"region:us"
] | null | 2024-04-29T03:37:53+00:00 |
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]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[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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### 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]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | Nilesh360/llama-vid-7b-full-336 | null | [
"transformers",
"safetensors",
"llamavid",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T03:38:40+00:00 |
sentence-similarity | sentence-transformers |
# DivyaMereddy007/FewLayers_Finetuning_V1_TrainSetenceTransforme-Finetuning_COpyfromv5finetuneEPOC20
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('DivyaMereddy007/FewLayers_Finetuning_V1_TrainSetenceTransforme-Finetuning_COpyfromv5finetuneEPOC20')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('DivyaMereddy007/FewLayers_Finetuning_V1_TrainSetenceTransforme-Finetuning_COpyfromv5finetuneEPOC20')
model = AutoModel.from_pretrained('DivyaMereddy007/FewLayers_Finetuning_V1_TrainSetenceTransforme-Finetuning_COpyfromv5finetuneEPOC20')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=DivyaMereddy007/FewLayers_Finetuning_V1_TrainSetenceTransforme-Finetuning_COpyfromv5finetuneEPOC20)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 110 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 20,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 220.0,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | {"library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | DivyaMereddy007/FewLayers_Finetuning_V1_TrainSetenceTransforme-Finetuning_COpyfromv5finetuneEPOC20 | null | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T03:40:07+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[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
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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## Glossary [optional]
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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/ft788j9 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T03:41:02+00:00 |
null | null | {} | SachithRKA/ASL_AI_model | null | [
"region:us"
] | null | 2024-04-29T03:41:30+00:00 |
|
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. -->
# GIT_inf_w_caption_blur_ep5
This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.38.1
- Pytorch 2.2.1+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/git-base", "model-index": [{"name": "GIT_inf_w_caption_blur_ep5", "results": []}]} | vishwa27/GIT_inf_w_caption_blur_ep5 | null | [
"transformers",
"safetensors",
"git",
"text-generation",
"generated_from_trainer",
"base_model:microsoft/git-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T03:45:43+00:00 |
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]
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- **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": []} | golf2248/09m6mmy | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T03:48:56+00:00 |
null | null | {} | wniu/ps_20240419_112256 | null | [
"region:us"
] | null | 2024-04-29T03:49:07+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/pmxyqjx | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T03:49:16+00:00 |
null | null | {} | real-donald-trump/5242-models | null | [
"region:us"
] | null | 2024-04-29T03:50:49+00:00 |
|
text2text-generation | transformers | {} | Mishamq/opus-mt-en-ro-finetuned-en-to-ro | null | [
"transformers",
"tensorboard",
"safetensors",
"marian",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T03:54:06+00:00 |
|
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
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[More Information Needed]
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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
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[More Information Needed]
## Training Details
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | HenryCai1129/adapter-llama-adapterhappy2sad-2k-search-50-0.003 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T03:55:39+00:00 |
text-to-image | diffusers |
# Sub-path Linear Approximation Model (SLAM): SD1.5
Paper: [https://arxiv.org/abs/2404.13903](https://arxiv.org/abs/2404.13903)</br>
Project Page: [https://subpath-linear-approx-model.github.io/](https://subpath-linear-approx-model.github.io/)</br>
The checkpoint is a distilled from [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) with our proposed Sub-path Linear Approximation Model, which reduces the number of inference steps to only between 2-4 steps.
## Usage
First, install the latest version of the Diffusers library as well as peft, accelerate and transformers.
```bash
pip install --upgrade pip
pip install --upgrade diffusers transformers accelerate peft
```
We implement SLAM to be compatible with [LCMScheduler](https://huggingface.co/docs/diffusers/v0.22.3/en/api/schedulers/lcm#diffusers.LCMScheduler). You can use SLAM just like you use LCM, with guidance_scale set to 1 constantly.
```python
from diffusers import DiffusionPipeline
import torch
pipe = DiffusionPipeline.from_pretrained("alimama-creative/slam-sd1.5")
# To save GPU memory, torch.float16 can be used, but it may compromise image quality.
pipe.to(torch_device="cuda", torch_dtype=torch.float16)
prompt = "a painting of a majestic kingdom with towering castles, lush gardens, ice and snow world"
num_inference_steps = 2
images = pipe(prompt=prompt, num_inference_steps=num_inference_steps, guidance_scale=1, lcm_origin_steps=50, output_type="pil").images
```
 | {"license": "apache-2.0", "library_name": "diffusers", "tags": ["text-to-image"], "inference": false} | alimama-creative/slam-sd1.5 | null | [
"diffusers",
"text-to-image",
"arxiv:2404.13903",
"license:apache-2.0",
"region:us"
] | null | 2024-04-29T03:55:51+00:00 |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# job_postings_mlm_model_500k
This model is a fine-tuned version of [giyoung-kwon-0902/job_postings_mlm_model_450k](https://huggingface.co/giyoung-kwon-0902/job_postings_mlm_model_450k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1135
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.1519 | 1.0 | 14307 | 0.1363 |
| 0.1242 | 2.0 | 28614 | 0.1135 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "giyoung-kwon-0902/job_postings_mlm_model_450k", "model-index": [{"name": "job_postings_mlm_model_500k", "results": []}]} | giyoung-kwon-0902/job_postings_mlm_model_500k | null | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:giyoung-kwon-0902/job_postings_mlm_model_450k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T03:56:07+00:00 |
fill-mask | transformers |
## SPLADE-v3-Lexical
SPLADE-v3-Lexical is the SPLADE-Lexical version of `naver/splade-v3` (no expansion on the query side - only term weighting).
For more details, see our arXiv companion book: https://arxiv.org/abs/2403.06789
To use SPLADE, please visit our GitHub repository: https://github.com/naver/splade
## Performance
| | MRR@10 (MS MARCO dev) | avg nDCG@10 (BEIR-13) |
| --- | --- | --- |
| `naver/splade-v3-lexical` | 40.0 | 49.1 |
## Citation
If you use our checkpoint, please cite our work:
```
@misc{lassance2024spladev3,
title={SPLADE-v3: New baselines for SPLADE},
author={Carlos Lassance and Hervé Déjean and Thibault Formal and Stéphane Clinchant},
year={2024},
eprint={2403.06789},
archivePrefix={arXiv},
primaryClass={cs.IR},
copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}
``` | {"language": ["en"], "license": "cc-by-nc-sa-4.0", "tags": ["splade"]} | nirantk/splade-v3-lexical | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"splade",
"en",
"arxiv:2403.06789",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T03:59:50+00:00 |
null | null | {} | raptorkwok/cantonese-chinese-translation-pycantonese-v19 | null | [
"region:us"
] | null | 2024-04-29T04:00:32+00:00 |
|
null | null | {} | gurman-02/fairy-talks | null | [
"region:us"
] | null | 2024-04-29T04:02:05+00:00 |
|
null | transformers |
# Uploaded model
- **Developed by:** dmorrigan
- **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"} | dmorrigan/HebrewLyricsLoRA-23K-4Epoch | 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 | 2024-04-29T04:02:13+00:00 |
text-to-image | diffusers | # Sub-path Linear Approximation Model (SLAM): DreamShaperV7
Paper: [https://arxiv.org/abs/2404.13903](https://arxiv.org/abs/2404.13903)<br/>
Project Page: [https://subpath-linear-approx-model.github.io/](https://subpath-linear-approx-model.github.io/)<br/>
The checkpoint is a distilled from [https://huggingface.co/Lykon/dreamshaper-7](https://huggingface.co/Lykon/dreamshaper-7) with our proposed Sub-path Linear Approximation Model, which reduces the number of inference steps to only between 2-4 steps.
## Usage
First, install the latest version of the Diffusers library as well as peft, accelerate and transformers.
```bash
pip install --upgrade pip
pip install --upgrade diffusers transformers accelerate peft
```
We implement SLAM to be compatible with [LCMScheduler](https://huggingface.co/docs/diffusers/v0.22.3/en/api/schedulers/lcm#diffusers.LCMScheduler). You can use SLAM just like you use LCM, with guidance_scale set to 1 constantly.
```python
from diffusers import DiffusionPipeline
import torch
pipe = DiffusionPipeline.from_pretrained("alimama-creative/slam-dreamshaper7")
# To save GPU memory, torch.float16 can be used, but it may compromise image quality.
pipe.to(torch_device="cuda", torch_dtype=torch.float16)
prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k"
num_inference_steps = 4
images = pipe(prompt=prompt, num_inference_steps=num_inference_steps, guidance_scale=1, lcm_origin_steps=50, output_type="pil").images
```
| {"license": "apache-2.0", "library_name": "diffusers", "tags": ["text-to-image"], "inference": false} | alimama-creative/slam-dreamshaper7 | null | [
"diffusers",
"text-to-image",
"arxiv:2404.13903",
"license:apache-2.0",
"region:us"
] | null | 2024-04-29T04:02:50+00:00 |
sentence-similarity | sentence-transformers |
# DivyaMereddy007/FewLayers_Finetuning_V1_TrainSetenceTransforme-Finetuning_COpyfromv5finetuneEPOC10
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('DivyaMereddy007/FewLayers_Finetuning_V1_TrainSetenceTransforme-Finetuning_COpyfromv5finetuneEPOC10')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('DivyaMereddy007/FewLayers_Finetuning_V1_TrainSetenceTransforme-Finetuning_COpyfromv5finetuneEPOC10')
model = AutoModel.from_pretrained('DivyaMereddy007/FewLayers_Finetuning_V1_TrainSetenceTransforme-Finetuning_COpyfromv5finetuneEPOC10')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=DivyaMereddy007/FewLayers_Finetuning_V1_TrainSetenceTransforme-Finetuning_COpyfromv5finetuneEPOC10)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 55 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 55.0,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | {"library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | DivyaMereddy007/FewLayers_Finetuning_V1_TrainSetenceTransforme-Finetuning_COpyfromv5finetuneEPOC10 | null | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T04:02:53+00:00 |
null | null | {} | xu3kev/pbelora | null | [
"region:us"
] | null | 2024-04-29T04:04:30+00:00 |
|
null | null | {} | xu3kev/pbe-lora | null | [
"region:us"
] | null | 2024-04-29T04:04:47+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | golf2248/6tcc12y | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T04:07:19+00:00 |
text-to-image | diffusers | # Sub-path Linear Approximation Model (SLAM) LoRA: SDXL
Paper: [https://arxiv.org/abs/2404.13903](https://arxiv.org/abs/2404.13903)<br/>
Project Page: [https://subpath-linear-approx-model.github.io/](https://subpath-linear-approx-model.github.io/)<br/>
The checkpoint is a distilled from [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) with our proposed Sub-path Linear Approximation Model, which reduces the number of inference steps to only between 2-4 steps.
