modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-02 18:27:42
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
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listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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abhishek/autotrain-llama3-orpo
|
abhishek
| 2024-04-19T14:43:44Z | 7 | 6 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"autotrain",
"text-generation-inference",
"conversational",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-04-19T13:48:17Z |
---
tags:
- autotrain
- text-generation-inference
- text-generation
library_name: transformers
widget:
- messages:
- role: user
content: What is your favorite condiment?
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
```
|
clam004/Qwen-Qwen1.5-0.5B-Chat
|
clam004
| 2024-04-19T14:43:35Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-04-06T18:19:10Z |
---
library_name: transformers
tags: []
---
# 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]
|
ali9132/CostumData_new
|
ali9132
| 2024-04-19T14:34:00Z | 7 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"fa",
"dataset:mozilla-foundation/CostumData_new",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-04-19T12:18:38Z |
---
language:
- fa
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/CostumData_new
model-index:
- name: Whisper Small CostumData_new
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small CostumData_new
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the CostumData_new dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2603
- eval_wer: 28.9730
- eval_runtime: 3297.5897
- eval_samples_per_second: 1.931
- eval_steps_per_second: 0.241
- epoch: 2.5126
- step: 1000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
AlignmentResearch/robust_llm_pythia-160m_ian-022_PasswordMatch_n-its-10
|
AlignmentResearch
| 2024-04-19T14:32:55Z | 5 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-160m",
"base_model:finetune:EleutherAI/pythia-160m",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-04-19T14:32:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
base_model: EleutherAI/pythia-160m
model-index:
- name: robust_llm_pythia-160m_ian-022_PasswordMatch_n-its-10
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# robust_llm_pythia-160m_ian-022_PasswordMatch_n-its-10
This model is a fine-tuned version of [EleutherAI/pythia-160m](https://huggingface.co/EleutherAI/pythia-160m) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
|
luluxxx/test
|
luluxxx
| 2024-04-19T14:28:37Z | 0 | 0 | null |
[
"animatediff",
"controlnet",
"IPAdapter",
"automatic-speech-recognition",
"en",
"region:us"
] |
automatic-speech-recognition
| 2024-04-19T12:21:00Z |
---
language:
- en
pipeline_tag: automatic-speech-recognition
tags:
- animatediff
- controlnet
- IPAdapter
---
fix to have pipeline_animatediff_controlnet working correctly with IPAdapter
https://github.com/huggingface/diffusers/pull/7413
|
Raul569/lora_outfit_recommender_model
|
Raul569
| 2024-04-19T14:26:21Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T14:26:09Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** Raul569
- **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)
|
coralexbadea/llama3-sql-adapter
|
coralexbadea
| 2024-04-19T14:23:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T14:23:02Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** coralexbadea
- **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)
|
Sif10/summarization
|
Sif10
| 2024-04-19T14:22:38Z | 22 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-base",
"base_model:finetune:google-t5/t5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-04-19T10:30:44Z |
---
license: apache-2.0
base_model: google-t5/t5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: summarization
results: []
---
<!-- 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. -->
# summarization
This model is a fine-tuned version of [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2491
- Rouge1: 0.3279
- Rouge2: 0.2271
- Rougel: 0.3003
- Rougelsum: 0.3005
- Gen Len: 18.9811
## 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: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.372 | 1.0 | 4189 | 0.2643 | 0.3326 | 0.2341 | 0.3055 | 0.3053 | 18.9784 |
| 0.3303 | 2.0 | 8378 | 0.2558 | 0.3379 | 0.2401 | 0.3112 | 0.3112 | 18.9808 |
| 0.3069 | 3.0 | 12567 | 0.2482 | 0.34 | 0.241 | 0.3129 | 0.313 | 18.9815 |
| 0.3057 | 4.0 | 16756 | 0.2491 | 0.3279 | 0.2271 | 0.3003 | 0.3005 | 18.9811 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
Alefiah/UrduSum7
|
Alefiah
| 2024-04-19T14:20:51Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-04-19T14:11:50Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: UrduSum7
results: []
---
<!-- 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. -->
# UrduSum7
This model is a fine-tuned version of [ahmed0189/mT5-Arabic-text-summarization](https://huggingface.co/ahmed0189/mT5-Arabic-text-summarization) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 175 | 6.0928 | 0.0 | 0.0 | 0.0 | 0.0 | 40.64 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.13.3
|
tedad09/PolizzeDonut-CR-ProvaGrayscale-5Epochs
|
tedad09
| 2024-04-19T14:17:15Z | 6 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:naver-clova-ix/donut-base",
"base_model:finetune:naver-clova-ix/donut-base",
"license:mit",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2024-04-19T12:43:32Z |
---
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: PolizzeDonut-CR-ProvaGrayscale-5Epochs
results: []
---
<!-- 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. -->
# PolizzeDonut-CR-ProvaGrayscale-5Epochs
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset.