## Usage
First, install the latest version of the Diffusers library as well as peft, accelerate and transformers.
```bash
pip install --upgrade pip
pip install --upgrade diffusers transformers accelerate peft
```
We implement SLAM to be compatible with [LCMScheduler](https://huggingface.co/docs/diffusers/v0.22.3/en/api/schedulers/lcm#diffusers.LCMScheduler). You can use SLAM-LoRA just like you use LCM-LoRA.
```python
import torch
from diffusers import LCMScheduler, AutoPipelineForText2Image
model_id = "stabilityai/stable-diffusion-xl-base-1.0"
adapter_id = "alimama-creative/slam-lora-sdxl"
pipe = AutoPipelineForText2Image.from_pretrained(model_id, torch_dtype=torch.float16, variant="fp16")
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe.to("cuda")
# load and fuse lcm lora
pipe.load_lora_weights(adapter_id)
pipe.fuse_lora()
prompt = "A brown teddy bear holding a glass vase in front of a grave."
image = pipe(prompt=prompt, num_inference_steps=4, guidance_scale=1.0).images[0]
```
| {"license": "apache-2.0", "library_name": "diffusers", "tags": ["text-to-image"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "inference": false} | alimama-creative/slam-lora-sdxl | null | [
"diffusers",
"text-to-image",
"arxiv:2404.13903",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:apache-2.0",
"region:us"
] | null | 2024-04-29T04:07:28+00:00 |
null | null | {"license": "apache-2.0"} | Roselia-penguin/medical_qwen_1.8b | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-04-29T04:07:49+00:00 |
|
image-text-to-text | transformers | ### TinyLLaVA
We trained 1 model with fewer than 1B parameters using the TinyLLaVA approach, employing the same training settings as [TinyLLaVA](https://github.com/DLCV-BUAA/TinyLLaVABench). For the Language and Vision models, we chose [OpenELM-450M-Instruct](apple/OpenELM-450M-Instruct) and [siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384), respectively. The Connector was configured with a 2-layer MLP. The dataset used for training is the save as [LLaVA](https://github.com/haotian-liu/LLaVA). During testing, we found that [TinyLLaVA-0.55B](https://huggingface.co/jiajunlong/TinyLLaVA-0.55B) exhibited significantly faster inference speed on CPU compared to [TinyLLaVA-1.5B](https://huggingface.co/bczhou/TinyLLaVA-1.5B)
### Usage
1. you need to download the generate file "generate_model.py".
2. running the following command:
```bash
python generate_model --model jiajunlong/TinyLLaVA-0.89B --prompt 'you want to ask' --image '/path/to/related/image'
```
or execute the following test code:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from generate_model import *
model = AutoModelForCausalLM.from_pretrained("jiajunlong/TinyLLaVA-0.55B", trust_remote_code=True)
config = model.config
tokenizer = AutoTokenizer.from_pretrained("jiajunlong/TinyLLaVA-0.55B", use_fast=False, model_max_length = config.tokenizer_model_max_length,padding_side = config.tokenizer_padding_side)
prompt="you want to ask"
image="/path/to/related/image"
output_text, genertaion_time = generate(prompt=prompt, image=image, model=model, tokenizer=tokenizer)
print_txt = (
f'\r\n{"=" * os.get_terminal_size().columns}\r\n'
'\033[1m Prompt + Generated Output\033[0m\r\n'
f'{"-" * os.get_terminal_size().columns}\r\n'
f'{output_text}\r\n'
f'{"-" * os.get_terminal_size().columns}\r\n'
'\r\nGeneration took'
f'\033[1m\033[92m {round(genertaion_time, 2)} \033[0m'
'seconds.\r\n'
)
print(print_txt)
```
### Result
| model_name | gqa | textvqa | sqa | vqav2 | MME | MMB | MM-VET |
| :----------------------------------------------------------: | ----- | ------- | ----- | ----- | ------- | ----- | ------ |
| [TinyLLaVA-1.5B](https://huggingface.co/bczhou/TinyLLaVA-1.5B) | 60.3 | 51.7 | 60.3 | 76.9 | 1276.5 | 55.2 | 25.8 |
| [TinyLLaVA-0.55B](https://huggingface.co/jiajunlong/TinyLLaVA-0.89B) | 53.87 | 44.02 | 54.09 | 71.74 | 1118.75 | 37.8 | 20 |
| {"license": "apache-2.0", "pipeline_tag": "image-text-to-text"} | jiajunlong/TinyLLaVA-0.89B | null | [
"transformers",
"safetensors",
"tinyllava",
"text-generation",
"image-text-to-text",
"custom_code",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | null | 2024-04-29T04:09:45+00:00 |
null | null |
# T3qm7xpShadowm7exp-7B
T3qm7xpShadowm7exp-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration.
## 🧩 Configuration
```yaml
models:
- model: mistralai/Mistral-7B-v0.1
- model: nlpguy/T3QM7XP
- model: mahiatlinux/ShadowM7EXP-7B
merge_method: model_stock
base_model: mistralai/Mistral-7B-v0.1
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "automerger/T3qm7xpShadowm7exp-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "automerger"]} | automerger/T3qm7xpShadowm7exp-7B | null | [
"merge",
"mergekit",
"lazymergekit",
"automerger",
"license:apache-2.0",
"region:us"
] | null | 2024-04-29T04:10:23+00:00 |
text-generation | transformers | The ai-forever/rugpt3large_based_on_gpt2 based model was fine tuned for Question-Answer tasks in Russian.
Версия: датасет 60тыс. строк, 1-ая эпоха. В дальнейшем будут появлятся другие модели.
Качество ответа: среднее
Формат запроса: `<s> [user] Запрос (пока нормально отвечает только на вопросы) [assistant] ... </s>`
Пример использования:
```
from transformers import GPT2Tokenizer, GPT2LMHeadModel
model = GPT2LMHeadModel.from_pretrained("ERmak1581/rugpt3large_for_qna_60k1")
tokenizer = GPT2Tokenizer.from_pretrained("ERmak1581/rugpt3large_for_qna_60k1")
print(tokenizer.decode(model.generate(
tokenizer.encode('<s> [user] Почему небо синее? [assistant]',
return_tensors="pt"),
max_new_tokens=100, no_repeat_ngram_size=2, temperature=0.7, do_sample=True)[0]))
``` | {"language": ["ru"], "license": "mit", "library_name": "transformers", "pipeline_tag": "text-generation"} | ERmak1581/rugpt3large_for_qna_60k1 | null | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"ru",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T04:11:15+00:00 |
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="Paoja/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}]}]}]} | Paoja/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2024-04-29T04:11:17+00:00 |
feature-extraction | transformers |
# Model Card
## Model Details
- Architecture: ViT-Large with patch size 14
- Training Data: DTD dataset
## Training Details
Adam Optimizer with a constant learning rate 1e-5 for 4000 steps training (batch_size=32).
Only the vision encoder is fine-tuned.
## Evaluation Results
- pre-trained: 0.554787278175354
- fine-tuned: 0.8547872304916382
| {"datasets": ["tanganke/dtd"], "metrics": ["accuracy"], "base_model": ["openai/clip-vit-large-patch14"]} | tanganke/clip-vit-large-patch14_dtd | null | [
"transformers",
"safetensors",
"clip_vision_model",
"feature-extraction",
"dataset:tanganke/dtd",
"base_model:openai/clip-vit-large-patch14",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T04:11:41+00:00 |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: Blues-Monster/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy"]} | Blues-Monster/ppo-Huggy | null | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | null | 2024-04-29T04:12:32+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | harir/phi-3-mini-review-toxicity | null | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"has_space"
] | null | 2024-04-29T04:12:59+00:00 |
null | null | {"license": "apache-2.0"} | Theon1130/failed_lr_vqarad_result | null | [
"safetensors",
"license:apache-2.0",
"region:us"
] | null | 2024-04-29T04:13:20+00:00 |
|
feature-extraction | transformers |
## Overview
This is a bare model without any output layer or classification head. It has been quantized to be used for feature extraction tasks.
**Usage**
This model is intended to be used as a base for training on downstream tasks. In order to use it for predictions and inference, it should be fine-tuned on a specific task with an appropriate output layer or classification head added.
**Quantization**
The model has been quantized using the following parameters:
Lora alpha: 16
Lora rank: 32
Lora target modules: all-linear
bits: 4
LoftQ iterations: 5 | {"pipeline_tag": "feature-extraction"} | smallsuper/Mistral-7B-v0.1-4bit-32rank | null | [
"transformers",
"safetensors",
"mistral",
"feature-extraction",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T04:13:33+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1393
- F1: 0.8696
## 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: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2592 | 1.0 | 525 | 0.1507 | 0.8269 |
| 0.1253 | 2.0 | 1050 | 0.1413 | 0.8550 |
| 0.0793 | 3.0 | 1575 | 0.1393 | 0.8696 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "base_model": "xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": []}]} | u00890358/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T04:13:41+00:00 |
null | null |
# DavidAU/LWM-Text-Chat-1M-Q8_0-GGUF
This model was converted to GGUF format from [`LargeWorldModel/LWM-Text-Chat-1M`](https://huggingface.co/LargeWorldModel/LWM-Text-Chat-1M) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/LargeWorldModel/LWM-Text-Chat-1M) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo DavidAU/LWM-Text-Chat-1M-Q8_0-GGUF --model lwm-text-chat-1m.Q8_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo DavidAU/LWM-Text-Chat-1M-Q8_0-GGUF --model lwm-text-chat-1m.Q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m lwm-text-chat-1m.Q8_0.gguf -n 128
```
| {"tags": ["llama-cpp", "gguf-my-repo"], "inference": false} | DavidAU/LWM-Text-Chat-1M-Q8_0-GGUF | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"region:us"
] | null | 2024-04-29T04:14:18+00:00 |
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).
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| {"library_name": "transformers", "tags": []} | ramprasadsoren7061/ol_chiki_tokenizer | null | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T04:14:20+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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[More Information Needed] | {"library_name": "transformers", "tags": []} | charlieoneill/llama3-8b-hypogen | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T04:15:00+00:00 |
text-to-image | diffusers |
# EcomXL Inpaint ControlNet
EcomXL contains a series of text-to-image diffusion models optimized for e-commerce scenarios, developed based on [Stable Diffusion XL](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0).<br/>
For e-commerce scenarios, we trained Inpaint ControlNet to control diffusion models.
Unlike the inpaint controlnets used for general scenarios, this model is fine-tuned with instance masks to prevent foreground outpainting.
## Examples
<span style="width: 150px !important;display: inline-block;">`Foreground`<span> | <span style="width: 150px !important;display: inline-block;">`Mask`<span> | <span style="width: 150px !important;display: inline-block;">`w/o instance mask`<span> | <span style="width: 150px !important;display: inline-block;">`w/ instance mask`<span>
:--:|:--:|:--:|:--:
 |  |  | 
<!-- <img src="https://huggingface.co/alimama-creative/EcomXL/resolve/main/images/inp_0.png" width="300"/> | <img src="https://huggingface.co/alimama-creative/EcomXL/resolve/main/images/inp_1.png" width="300"/> | <img src="https://huggingface.co/alimama-creative/EcomXL/resolve/main/images/inp_2.png" width="300"/> | <img src="https://huggingface.co/alimama-creative/EcomXL/resolve/main/images/inp_3.png" width="300"/> -->
Using this ControlNet with a control weight of 0.5 may achieve better results.
## Training details
In the first phase, the model was trained on 12M laion2B and internal source images with random masks for 20k steps. In the second phase, the model was trained on 3M e-commerce images with the instance mask for 20k steps.<br>
Mixed precision: FP16<br>
Learning rate: 1e-4<br>
batch size: 2048<br>
Noise offset: 0.05 | {"language": ["en"], "license": "apache-2.0", "tags": ["stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "controlnet"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "inference": false, "pipeline_tag": "text-to-image"} | alimama-creative/EcomXL_controlnet_inpaint | null | [
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"controlnet",
"en",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:apache-2.0",
"region:us"
] | null | 2024-04-29T04:15:28+00:00 |
text-to-image | diffusers |
### Softedge ControlNet
EcomXL contains a series of text-to-image diffusion models optimized for e-commerce scenarios, developed based on [Stable Diffusion XL](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0).<br/>
The controlnet weights are fine-tuned based on stable-diffusion-xl-base-1.0.
It works well on SDXL as well as community models based on SDXL.
The model is trained on general data and e-commerce data, and has good capabilities in both general and e-commerce scenarios.
#### Examples
<span style="width: 150px !important;display: inline-block;">`Edge`<span> | <span style="width: 150px !important;display: inline-block;">`Output`<span> | <span style="width: 150px !important;display: inline-block;">`Output`<span> | <span style="width: 150px !important;display: inline-block;">`Output`<span>
:--:|:--:|:--:|:--:
 |  |  | 
<!-- <img src="https://huggingface.co/alimama-creative/EcomXL/resolve/main/images/edge_0.png" width="300"/> | <img src="https://huggingface.co/alimama-creative/EcomXL/resolve/main/images/edge_1.png" width="300"/> | <img src="https://huggingface.co/alimama-creative/EcomXL/resolve/main/images/edge_2.png" width="300"/> | <img src="https://huggingface.co/alimama-creative/EcomXL/resolve/main/images/edge_3.png" width="300"/> -->
#### Training details
The model is trained for 37k steps. The training data includes 12M laion2B images and internal sources images, as well as 3M e-commerce images. During training, the softedge preprocessor is randomly selected from pidinet, hed, pidisafe and hedsafe, which are officially supported by Automatic&&Mikubill. <br>
Mixed precision: FP16<br>
Learning rate: 1e-5<br>
batch size: 1024<br>
Noise offset: 0.05 | {"language": ["en"], "license": "apache-2.0", "tags": ["stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "controlnet"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "inference": false, "pipeline_tag": "text-to-image"} | alimama-creative/EcomXL_controlnet_softedge | null | [
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"controlnet",
"en",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:apache-2.0",
"region:us"
] | null | 2024-04-29T04:15:47+00:00 |
text-classification | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | MLGuy2/Team7 | null | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T04:16:05+00:00 |
text-generation | 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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## How to Get Started with the Model
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[More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/e3a99ur | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T04:16:09+00:00 |
null | null | {"license": "apache-2.0"} | sriraj123/Weather_Prediction | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-04-29T04:16:14+00:00 |
|
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="Paoja/Taxiv3", 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": "Taxiv3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.54 +/- 2.73", "name": "mean_reward", "verified": false}]}]}]} | Paoja/Taxiv3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2024-04-29T04:16:37+00:00 |
null | null |
# DavidAU/LWM-Text-1M-Q6_K-GGUF
This model was converted to GGUF format from [`LargeWorldModel/LWM-Text-1M`](https://huggingface.co/LargeWorldModel/LWM-Text-1M) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/LargeWorldModel/LWM-Text-1M) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo DavidAU/LWM-Text-1M-Q6_K-GGUF --model lwm-text-1m.Q6_K.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo DavidAU/LWM-Text-1M-Q6_K-GGUF --model lwm-text-1m.Q6_K.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m lwm-text-1m.Q6_K.gguf -n 128
```
| {"tags": ["llama-cpp", "gguf-my-repo"], "inference": false} | DavidAU/LWM-Text-1M-Q6_K-GGUF | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"region:us"
] | null | 2024-04-29T04:19:00+00:00 |
text-generation | transformers |
## FinguAI-Chat-Mid-v1
### Overview
The FinguAI-Chat-Mid-v1 model offers a specialized curriculum tailored to English, Korean, and Japanese speakers interested in finance, investment, and legal frameworks.
It aims to enhance language proficiency while providing insights into global finance markets and regulatory landscapes.
### Key Features
- **Global Perspective**: Explores diverse financial markets and regulations across English, Korean, and Japanese contexts.
- **Language Proficiency**: Enhances language skills in English, Korean, and Japanese for effective communication in finance and legal domains.
- **Career Advancement**: Equips learners with knowledge and skills for roles in investment banking, corporate finance, asset management, and regulatory compliance.
### Model Information
- **Model Name**: FinguAI-Chat-Mid-v1
- **Checkpoint**: FinguAI-Chat-Mid-v1
- **Author**: Grinda AI Inc.
- **License**: Apache-2.0
### Training Details
- **Fine-Tuning**: The model was fine-tuned on the base model [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct)
- through ORPO fine-tuning using the TrL Library and Transformer.
- **Dataset**: The fine-tuning dataset consisted of 28178 training samples.
### How to Use
To use the FinguAI-Chat-Mid-v1 model, you can utilize the Hugging Face Transformers library.
Here's a Python code snippet demonstrating how to load the model and generate predictions:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig,TextStreamer
model_id = 'FinguAI-Chat-Mid-v1'
model = AutoModelForCausalLM.from_pretrained(model_id, attn_implementation="flash_attention_2", torch_dtype= torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained(model_id)
streamer = TextStreamer(tokenizer)
model.to('cuda')
messages = [
{"role": "system","content": " you are as a finance specialist, help the user and provide accurat information."},
{"role": "user", "content": " what are the best approch to prevent loss?"},
]
tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
generation_params = {
'max_new_tokens': 1000,
'use_cache': True,
'do_sample': True,
'temperature': 0.7,
'top_p': 0.9,
'top_k': 50,
'eos_token_id': tokenizer.eos_token_id,
}
outputs = model.generate(tokenized_chat, **generation_params, streamer=streamer)
decoded_outputs = tokenizer.batch_decode(outputs)
``` | {"license": "mit", "tags": ["trl", "orpo", "generated_from_trainer"], "base_model": "microsoft/Phi-3-mini-4k-instruct", "model-index": [{"name": "FinguAI-Chat-Mid-v1", "results": []}]} | FINGU-AI/FinguAI-Chat-Mid-v1 | null | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"trl",
"orpo",
"generated_from_trainer",
"conversational",
"custom_code",
"base_model:microsoft/Phi-3-mini-4k-instruct",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"has_space"
] | null | 2024-04-29T04:19:05+00:00 |
null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# G0428HMA16
This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1057
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6372 | 0.09 | 10 | 1.7096 |
| 1.1238 | 0.18 | 20 | 0.4795 |
| 0.2595 | 0.27 | 30 | 0.1668 |
| 0.155 | 0.36 | 40 | 0.1603 |
| 0.1489 | 0.45 | 50 | 0.1483 |