## 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: 2
- eval_batch_size: 8
- 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 4.38.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
brugmark/all-MiniLM-L6-v2-personal-project-default-2024-04-19
|
brugmark
| 2024-04-19T14:14:31Z | 6 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:sentence-transformers/all-MiniLM-L6-v2",
"base_model:finetune:sentence-transformers/all-MiniLM-L6-v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2024-04-19T13:12:36Z |
---
license: apache-2.0
base_model: sentence-transformers/all-MiniLM-L6-v2
tags:
- generated_from_trainer
model-index:
- name: all-MiniLM-L6-v2-personal-project-default-2024-04-19
results: []
---
<!-- 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. -->
# all-MiniLM-L6-v2-personal-project-default-2024-04-19
This model is a fine-tuned version of [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 10.7201
- eval_runtime: 3708.4666
- eval_samples_per_second: 8.168
- eval_steps_per_second: 0.255
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
|
mani-a-i/mistral-7b-v2-full-model
|
mani-a-i
| 2024-04-19T14:11:57Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-04-19T13:58:53Z |
---
library_name: transformers
tags: []
---
# 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]
|
MR-Eder/llama3-1000-steps-wiki-de-conversation-merged-16bit-GGUF
|
MR-Eder
| 2024-04-19T14:10:52Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-04-19T14:04:35Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
---
# Uploaded model
- **Developed by:** MR-Eder
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
DandinPower/deberta-v3-base-maftt
|
DandinPower
| 2024-04-19T14:02:54Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"nycu-112-2-datamining-hw2",
"generated_from_trainer",
"en",
"dataset:DandinPower/review_mergeallfeaturetotext",
"base_model:microsoft/deberta-v3-base",
"base_model:finetune:microsoft/deberta-v3-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-04-19T13:21:23Z |
---
language:
- en
license: mit
base_model: microsoft/deberta-v3-base
tags:
- nycu-112-2-datamining-hw2
- generated_from_trainer
datasets:
- DandinPower/review_mergeallfeaturetotext
metrics:
- accuracy
model-index:
- name: deberta-v3-base-maftt
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: DandinPower/review_mergeallfeaturetotext
type: DandinPower/review_mergeallfeaturetotext
metrics:
- name: Accuracy
type: accuracy
value: 0.6288571428571429
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-v3-base-maftt
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the DandinPower/review_mergeallfeaturetotext dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4616
- Accuracy: 0.6289
- Macro F1: 0.6302
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 1.0302 | 0.14 | 500 | 1.0771 | 0.5511 | 0.5499 |
| 1.0412 | 0.29 | 1000 | 0.9406 | 0.5966 | 0.6030 |
| 0.9494 | 0.43 | 1500 | 0.9546 | 0.5949 | 0.5602 |
| 0.898 | 0.57 | 2000 | 1.0436 | 0.5957 | 0.5872 |
| 0.9171 | 0.71 | 2500 | 0.9004 | 0.622 | 0.6074 |
| 0.8856 | 0.86 | 3000 | 0.8741 | 0.6137 | 0.5990 |
| 0.9359 | 1.0 | 3500 | 0.8821 | 0.6267 | 0.6245 |
| 0.8626 | 1.14 | 4000 | 0.8859 | 0.6213 | 0.6200 |
| 0.7953 | 1.29 | 4500 | 0.8606 | 0.6337 | 0.6271 |
| 0.8206 | 1.43 | 5000 | 0.8543 | 0.6169 | 0.6202 |
| 0.8184 | 1.57 | 5500 | 0.9360 | 0.6266 | 0.6165 |
| 0.8044 | 1.71 | 6000 | 0.8606 | 0.6234 | 0.6227 |
| 0.7094 | 1.86 | 6500 | 0.8842 | 0.6434 | 0.6387 |
| 0.8264 | 2.0 | 7000 | 0.9063 | 0.612 | 0.6128 |
| 0.6951 | 2.14 | 7500 | 0.8782 | 0.6386 | 0.6415 |
| 0.704 | 2.29 | 8000 | 0.9510 | 0.6326 | 0.6308 |
| 0.6806 | 2.43 | 8500 | 0.8709 | 0.6413 | 0.6455 |
| 0.6983 | 2.57 | 9000 | 0.8977 | 0.6426 | 0.6436 |
| 0.6852 | 2.71 | 9500 | 0.9686 | 0.5984 | 0.6010 |