| 0.1474 | 0.54 | 60 | 0.1499 |
| 0.1479 | 0.63 | 70 | 0.1470 |
| 0.1491 | 0.73 | 80 | 0.1479 |
| 0.1413 | 0.82 | 90 | 0.1486 |
| 0.1448 | 0.91 | 100 | 0.1479 |
| 0.1492 | 1.0 | 110 | 0.1488 |
| 0.1429 | 1.09 | 120 | 0.1485 |
| 0.1447 | 1.18 | 130 | 0.1485 |
| 0.146 | 1.27 | 140 | 0.1473 |
| 0.1478 | 1.36 | 150 | 0.1466 |
| 0.1423 | 1.45 | 160 | 0.1507 |
| 0.1434 | 1.54 | 170 | 0.1435 |
| 0.1392 | 1.63 | 180 | 0.1377 |
| 0.1379 | 1.72 | 190 | 0.1359 |
| 0.1285 | 1.81 | 200 | 0.1294 |
| 0.1271 | 1.9 | 210 | 0.1303 |
| 0.1269 | 1.99 | 220 | 0.1228 |
| 0.1118 | 2.08 | 230 | 0.1210 |
| 0.1144 | 2.18 | 240 | 0.1153 |
| 0.1106 | 2.27 | 250 | 0.1123 |
| 0.1116 | 2.36 | 260 | 0.1155 |
| 0.1158 | 2.45 | 270 | 0.1118 |
| 0.1066 | 2.54 | 280 | 0.1109 |
| 0.0991 | 2.63 | 290 | 0.1098 |
| 0.1016 | 2.72 | 300 | 0.1064 |
| 0.1029 | 2.81 | 310 | 0.1058 |
| 0.1052 | 2.9 | 320 | 0.1057 |
| 0.106 | 2.99 | 330 | 0.1057 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "gemma", "tags": ["generated_from_trainer"], "base_model": "google/gemma-2b", "model-index": [{"name": "G0428HMA16", "results": []}]} | Litzy619/G0428HMA16 | null | [
"safetensors",
"generated_from_trainer",
"base_model:google/gemma-2b",
"license:gemma",
"region:us"
] | null | 2024-04-29T04:19:11+00:00 |
null | null |
# DavidAU/LWM-Text-512K-Q8_0-GGUF
This model was converted to GGUF format from [`LargeWorldModel/LWM-Text-512K`](https://huggingface.co/LargeWorldModel/LWM-Text-512K) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/LargeWorldModel/LWM-Text-512K) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo DavidAU/LWM-Text-512K-Q8_0-GGUF --model lwm-text-512k.Q8_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo DavidAU/LWM-Text-512K-Q8_0-GGUF --model lwm-text-512k.Q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m lwm-text-512k.Q8_0.gguf -n 128
```
| {"tags": ["llama-cpp", "gguf-my-repo"], "inference": false} | DavidAU/LWM-Text-512K-Q8_0-GGUF | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"region:us"
] | null | 2024-04-29T04:19:42+00:00 |
text-generation | null |
## Exllama v2 Quantizations of dolphin-2.9-llama3-8b-256k
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.20">turboderp's ExLlamaV2 v0.0.20</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/cognitivecomputations/dolphin-2.9-llama3-8b-256k
## 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/dolphin-2.9-llama3-8b-256k-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/dolphin-2.9-llama3-8b-256k-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/dolphin-2.9-llama3-8b-256k-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/dolphin-2.9-llama3-8b-256k-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/dolphin-2.9-llama3-8b-256k-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/dolphin-2.9-llama3-8b-256k-exl2 dolphin-2.9-llama3-8b-256k-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/dolphin-2.9-llama3-8b-256k-exl2 --revision 6_5 --local-dir dolphin-2.9-llama3-8b-256k-exl2-6_5 --local-dir-use-symlinks False
```
Windows (which apparently doesn't like _ in folders sometimes?):
```shell
huggingface-cli download bartowski/dolphin-2.9-llama3-8b-256k-exl2 --revision 6_5 --local-dir dolphin-2.9-llama3-8b-256k-exl2-6.5 --local-dir-use-symlinks False
```
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
| {"license": "llama3", "quantized_by": "bartowski", "pipeline_tag": "text-generation"} | bartowski/dolphin-2.9-llama3-8b-256k-exl2 | null | [
"text-generation",
"license:llama3",
"region:us"
] | null | 2024-04-29T04:21:03+00:00 |
automatic-speech-recognition | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | spsither/mms_300_v2.1190 | null | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T04:21:30+00:00 |
null | null | {} | Soham1508/jonbg | null | [
"region:us"
] | null | 2024-04-29T04:21:48+00:00 |
|
null | null | {} | Soham1508/jobg | null | [
"region:us"
] | null | 2024-04-29T04:21:59+00:00 |
|
text-generation | transformers |
## HOW TO Use frankmorales2020/torchtune-Llama-2-7b
ARTICLE: https://medium.com/ai-in-plain-english/torchtune-simplifying-llm-fine-tuning-8811d2bb25a5
# STEP1
```python
!pip install torchtune -q
```
# STEP2
```python
!tune -h
```
```
usage: tune [-h] {download,ls,cp,run,validate} ...
Welcome to the TorchTune CLI!
options:
-h, --help show this help message and exit
subcommands:
{download,ls,cp,run,validate}
download Download a model from the Hugging Face Hub.
ls List all built-in recipes and configs
cp Copy a built-in recipe or config to a local path.
run Run a recipe. For distributed recipes, this supports all torchrun
arguments.
validate Validate a config and ensure that it is well-formed.
```
# STEP3
```python
!tune download frankmorales2020/torchtune-Llama-2-7b --output-dir /tmp/Llama-2-7b-hf
```
# STEP4
```python
!tune cp generation /content/custom_generation_config.yaml
```
# STEP5
```python
!tune run generate --config /content/custom_generation_config.yaml prompt="What are some interesting sites to visit in the Bay Area?"
```
```
INFO:torchtune.utils.logging:Running InferenceRecipe with resolved config:
checkpointer:
_component_: torchtune.utils.FullModelHFCheckpointer
checkpoint_dir: /tmp/Llama-2-7b-hf/
checkpoint_files:
- hf_model_0001_0.pt
- hf_model_0002_0.pt
model_type: LLAMA2
output_dir: /tmp/Llama-2-7b-hf/
device: cuda
dtype: bf16
max_new_tokens: 300
model:
_component_: torchtune.models.llama2.llama2_7b
prompt: What are some interesting sites to visit in the Bay Area?
quantizer: null
seed: 1234
temperature: 0.6
tokenizer:
_component_: torchtune.models.llama2.llama2_tokenizer
path: /tmp/Llama-2-7b-hf/tokenizer.model
top_k: 300
DEBUG:torchtune.utils.logging:Setting manual seed to local seed 1234. Local seed is seed + rank = 1234 + 0
INFO:torchtune.utils.logging:Model is initialized with precision torch.bfloat16.
INFO:torchtune.utils.logging: What are some interesting sites to visit in the Bay Area?
What are some interesting sites to visit in the Bay Area?
The Bay Area is home to many interesting sites, from the iconic Golden Gate Bridge to the quirky Alcatraz Island. Here are some of the most interesting sites to visit in the Bay Area:
Golden Gate Bridge: This suspension bridge is one of the most recognizable landmarks in the world. It spans the Golden Gate Strait, connecting San Francisco to Marin County. Visitors can take a walk or bike ride across the bridge and enjoy the stunning views of the bay.
Alcatraz Island: This former prison is now a popular tourist attraction. Visitors can take a ferry to the island and explore the cell blocks, hospital, and other buildings. There is also a museum on the island, which tells the history of the prison and its most famous inmates.
Coit Tower: This 210-foot tower is located in the Telegraph Hill neighborhood of San Francisco. It offers panoramic views of the city and the bay. Visitors can take an elevator to the top of the tower and enjoy the views.
Sutro Baths: These ruins are located in the Lands End area of San Francisco. The baths were a popular swimming and spa destination in the late 19th century, but closed in 1966. Today, visitors can explore the ru
INFO:torchtune.utils.logging:Time for inference: 20.15 sec total, 14.89 tokens/sec
INFO:torchtune.utils.logging:Bandwidth achieved: 233.66 GB/s
INFO:torchtune.utils.logging:Memory used: 15.72 GB
```
# **Llama 2**
Llama 2 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 7B pretrained model, converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.
## Model Details
*Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.*
Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM.
**Model Developers** Meta
**Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations.
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety.
||Training Data|Params|Content Length|GQA|Tokens|LR|
|---|---|---|---|---|---|---|
|Llama 2|*A new mix of publicly available online data*|7B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|13B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|70B|4k|✔|2.0T|1.5 x 10<sup>-4</sup>|
*Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Dates** Llama 2 was trained between January 2023 and July 2023.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
**Research Paper** ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288)
## Intended Use
**Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212).
**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 Llama 2.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program.
||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO<sub>2</sub>eq)|
|---|---|---|---|
|Llama 2 7B|184320|400|31.22|
|Llama 2 13B|368640|400|62.44|
|Llama 2 70B|1720320|400|291.42|
|Total|3311616||539.00|
**CO<sub>2</sub> emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023.
## Evaluation Results
In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library.
|Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval|
|---|---|---|---|---|---|---|---|---|---|
|Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|
|Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|
|Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|
|Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|
|Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|
|Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|
|Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|
**Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1.
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama 1|7B|27.42|23.00|
|Llama 1|13B|41.74|23.08|
|Llama 1|33B|44.19|22.57|
|Llama 1|65B|48.71|21.77|
|Llama 2|7B|33.29|**21.25**|
|Llama 2|13B|41.86|26.10|
|Llama 2|70B|**50.18**|24.60|
**Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better).
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama-2-Chat|7B|57.04|**0.00**|
|Llama-2-Chat|13B|62.18|**0.00**|
|Llama-2-Chat|70B|**64.14**|0.01|
**Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above.
## Ethical Considerations and Limitations
Llama 2 is 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, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide)
## Reporting Issues
Please report any software “bug,” or other problems with the models through one of the following means:
- Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
- Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
- Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
## Llama Model Index
|Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf|
|---|---|---|---|---|
|7B| [Link](https://huggingface.co/meta-llama/Llama-2-7b) | [Link](https://huggingface.co/meta-llama/Llama-2-7b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-7b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)|
|13B| [Link](https://huggingface.co/meta-llama/Llama-2-13b) | [Link](https://huggingface.co/meta-llama/Llama-2-13b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-13b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf)|
|70B| [Link](https://huggingface.co/meta-llama/Llama-2-70b) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf)| | {"language": ["en"], "license": "llama2", "tags": ["facebook", "meta", "pytorch", "llama", "llama-2"], "extra_gated_heading": "You need to share contact information with Meta to access this model", "extra_gated_prompt": "### LLAMA 2 COMMUNITY LICENSE AGREEMENT\n\"Agreement\" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. \n\"Documentation\" means the specifications, manuals and documentation accompanying Llama 2 distributed by Meta at https://ai.meta.com/resources/models-and-libraries/llama-downloads/. \n\"Licensee\" or \"you\" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity's behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. \n\"Llama 2\" means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at ai.meta.com/resources/models-and-libraries/llama-downloads/.\n\"Llama Materials\" means, collectively, Meta's proprietary Llama 2 and documentation (and any portion thereof) made available under this Agreement.\n\"Meta\" or \"we\" means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland). \n\nBy clicking \"I Accept\" below or by using or distributing any portion or element of the Llama Materials, you agree to be bound by this Agreement.\n1. License Rights and Redistribution. \na. Grant of Rights. You are granted a non-exclusive, worldwide, non- transferable and royalty-free limited license under Meta's intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials. \nb. Redistribution and Use.\ni. If you distribute or make the Llama Materials, or any derivative works thereof, available to a third party, you shall provide a copy of this Agreement to such third party. \nii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you. \niii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a \"Notice\" text file distributed as a part of such copies: \"Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved.\"\niv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at https://ai.meta.com/llama/use-policy), which is hereby incorporated by reference into this Agreement.\nv. You will not use the Llama Materials or any output or results of the Llama Materials to improve any other large language model (excluding Llama 2 or derivative works thereof). \n\n2. Additional Commercial Terms. If, on the Llama 2 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee's affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights.\n3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN \"AS IS\" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.\n4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING.\n\n5. Intellectual Property.\na. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials.\nb. Subject to Meta's ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications.\nc. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Llama 2 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials.\n6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement. \n7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement. \n### Llama 2 Acceptable Use Policy\nMeta is committed to promoting safe and fair use of its tools and features, including Llama 2. If you access or use Llama 2, you agree to this Acceptable Use Policy (\u201cPolicy\u201d). The most recent copy of this policy can be found at [ai.meta.com/llama/use-policy](http://ai.meta.com/llama/use-policy).\n#### Prohibited Uses\nWe want everyone to use Llama 2 safely and responsibly. You agree you will not use, or allow others to use, Llama 2 to:\n1. Violate the law or others\u2019 rights, including to:\n 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: \n 1. Violence or terrorism \n 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material\n 3. Human trafficking, exploitation, and sexual violence\n 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.\n 5. Sexual solicitation\n 6. Any other criminal activity\n 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals\n 3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services\n 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices \n 5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws\n 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials\n 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system \n2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 2 related to the following:\n 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State\n 2. Guns and illegal weapons (including weapon development)\n 3. Illegal drugs and regulated/controlled substances\n 4. Operation of critical infrastructure, transportation technologies, or heavy machinery\n 5. Self-harm or harm to others, including suicide, cutting, and eating disorders\n 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual\n3. Intentionally deceive or mislead others, including use of Llama 2 related to the following:\n 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation\n 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content\n 3. Generating, promoting, or further distributing spam\n 4. Impersonating another individual without consent, authorization, or legal right\n 5. Representing that the use of Llama 2 or outputs are human-generated\n 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement \n 4. Fail to appropriately disclose to end users any known dangers of your AI system \nPlease report any violation of this Policy, software \u201cbug,\u201d or other problems that could lead to a violation of this Policy through one of the following means: \n * Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)\n * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\n * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) \n * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama: [[email protected]](mailto:[email protected])", "extra_gated_fields": {"First Name": "text", "Last Name": "text", "Date of birth": "date_picker", "Country": "country", "Affiliation": "text", "geo": "ip_location", "By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy": "checkbox"}, "extra_gated_description": "The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).", "extra_gated_button_content": "Submit", "pipeline_tag": "text-generation"} | frankmorales2020/torchtune-Llama-2-7b | null | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"llama-2",
"en",
"arxiv:2307.09288",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T04:22:07+00:00 |
null | null | {"license": "openrail"} | frankmurray/impress | null | [
"license:openrail",
"region:us"
] | null | 2024-04-29T04:22:39+00:00 |
|
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": []} | aiqwe/gemma-2b-it-example-v1 | null | [
"transformers",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T04:23:43+00:00 |
null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# O0428B1
This model is a fine-tuned version of [allenai/OLMo-1B](https://huggingface.co/allenai/OLMo-1B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1466
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 60
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7159 | 0.09 | 10 | 1.9907 |
| 1.3938 | 0.18 | 20 | 0.5917 |
| 0.2804 | 0.27 | 30 | 0.1637 |
| 0.164 | 0.36 | 40 | 0.1546 |
| 0.1511 | 0.45 | 50 | 0.1503 |
| 0.1892 | 0.54 | 60 | 0.1511 |
| 0.1599 | 0.63 | 70 | 0.1479 |
| 0.1619 | 0.73 | 80 | 0.1522 |
| 0.1444 | 0.82 | 90 | 0.1492 |
| 0.1531 | 0.91 | 100 | 0.1480 |
| 0.1561 | 1.0 | 110 | 0.1506 |
| 0.1445 | 1.09 | 120 | 0.1471 |
| 0.1713 | 1.18 | 130 | 0.1497 |
| 0.1559 | 1.27 | 140 | 0.1478 |
| 0.1598 | 1.36 | 150 | 0.1466 |
| 0.143 | 1.45 | 160 | 0.1484 |
| 0.144 | 1.54 | 170 | 0.1462 |
| 0.1456 | 1.63 | 180 | 0.1464 |
| 0.1548 | 1.72 | 190 | 0.1495 |
| 0.1586 | 1.81 | 200 | 0.1473 |
| 0.1459 | 1.9 | 210 | 0.1471 |
| 0.1447 | 1.99 | 220 | 0.1486 |
| 0.1457 | 2.08 | 230 | 0.1464 |
| 0.1667 | 2.18 | 240 | 0.1467 |
| 0.1431 | 2.27 | 250 | 0.1466 |
| 0.1444 | 2.36 | 260 | 0.1474 |
| 0.1507 | 2.45 | 270 | 0.1473 |
| 0.1514 | 2.54 | 280 | 0.1468 |
| 0.1538 | 2.63 | 290 | 0.1470 |
| 0.1443 | 2.72 | 300 | 0.1467 |
| 0.1444 | 2.81 | 310 | 0.1467 |
| 0.1641 | 2.9 | 320 | 0.1466 |
| 0.1564 | 2.99 | 330 | 0.1466 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "allenai/OLMo-1B", "model-index": [{"name": "O0428B1", "results": []}]} | Litzy619/O0428B1 | null | [
"safetensors",
"generated_from_trainer",
"base_model:allenai/OLMo-1B",
"license:apache-2.0",
"region:us"
] | null | 2024-04-29T04:25:06+00:00 |
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. -->
# food_model_calsification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3138
- Accuracy: 0.904
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7648 | 0.992 | 62 | 2.5554 | 0.844 |
| 1.786 | 2.0 | 125 | 1.6917 | 0.881 |
| 1.4047 | 2.992 | 187 | 1.3760 | 0.912 |
| 1.2497 | 3.968 | 248 | 1.3138 | 0.904 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "food_model_calsification", "results": []}]} | georffrey/food_model_calsification | null | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T04:27:13+00:00 |
null | null | {} | aarashfeizi/jfg-new-SDXL-LoRA | null | [
"region:us"
] | null | 2024-04-29T04:27:50+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
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## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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## Glossary [optional]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/vfojwet | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T04:27:51+00:00 |
text-generation | transformers | # **BLOSSOM-v5-32b**
[💻Github](https://github.com/Azure99/BlossomLM) • [🚀Blossom Chat Demo](https://blossom-chat.com/)
### What's new?
The Blossom V5 series models is fully trained using high-quality data distilled from gpt-4-0125-preview, resulting in significant improvements.
### Introduction
Blossom is a conversational large language model, fine-tuned on the Blossom Orca/Wizard/Chat/Math mixed dataset based on the Qwen1.5-32B pre-trained model. Blossom possesses robust general capabilities and context comprehension. Additionally, the high-quality Chinese and English datasets used for training have been made open source.
Training was conducted in two stages. The first stage used 40K Wizard, 40K Orca, 10K Math single-turn instruction datasets, training for 1 epoch; the second stage used 10K Blossom chat multi-turn dialogue dataset, and 10% randomly sampled data from the first stage, training for 3 epochs.
### Inference
Inference is performed in the form of dialogue continuation.
Single-turn dialogue
```
A chat between a human and an artificial intelligence bot. The bot gives helpful, detailed, and polite answers to the human's questions.
|Human|: hello
|Bot|:
```
Multi-turn dialogue
```
A chat between a human and an artificial intelligence bot. The bot gives helpful, detailed, and polite answers to the human's questions.
|Human|: hello
|Bot|: Hello! How can I assist you today?<|endoftext|>
|Human|: Generate a random number using python
|Bot|:
```
Note: At the end of the Bot's output in the historical conversation, append a `<|endoftext|>`. | {"language": ["zh", "en"], "license": "apache-2.0", "datasets": ["Azure99/blossom-chat-v3", "Azure99/blossom-math-v4", "Azure99/blossom-wizard-v3", "Azure99/blossom-orca-v3"]} | Azure99/blossom-v5-32b | null | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"zh",
"en",
"dataset:Azure99/blossom-chat-v3",
"dataset:Azure99/blossom-math-v4",
"dataset:Azure99/blossom-wizard-v3",
"dataset:Azure99/blossom-orca-v3",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T04:28:02+00:00 |
text-generation | transformers |
# mistral-orpo-capybara-3k
This model is a full fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) with ORPO on the [eduagarcia/capybara-dpo-3k](https://huggingface.co/datasets/eduagarcia/capybara-dpo-3k) dataset with the [huggingface/alignment-handbook](https://github.com/huggingface/alignment-handbook).
## Model description
Trained for 4.5 hours on 1xA100
### Aligment Handbook recipe
```yaml
# Model arguments
model_name_or_path: mistralai/Mistral-7B-v0.1
model_revision: main
torch_dtype: bfloat16
use_flash_attention_2: true
trust_remote_code: true
# Data training arguments
chat_template: "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n' + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}"
dataset_mixer:
eduagarcia/capybara-dpo-3k: 1.0
dataset_splits:
- train
- test
preprocessing_num_workers: 8
# ORPOTrainer arguments
bf16: true
beta: 0.05
gradient_accumulation_steps: 8
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: true
hub_model_id: mistral-orpo-capybara-3k
learning_rate: 5.0e-6
log_level: info
logging_steps: 10
lr_scheduler_type: inverse_sqrt
max_length: 2048
max_prompt_length: 1792
num_train_epochs: 1
optim: adamw_bnb_8bit
output_dir: data/mistral-orpo-capybara-3k
per_device_train_batch_size: 4
push_to_hub: true
report_to:
- tensorboard
- wandb
save_strategy: "no"
seed: 42
warmup_steps: 100
```
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.1.2
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"language": ["en"], "license": "apache-2.0", "tags": ["alignment-handbook", "trl", "orpo", "generated_from_trainer"], "datasets": ["eduagarcia/capybara-dpo-3k"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "mistral-orpo-capybara-3k", "results": []}]} | eduagarcia/mistral-orpo-capybara-3k | null | [
"transformers",
"tensorboard",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"trl",
"orpo",
"generated_from_trainer",
"conversational",
"en",
"dataset:eduagarcia/capybara-dpo-3k",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T04:28:13+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## How to Get Started with the Model
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
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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).