| 0.6761 | 2.86 | 10000 | 0.8961 | 0.6386 | 0.6406 |
| 0.6804 | 3.0 | 10500 | 0.9378 | 0.6307 | 0.6332 |
| 0.5329 | 3.14 | 11000 | 1.1209 | 0.6341 | 0.6382 |
| 0.5461 | 3.29 | 11500 | 1.0323 | 0.6393 | 0.6377 |
| 0.5725 | 3.43 | 12000 | 1.0678 | 0.6334 | 0.6366 |
| 0.5499 | 3.57 | 12500 | 1.0547 | 0.6374 | 0.6394 |
| 0.5218 | 3.71 | 13000 | 1.0524 | 0.6453 | 0.6460 |
| 0.5022 | 3.86 | 13500 | 1.1100 | 0.6363 | 0.6358 |
| 0.534 | 4.0 | 14000 | 1.0378 | 0.6357 | 0.6386 |
| 0.3823 | 4.14 | 14500 | 1.3985 | 0.6357 | 0.6357 |
| 0.4518 | 4.29 | 15000 | 1.3265 | 0.6314 | 0.6318 |
| 0.4147 | 4.43 | 15500 | 1.3946 | 0.631 | 0.6324 |
| 0.3936 | 4.57 | 16000 | 1.4649 | 0.6279 | 0.6308 |
| 0.4339 | 4.71 | 16500 | 1.5322 | 0.6286 | 0.6314 |
| 0.4448 | 4.86 | 17000 | 1.4890 | 0.629 | 0.6302 |
| 0.4006 | 5.0 | 17500 | 1.4616 | 0.6289 | 0.6302 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
idoru/jetmoe-8b-MyRus-kto
|
idoru
| 2024-04-19T14:01:19Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"jetmoe",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-04-19T13:57:01Z |
---
library_name: transformers
tags: []
---
# 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]
|
OneGate/Llama3-OGSQL-FT-8B
|
OneGate
| 2024-04-19T14:00:49Z | 43 | 2 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"Text-to-sql",
"conversational",
"license:cc-by-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-04-19T11:45:16Z |
---
license: cc-by-4.0
tags:
- Text-to-sql
library_name: transformers
---
### Llama3-OGSQL-8B
### Model Description
Llama3-OGSQL-8B was fine-tuned on the most recent and state of the art models (LLAMA 3) for the task of converting natural language text into SQL queries.
The model has been trained on more than 270 million tokens, ensuring robust performance and high accuracy in SQL generation tasks.
- **Model type**: Auto-regressive language model
- **Language(s) (NLP)**: SQL (target language for generation)
- **Finetuned from model**: Llama3-8B
## Use Case
OGSQL-7B is designed to facilitate the conversion of natural language queries into structured SQL commands, aiding in database querying without the need for manual SQL knowledge.
## How to Get Started with the Model
```python
# Example code to load and use the model
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
model_name = "Llama3-OGSQL-8B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
def generate_sql(query):
inputs = tokenizer.encode(query, return_tensors="pt")
outputs = model.generate(inputs)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
# Example use
query = """
using this context:
-- Create Customers Table
CREATE TABLE Customers (
customer_id INTEGER PRIMARY KEY,
name TEXT NOT NULL,
email TEXT,
join_date DATE
);
-- Create Products Table
CREATE TABLE Products (
product_id INTEGER PRIMARY KEY,
name TEXT NOT NULL,
price DECIMAL(10, 2)
);
-- Create Orders Table
CREATE TABLE Orders (
order_id INTEGER PRIMARY KEY,
customer_id INTEGER,
product_id INTEGER,
order_date DATE,
quantity INTEGER,
total_price DECIMAL(10, 2),
FOREIGN KEY (customer_id) REFERENCES Customers(customer_id),
FOREIGN KEY (product_id) REFERENCES Products(product_id)
);
show me all the orders from last month , sort by date
"""
print(generate_sql(query))
```
## alternatively you can use this notebook:
[](https://colab.research.google.com/drive/1pQuIuCdoFMG76AH3BNZzep8PgRaZkkYS?usp=sharing)
|
dylanebert/imagedream
|
dylanebert
| 2024-04-19T13:57:32Z | 8 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"image-to-3d",
"arxiv:2312.02201",
"license:openrail",
"diffusers:MVDreamPipeline",
"region:us"
] |
image-to-3d
| 2024-04-19T13:57:16Z |
---
license: openrail
pipeline_tag: image-to-3d
---
This is a copy of [ashawkey/imagedream-ipmv-diffusers](https://huggingface.co/ashawkey/imagedream-ipmv-diffusers).