- **Hardware Type:** [More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | golf2248/a50avyu | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T04:29:37+00:00 |
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. -->
# test-small-komodel
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the jsfamily/test-small-komodel dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 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
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"language": ["ko"], "license": "apache-2.0", "tags": ["hf-asr-leaderboard", "generated_from_trainer"], "datasets": ["jsfamily/test-small-komodel"], "base_model": "openai/whisper-small", "model-index": [{"name": "test-small-komodel", "results": []}]} | jsfamily/test-small-komodel | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"ko",
"dataset:jsfamily/test-small-komodel",
"base_model:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T04:30:22+00:00 |
null | null | {} | automated-finetunning/bart_test_20 | null | [
"region:us"
] | null | 2024-04-29T04:33:08+00:00 |
|
null | null | {} | LeoZZzzZZ/distilbert-base-uncased-finetuned-fact | null | [
"region:us"
] | null | 2024-04-29T04:33:48+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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[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]
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### Testing Data, Factors & Metrics
#### 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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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]
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[More Information Needed]
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[More Information Needed]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | Ramikan-BR/tinyllama_PY-CODER-bnb-4bit-lora_4k-v2_q4_k_m | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-29T04:34:52+00:00 |
null | null | {} | DOGEKATIORO/celes | null | [
"region:us"
] | null | 2024-04-29T04:35:12+00:00 |
|
text-generation | transformers |
# arcee-ai/Llama-3-MegaMed-8B-Model-Stock
arcee-ai/Llama-3-MegaMed-8B-Model-Stock is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
## 🧩 Configuration
```yaml
models:
- model: aaditya/OpenBioLLM-Llama3-8B
- model: johnsnowlabs/JSL-Med-Sft-Llama-3-8B
- model: MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3
merge_method: model_stock
base_model: meta-llama/Meta-Llama-3-8B
dtype: float16
``` | {"license": "apache-2.0", "tags": ["merge", "mergekit"]} | arcee-ai/Llama-3-MegaMed-8B-Model-Stock | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T04:35:26+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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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.
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[More Information Needed]
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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | Crysiss/llama-3-8B-healthcare-15000 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T04:36:17+00:00 |
text2text-generation | transformers |
# Model Card for Model ID
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### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### 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": []} | kssumanth6/t5_small_chit_chat_generator_v3 | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T04:36:27+00:00 |
null | null | {} | qsy71/medical_qwen_1.8b | null | [
"region:us"
] | null | 2024-04-29T04:36:56+00:00 |
|
null | null | {} | automated-finetunning/bart_test_19 | null | [
"region:us"
] | null | 2024-04-29T04:37:06+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | bsheon/adsl | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T04:37:29+00:00 |
text-generation | transformers |
## モデル
- ベースモデル：[ryota39/llm-jp-1b-sft-100k-LoRA](https://huggingface.co/ryota39/llm-jp-1b-sft-100k-LoRA)
- 学習データセット：[ryota39/dpo-ja-194k](https://huggingface.co/datasets/ryota39/dpo-ja-194k)
- 学習方式：フルパラメータチューニング
## サンプル
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained(
"ryota39/llm-jp-1b-sft-15k"
)
pad_token_id = tokenizer.pad_token_id
model = AutoModelForCausalLM.from_pretrained(
"ryota39/llm-jp-1b-sft-15k",
device_map="auto",
)
text = "###Input: 東京の観光名所を教えてください。\n###Output: "
tokenized_input = tokenizer.encode(
text,
add_special_tokens=False,
return_tensors="pt"
).to(model.device)
attention_mask = torch.ones_like(tokenized_input)
attention_mask[tokenized_input == pad_token_id] = 0
with torch.no_grad():
output = model.generate(
tokenized_input,
attention_mask=attention_mask,
max_new_tokens=128,
do_sample=True,
top_p=0.95,
temperature=0.8,
repetition_penalty=1.0
)[0]
print(tokenizer.decode(output))
```
## 出力例
```
###Input: 東京の観光名所を教えてください。
###Output: 浅草寺。東京都台東区にある日本の仏教寺院。
浅草寺は、徳川家の菩提寺として有名な寺院。この寺は、創建から200年以上もの歴史を持ち、多くの人々から信仰されている。
また、境内には多くの建造物があり、歴史を感じることが出来る。また、境内には雷門があり、多くの人が訪れている。
また、本堂の中には、本尊である釈迦如来と文殊菩薩が安置されており、歴史を感じながら参拝することが出来る。この浅草寺は、東京都の観光
```
## 謝辞
本成果は【LOCAL AI HACKATHON #001】240時間ハッカソンの成果です。
運営の方々に深く御礼申し上げます。
- 【メタデータラボ株式会社】様
- 【AI声づくり技術研究会】
- サーバー主：やなぎ(Yanagi)様
- 【ローカルLLMに向き合う会】
- サーバー主：saldra(サルドラ)様
[メタデータラボ、日本最大規模のAIハッカソン「LOCAL AI HACKATHON #001」~ AIの民主化 ~を開催、本日より出場チームの募集を開始](https://prtimes.jp/main/html/rd/p/000000008.000056944.html)
| {"language": ["ja"], "license": "cc", "library_name": "transformers", "tags": ["dpo"], "datasets": ["ryota39/dpo-ja-194k"]} | ryota39/llm-jp-1b-sft-100k-LoRA-dpo-194k | null | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"dpo",
"ja",
"dataset:ryota39/dpo-ja-194k",
"license:cc",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T04:37:39+00:00 |
text-to-image | diffusers |
# SDXL LoRA DreamBooth - aarashfeizi/jean-francois-godbout
<Gallery />
## Model description
### These are aarashfeizi/jean-francois-godbout LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- **LoRA**: download **[`/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout.safetensors` here 💾](/aarashfeizi/jean-francois-godbout/blob/main//home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout.safetensors)**.
- Place it on your `models/Lora` folder.
- On AUTOMATIC1111, load the LoRA by adding `<lora:/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
- *Embeddings*: download **[`/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout_emb.safetensors` here 💾](/aarashfeizi/jean-francois-godbout/blob/main//home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout_emb.safetensors)**.
- Place it on it on your `embeddings` folder
- Use it by adding `/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout_emb` to your prompt. For example, `A photo of Jean-Francois Godbout`
(you need both the LoRA and the embeddings as they were trained together for this LoRA)
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('aarashfeizi/jean-francois-godbout', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='aarashfeizi/jean-francois-godbout', filename='/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout_emb.safetensors', repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=[], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=[], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
image = pipeline('A photo of Jean-Francois Godbout talking with Joe Biden').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `TOK` → use `<s0><s1>` in your prompt
## Details
All [Files & versions](/aarashfeizi/jean-francois-godbout/tree/main).
The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
| {"license": "openrail++", "tags": ["stable-diffusion-xl", "stable-diffusion-xl-diffusers", "diffusers-training", "text-to-image", "diffusers", "lora", "template:sd-lora"], "widget": [{"text": "A photo of Jean-Francois Godbout talking with Joe Biden", "output": {"url": "image_0.png"}}, {"text": "A photo of Jean-Francois Godbout talking with Joe Biden", "output": {"url": "image_1.png"}}, {"text": "A photo of Jean-Francois Godbout talking with Joe Biden", "output": {"url": "image_2.png"}}, {"text": "A photo of Jean-Francois Godbout talking with Joe Biden", "output": {"url": "image_3.png"}}], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "A photo of Jean-Francois Godbout"} | aarashfeizi/jean-francois-godbout | null | [
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"diffusers-training",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | null | 2024-04-29T04:37:56+00:00 |
null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# O0428B2
This model is a fine-tuned version of [allenai/OLMo-1B](https://huggingface.co/allenai/OLMo-1B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1472
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 60
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.8141 | 0.09 | 10 | 2.3918 |
| 2.3705 | 0.18 | 20 | 2.2984 |
| 2.1425 | 0.27 | 30 | 1.8693 |
| 1.5052 | 0.36 | 40 | 0.9945 |
| 0.6052 | 0.45 | 50 | 0.2390 |
| 0.2304 | 0.54 | 60 | 0.1544 |
| 0.1666 | 0.63 | 70 | 0.1506 |
| 0.1633 | 0.73 | 80 | 0.1514 |
| 0.1444 | 0.82 | 90 | 0.1481 |
| 0.1551 | 0.91 | 100 | 0.1476 |
| 0.1575 | 1.0 | 110 | 0.1517 |
| 0.1455 | 1.09 | 120 | 0.1483 |
| 0.1724 | 1.18 | 130 | 0.1493 |
| 0.1558 | 1.27 | 140 | 0.1484 |
| 0.1604 | 1.36 | 150 | 0.1473 |
| 0.1432 | 1.45 | 160 | 0.1488 |
| 0.1446 | 1.54 | 170 | 0.1467 |
| 0.1463 | 1.63 | 180 | 0.1470 |
| 0.1554 | 1.72 | 190 | 0.1506 |
| 0.1595 | 1.81 | 200 | 0.1480 |
| 0.1462 | 1.9 | 210 | 0.1475 |
| 0.1449 | 1.99 | 220 | 0.1494 |
| 0.1464 | 2.08 | 230 | 0.1473 |
| 0.1671 | 2.18 | 240 | 0.1473 |
| 0.1437 | 2.27 | 250 | 0.1472 |
| 0.1448 | 2.36 | 260 | 0.1478 |
| 0.1508 | 2.45 | 270 | 0.1477 |
| 0.152 | 2.54 | 280 | 0.1475 |
| 0.1547 | 2.63 | 290 | 0.1476 |
| 0.1447 | 2.72 | 300 | 0.1474 |
| 0.1448 | 2.81 | 310 | 0.1472 |
| 0.1646 | 2.9 | 320 | 0.1472 |
| 0.1567 | 2.99 | 330 | 0.1472 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "allenai/OLMo-1B", "model-index": [{"name": "O0428B2", "results": []}]} | Litzy619/O0428B2 | null | [
"safetensors",
"generated_from_trainer",
"base_model:allenai/OLMo-1B",
"license:apache-2.0",
"region:us"
] | null | 2024-04-29T04:38:32+00:00 |
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. -->
# distilled-code-llama
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 6
### Framework versions
- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "model-index": [{"name": "distilled-code-llama", "results": []}]} | anudaw/distilled-code-llama | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"generated_from_trainer",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T04:38:42+00:00 |
null | null |
# DavidAU/LWM-Text-1M-Q8_0-GGUF
This model was converted to GGUF format from [`LargeWorldModel/LWM-Text-1M`](https://huggingface.co/LargeWorldModel/LWM-Text-1M) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/LargeWorldModel/LWM-Text-1M) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo DavidAU/LWM-Text-1M-Q8_0-GGUF --model lwm-text-1m.Q8_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo DavidAU/LWM-Text-1M-Q8_0-GGUF --model lwm-text-1m.Q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m lwm-text-1m.Q8_0.gguf -n 128
```
| {"tags": ["llama-cpp", "gguf-my-repo"], "inference": false} | DavidAU/LWM-Text-1M-Q8_0-GGUF | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"region:us"
] | null | 2024-04-29T04:38:55+00:00 |
null | null | {"license": "unknown"} | Shreyash56/abcd | null | [
"license:unknown",
"region:us"
] | null | 2024-04-29T04:39:14+00:00 |
|
null | transformers |
# Uploaded model
- **Developed by:** baka999
- **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"} | baka999/ruozhiba_lora_model | 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 | 2024-04-29T04:39:23+00:00 |
null | null |
# DavidAU/LWM-Text-256K-Q8_0-GGUF
This model was converted to GGUF format from [`LargeWorldModel/LWM-Text-256K`](https://huggingface.co/LargeWorldModel/LWM-Text-256K) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/LargeWorldModel/LWM-Text-256K) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo DavidAU/LWM-Text-256K-Q8_0-GGUF --model lwm-text-256k.Q8_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo DavidAU/LWM-Text-256K-Q8_0-GGUF --model lwm-text-256k.Q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m lwm-text-256k.Q8_0.gguf -n 128
```
| {"tags": ["llama-cpp", "gguf-my-repo"], "inference": false} | DavidAU/LWM-Text-256K-Q8_0-GGUF | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"region:us"
] | null | 2024-04-29T04:39:43+00:00 |
text-classification | transformers | {} | KalaiselvanD/albert_model__29_1 | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T04:39:50+00:00 |
|
text-generation | transformers | {"license": "mit"} | migueldeguzmandev/GPT2XL_RLLMv19-5 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T04:40:17+00:00 |
|
null | null |
# DavidAU/LWM-Text-128K-Q8_0-GGUF
This model was converted to GGUF format from [`LargeWorldModel/LWM-Text-128K`](https://huggingface.co/LargeWorldModel/LWM-Text-128K) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/LargeWorldModel/LWM-Text-128K) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo DavidAU/LWM-Text-128K-Q8_0-GGUF --model lwm-text-128k.Q8_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo DavidAU/LWM-Text-128K-Q8_0-GGUF --model lwm-text-128k.Q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m lwm-text-128k.Q8_0.gguf -n 128
```
| {"tags": ["llama-cpp", "gguf-my-repo"], "inference": false} | DavidAU/LWM-Text-128K-Q8_0-GGUF | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"region:us"
] | null | 2024-04-29T04:40:24+00:00 |
null | diffusers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
House plans
#trained on:WhiteCase
### Model Description
<!-- Provide a longer summary of what this model is. -->
```python
## Model Details
class TrainingConfig:
image_size = 192 # the generated image resolution
train_batch_size = 16
eval_batch_size = 16 # how many images to sample during evaluation
num_epochs = 200
gradient_accumulation_steps = 1
learning_rate = 1e-4
lr_warmup_steps = 500
save_image_epochs = 10
save_model_epochs = 30
mixed_precision = 'fp16' # `no` for float32, `fp16` for automatic mixed precision
output_dir = 'ddpm-butterflies-128' # the model namy locally and on the HF Hub
push_to_hub = True # whether to upload the saved model to the HF Hub
hub_private_repo = False
overwrite_output_dir = True # overwrite the old model when re-running the notebook
seed = 0
config = TrainingConfig()
```
This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### 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": "diffusers"} | uisikdag/robin | null | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2024-04-29T04:40:28+00:00 |
text2text-generation | transformers | # Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** **விபின்**
- **Model type:** T5-small
- **Language(s) (NLP):** English
- **License:** Apache 2.0 license
- **Finetuned from model [optional]:** T5-small model
## Uses
This model aims to respond with extractive and abstractive keyphrases for the given content. Kindly use "find keyphrase: " as the task prefix prompt to get the desired outputs.
## Bias, Risks, and Limitations
This model response is based on the inputs given to it. So if any Harmful sentences given to this model, it will respond according to that.
## How to Get Started with the Model
```
from transformers import T5Tokenizer, T5ForConditionalGeneration
import torch
model_dir = "rv2307/keyphrase-abstraction-t5-small"
tokenizer = T5Tokenizer.from_pretrained(model_dir)
model = T5ForConditionalGeneration.from_pretrained(model_dir, torch_dtype=torch.bfloat16)
device = "cuda"
model.to(device)
def generate(text):
text = "find keyphrase: " + text
inputs = tokenizer(text, max_length=512, padding=True, truncation=True, return_tensors='pt')
inputs = {k:v.to(model.device) for k,v in inputs.items()}
with torch.no_grad():
outputs = model.generate(
inputs['input_ids'],
attention_mask=inputs['attention_mask'],
max_length=100,
use_cache=True
)
output_list = tokenizer.decode(outputs[0],skip_special_tokens=True)
return output_list
content = "Use of BICs by businesses has been recommended by the Task Force on Nature-related Financial Disclosures[2] and the first provider of BICs for sale is Botanic Gardens Conservation International (BGCI). The credits are generated by BGCI's international member organisations by rebuilding the populations of tree species at high risk of extinction under the IUCN Red List methodology.[3]"
outputs = generate(content)
print(outputs)
"""
[
"BICs for businesses",
"Task Force on Naturerelated Financial Disclosures",
"Botanic Gardens Conservation International (BGCI)",
"Rebuilding tree species at high risk",
"IUCN Red List methodology",
"Credits generated by BGCI",
"International member organisations"
]
"""
```
## Training Details
### Training Data
Mostly used open source datasets for these tasks, which are already available on the huggingface.
### Training Procedure
This model has been fine tuned for 6 epochs with 40k datasets collected from the internet.
### Results
```
Epoch Training Loss Validation Loss Rouge1 Rouge2 Rougel Rougelsum Gen Len
1 0.105800 0.087497 43.840900 19.029900 40.303200 40.320300 16.306200
2 0.097600 0.081029 46.335000 21.246800 42.377400 42.387500 16.404900
3 0.091800 0.077546 47.721200 22.467200 43.622400 43.632000 16.308200
4 0.087600 0.075441 48.633700 23.351300 44.493800 44.504300 16.359000
5 0.088200 0.074088 48.977500 23.747000 44.804900 44.813200 16.300500
6 0.084900 0.073381 49.347300 24.029500 45.097100 45.108300 16.332600
``` | {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "metrics": ["rouge", "bleu"]} | rv2307/keyphrase-abstraction-t5-small | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T04:41:24+00:00 |
text-generation | transformers | Quantizations of https://huggingface.co/mergekit-community/HX-Mistral-3B_v0.1
# From original readme
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method.
### Models Merged
The following models were included in the merge:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
dtype: float16
merge_method: linear
slices:
- sources:
- layer_range: [0, 16] # Assuming the first half of the model is more general and can be reduced more
model: mistralai/Mistral-7B-Instruct-v0.2
parameters:
weight: 0.5 # Reduce the weight of the first half to make room for the second half
- layer_range: [16, 32] # Assuming the second half of the model is more specialized and can be reduced less
model: mistralai/Mistral-7B-Instruct-v0.2
parameters:
weight: 0.5 # Maintain the weight of the second half
```
| {"language": ["en"], "license": "other", "tags": ["transformers", "gguf", "imatrix", "HX-Mistral-3B_v0.1"], "pipeline_tag": "text-generation", "inference": false} | duyntnet/HX-Mistral-3B_v0.1-imatrix-GGUF | null | [
"transformers",
"gguf",
"imatrix",
"HX-Mistral-3B_v0.1",
"text-generation",
"en",
"arxiv:2203.05482",
"license:other",
"region:us"
] | null | 2024-04-29T04:41:45+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | hbin0701/Llama_3b_MATH_FT_checkpoint-1200 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T04:41:46+00:00 |
null | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/alpindale/miquella-120b
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/miquella-120b-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/miquella-120b-i1-GGUF/resolve/main/miquella-120b.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 31.2 | |
| [GGUF](https://huggingface.co/mradermacher/miquella-120b-i1-GGUF/resolve/main/miquella-120b.i1-IQ2_XS.gguf) | i1-IQ2_XS | 34.7 | |
| [GGUF](https://huggingface.co/mradermacher/miquella-120b-i1-GGUF/resolve/main/miquella-120b.i1-IQ2_S.gguf) | i1-IQ2_S | 36.5 | |
| [GGUF](https://huggingface.co/mradermacher/miquella-120b-i1-GGUF/resolve/main/miquella-120b.i1-IQ2_M.gguf) | i1-IQ2_M | 39.7 | |
| [GGUF](https://huggingface.co/mradermacher/miquella-120b-i1-GGUF/resolve/main/miquella-120b.i1-Q2_K.gguf) | i1-Q2_K | 43.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/miquella-120b-i1-GGUF/resolve/main/miquella-120b.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 45.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/miquella-120b-i1-GGUF/resolve/main/miquella-120b.i1-IQ3_XS.gguf) | i1-IQ3_XS | 48.2 | |
| [PART 1](https://huggingface.co/mradermacher/miquella-120b-i1-GGUF/resolve/main/miquella-120b.i1-Q3_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/miquella-120b-i1-GGUF/resolve/main/miquella-120b.i1-Q3_K_S.gguf.part2of2) | i1-Q3_K_S | 50.8 | IQ3_XS probably better |
| [PART 1](https://huggingface.co/mradermacher/miquella-120b-i1-GGUF/resolve/main/miquella-120b.i1-IQ3_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/miquella-120b-i1-GGUF/resolve/main/miquella-120b.i1-IQ3_S.gguf.part2of2) | i1-IQ3_S | 51.0 | beats Q3_K* |
| [PART 1](https://huggingface.co/mradermacher/miquella-120b-i1-GGUF/resolve/main/miquella-120b.i1-IQ3_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/miquella-120b-i1-GGUF/resolve/main/miquella-120b.i1-IQ3_M.gguf.part2of2) | i1-IQ3_M | 52.7 | |
| [PART 1](https://huggingface.co/mradermacher/miquella-120b-i1-GGUF/resolve/main/miquella-120b.i1-Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/miquella-120b-i1-GGUF/resolve/main/miquella-120b.i1-Q3_K_M.gguf.part2of2) | i1-Q3_K_M | 56.7 | IQ3_S probably better |
| [PART 1](https://huggingface.co/mradermacher/miquella-120b-i1-GGUF/resolve/main/miquella-120b.i1-Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/miquella-120b-i1-GGUF/resolve/main/miquella-120b.i1-Q3_K_L.gguf.part2of2) | i1-Q3_K_L | 61.8 | IQ3_M probably better |
| [PART 1](https://huggingface.co/mradermacher/miquella-120b-i1-GGUF/resolve/main/miquella-120b.i1-IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/miquella-120b-i1-GGUF/resolve/main/miquella-120b.i1-IQ4_XS.gguf.part2of2) | i1-IQ4_XS | 62.9 | |
| [PART 1](https://huggingface.co/mradermacher/miquella-120b-i1-GGUF/resolve/main/miquella-120b.i1-Q4_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/miquella-120b-i1-GGUF/resolve/main/miquella-120b.i1-Q4_0.gguf.part2of2) | i1-Q4_0 | 66.7 | fast, low quality |
| [PART 1](https://huggingface.co/mradermacher/miquella-120b-i1-GGUF/resolve/main/miquella-120b.i1-Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/miquella-120b-i1-GGUF/resolve/main/miquella-120b.i1-Q4_K_S.gguf.part2of2) | i1-Q4_K_S | 66.9 | optimal size/speed/quality |
| [PART 1](https://huggingface.co/mradermacher/miquella-120b-i1-GGUF/resolve/main/miquella-120b.i1-Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/miquella-120b-i1-GGUF/resolve/main/miquella-120b.i1-Q4_K_M.gguf.part2of2) | i1-Q4_K_M | 70.7 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/miquella-120b-i1-GGUF/resolve/main/miquella-120b.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/miquella-120b-i1-GGUF/resolve/main/miquella-120b.i1-Q5_K_S.gguf.part2of2) | i1-Q5_K_S | 81.1 | |
| [PART 1](https://huggingface.co/mradermacher/miquella-120b-i1-GGUF/resolve/main/miquella-120b.i1-Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/miquella-120b-i1-GGUF/resolve/main/miquella-120b.i1-Q5_K_M.gguf.part2of2) | i1-Q5_K_M | 83.3 | |
| [PART 1](https://huggingface.co/mradermacher/miquella-120b-i1-GGUF/resolve/main/miquella-120b.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/miquella-120b-i1-GGUF/resolve/main/miquella-120b.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 96.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": "alpindale/miquella-120b", "quantized_by": "mradermacher"} | mradermacher/miquella-120b-i1-GGUF | null | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:alpindale/miquella-120b",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T04:41:56+00:00 |
text-to-image | diffusers |
## 🧨 Diffusers
This model can be used just like any other Stable Diffusion model. For more information,
please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion).
```python
from diffusers import StableDiffusionPipeline
import torch
model_id = "fr4b/compose-v2"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "YOUR PROMPT"
image = pipe(prompt).images[0]
image.save("image.png")
```
| {} | nextab/Compose-v2.0 | null | [
"diffusers",
"safetensors",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | null | 2024-04-29T04:42:09+00:00 |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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### Model Sources [optional]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[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]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | ZeroWater93/fast_whisper-large-v2-korea-common_17 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T04:43:29+00:00 |
null | null |
# DavidAU/LWM-Text-32K-Q8_0-GGUF
This model was converted to GGUF format from [`LargeWorldModel/LWM-Text-32K`](https://huggingface.co/LargeWorldModel/LWM-Text-32K) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/LargeWorldModel/LWM-Text-32K) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo DavidAU/LWM-Text-32K-Q8_0-GGUF --model lwm-text-32k.Q8_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo DavidAU/LWM-Text-32K-Q8_0-GGUF --model lwm-text-32k.Q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m lwm-text-32k.Q8_0.gguf -n 128
```
| {"tags": ["llama-cpp", "gguf-my-repo"], "inference": false} | DavidAU/LWM-Text-32K-Q8_0-GGUF | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"region:us"
] | null | 2024-04-29T04:44:18+00:00 |
image-text-to-text | transformers | ### TinyLLaVA
We trained 1 model with fewer than 1B parameters using the TinyLLaVA approach, employing the same training settings as [TinyLLaVA](https://github.com/DLCV-BUAA/TinyLLaVABench). For the Language and Vision models, we chose [OpenELM-450M-Instruct](apple/OpenELM-450M-Instruct) and [clip-vit-base-patch16](https://huggingface.co/openai/clip-vit-base-patch16), respectively. The Connector was configured with a 2-layer MLP. The dataset used for training is the save as [LLaVA](https://github.com/haotian-liu/LLaVA). During testing, we found that [TinyLLaVA-0.55B](https://huggingface.co/jiajunlong/TinyLLaVA-0.55B) exhibited significantly faster inference speed on CPU compared to [TinyLLaVA-1.5B](https://huggingface.co/bczhou/TinyLLaVA-1.5B)
### Usage
1. you need to download the generate file "generate_model.py".
2. running the following command:
```bash
python generate_model --model jiajunlong/TinyLLaVA-0.89B --prompt 'you want to ask' --image '/path/to/related/image'
```
or execute the following test code:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from generate_model import *
model = AutoModelForCausalLM.from_pretrained("jiajunlong/TinyLLaVA-0.55B", trust_remote_code=True)
config = model.config
tokenizer = AutoTokenizer.from_pretrained("jiajunlong/TinyLLaVA-0.55B", use_fast=False, model_max_length = config.tokenizer_model_max_length,padding_side = config.tokenizer_padding_side)
prompt="you want to ask"
image="/path/to/related/image"
output_text, genertaion_time = generate(prompt=prompt, image=image, model=model, tokenizer=tokenizer)
print_txt = (
f'\r\n{"=" * os.get_terminal_size().columns}\r\n'
'\033[1m Prompt + Generated Output\033[0m\r\n'
f'{"-" * os.get_terminal_size().columns}\r\n'
f'{output_text}\r\n'
f'{"-" * os.get_terminal_size().columns}\r\n'
'\r\nGeneration took'
f'\033[1m\033[92m {round(genertaion_time, 2)} \033[0m'
'seconds.\r\n'
)
print(print_txt)
```
### Result
| model_name | gqa | textvqa | sqa | vqav2 | MME | MMB | MM-VET |
| :----------------------------------------------------------: | ----- | ------- | ----- | ----- | ------- | ----- | ------ |
| [TinyLLaVA-1.5B](https://huggingface.co/bczhou/TinyLLaVA-1.5B) | 60.3 | 51.7 | 60.3 | 76.9 | 1276.5 | 55.2 | 25.8 |
| [TinyLLaVA-0.55B](https://huggingface.co/jiajunlong/TinyLLaVA-0.89B) | 50.38 | 36.37 | 50.02 | 65.44 | 1056.69 | 26.29 | 15.4 |
| {"license": "apache-2.0", "pipeline_tag": "image-text-to-text"} | jiajunlong/TinyLLaVA-0.55B | null | [
"transformers",
"safetensors",
"text-generation",
"image-text-to-text",
"custom_code",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | null | 2024-04-29T04:44:54+00:00 |
null | transformers | {"license": "apache-2.0"} | bebongkyo/rewind | null | [
"transformers",
"gguf",
"llama",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T04:45:36+00:00 |
|
null | null | {} | hyojin99/wav2vec2-base-timit-demo | null | [
"region:us"
] | null | 2024-04-29T04:45:46+00:00 |
|
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. -->
# llava-1.5-7b-hf-ft-mix-vsft-3
This model is a fine-tuned version of [HuggingFaceH4/vsft-llava-1.5-7b-hf-trl](https://huggingface.co/HuggingFaceH4/vsft-llava-1.5-7b-hf-trl) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.4e-05
- train_batch_size: 4
- eval_batch_size: 16
- 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
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.19.1 | {"library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "HuggingFaceH4/vsft-llava-1.5-7b-hf-trl", "model-index": [{"name": "llava-1.5-7b-hf-ft-mix-vsft-3", "results": []}]} | Salmoli/llava-1.5-7b-hf-ft-mix-vsft-3 | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:HuggingFaceH4/vsft-llava-1.5-7b-hf-trl",
"region:us"
] | null | 2024-04-29T04:45:50+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/8w767z1 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T04:46:08+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | golf2248/843unq1 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-29T04:47:12+00:00 |
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. -->
# llamaduo_synth_ds_v0.1
This model is a fine-tuned version of [google/gemma-7b](https://huggingface.co/google/gemma-7b) on the chansung/synth_ds dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8292
## 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: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 3
- gradient_accumulation_steps: 2
- total_train_batch_size: 12
- total_eval_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.7837 | 0.9995 | 939 | 1.9731 |
| 0.7509 | 2.0 | 1879 | 1.9719 |
| 0.7086 | 2.9995 | 2817 | 2.0286 |
| 0.6156 | 4.0 | 3757 | 2.1647 |
| 0.4937 | 4.9995 | 4696 | 2.3686 |
| 0.4075 | 6.0 | 5636 | 2.7269 |
| 0.3395 | 6.9995 | 6575 | 3.1681 |
| 0.2962 | 8.0 | 7515 | 3.6134 |
| 0.284 | 8.9995 | 8454 | 3.8100 |
| 0.2782 | 9.9957 | 9390 | 3.8292 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.14.6
- Tokenizers 0.19.1 | {"license": "gemma", "library_name": "peft", "tags": ["alignment-handbook", "trl", "sft", "generated_from_trainer"], "datasets": ["chansung/synth_ds"], "base_model": "google/gemma-7b", "model-index": [{"name": "llamaduo_synth_ds_v0.1", "results": []}]} | chansung/llamaduo_synth_ds_v0.1 | null | [
"peft",
"tensorboard",
"safetensors",
"gemma",
"alignment-handbook",
"trl",
"sft",
"generated_from_trainer",
"dataset:chansung/synth_ds",
"base_model:google/gemma-7b",
"license:gemma",
"4-bit",
"region:us"
] | null | 2024-04-29T04:47:35+00:00 |
text-generation | transformers |
# Model Card for Model ID
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## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | lunarsylph/stablecell_v48 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T04:48:18+00:00 |
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