It is hosted here for persistence throughout the ML for 3D course.
# MVDream-diffusers Model Card
This is a port of https://huggingface.co/Peng-Wang/ImageDream into diffusers.
For usage, please check: https://github.com/ashawkey/mvdream_diffusers
## Citation
```
@article{wang2023imagedream,
title={ImageDream: Image-Prompt Multi-view Diffusion for 3D Generation},
author={Wang, Peng and Shi, Yichun},
journal={arXiv preprint arXiv:2312.02201},
year={2023}
}
```
## Misuse, Malicious Use, and Out-of-Scope Use
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
|
sosoai/Hansoldeco-llama3-8b-unsloth-v0.1-mlx
|
sosoai
| 2024-04-19T13:56:07Z | 7 | 0 |
mlx
|
[
"mlx",
"safetensors",
"llama",
"region:us"
] | null | 2024-04-19T13:40:45Z |
---
tags:
- mlx
---
# sosoai/Hansoldeco-llama3-8b-unsloth-v0.1-mlx
This model was converted to MLX format from [`sosoai/Hansoldeco-llama3-8b-unsloth-v0.1`]() using mlx-lm version **0.9.0**.
Refer to the [original model card](https://huggingface.co/sosoai/Hansoldeco-llama3-8b-unsloth-v0.1) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("sosoai/Hansoldeco-llama3-8b-unsloth-v0.1-mlx")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
ThoMyh/distilBert_for_binary_sentiment_classification
|
ThoMyh
| 2024-04-19T13:53:22Z | 5 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-04-19T09:38:39Z |
---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilBert_for_binary_sentiment_classification
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilBert_for_binary_sentiment_classification
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1395
- Accuracy: 0.9645
- F1: 0.9633
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.1351 | 1.0 | 1000 | 0.1304 | 0.9575 | 0.9563 |
| 0.0705 | 2.0 | 2000 | 0.1395 | 0.9645 | 0.9633 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
bartowski/Meta-Llama-3-70B-Instruct-exl2
|
bartowski
| 2024-04-19T13:52:12Z | 5 | 3 | null |
[
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"text-generation",
"en",
"license:other",
"region:us"
] |
text-generation
| 2024-04-19T13:52:11Z |
---
language:
- en
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
license: other
license_name: llama3
license_link: LICENSE
extra_gated_prompt: >-
### META LLAMA 3 COMMUNITY LICENSE AGREEMENT
Meta Llama 3 Version Release Date: April 18, 2024
"Agreement" means the terms and conditions for use, reproduction, distribution and modification of the
Llama Materials set forth herein.
"Documentation" means the specifications, manuals and documentation accompanying Meta Llama 3
distributed by Meta at https://llama.meta.com/get-started/.
"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.
"Meta Llama 3" means the foundational large language models and software and algorithms, including
machine-learning model code, trained model weights, inference-enabling code, training-enabling code,
fine-tuning enabling code and other elements of the foregoing distributed by Meta at
https://llama.meta.com/llama-downloads.
"Llama Materials" means, collectively, Meta’s proprietary Meta Llama 3 and Documentation (and any
portion thereof) made available under this Agreement.
"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).
1. License Rights and Redistribution.
a. 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.
b. Redistribution and Use.
i. If you distribute or make available the Llama Materials (or any derivative works
thereof), or a product or service that uses any of them, including another AI model, you shall (A) provide
a copy of this Agreement with any such Llama Materials; and (B) prominently display “Built with Meta
Llama 3” on a related website, user interface, blogpost, about page, or product documentation. If you
use the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is
distributed or made available, you shall also include “Llama 3” at the beginning of any such AI model
name.
ii. 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.
iii. 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: “Meta Llama 3 is
licensed under the Meta Llama 3 Community License, Copyright © Meta Platforms, Inc. All Rights
Reserved.”
iv. Your use of the Llama Materials must comply with applicable laws and regulations
(including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama
Materials (available at https://llama.meta.com/llama3/use-policy), which is hereby incorporated by
reference into this Agreement.
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and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this
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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.
### Meta Llama 3 Acceptable Use Policy
Meta is committed to promoting safe and fair use of its tools and features, including Meta Llama 3. If you
access or use Meta Llama 3, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of
this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy)
#### Prohibited Uses
We want everyone to use Meta Llama 3 safely and responsibly. You agree you will not use, or allow
others to use, Meta Llama 3 to:
1. Violate the law or others’ rights, including to:
1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:
1. Violence or terrorism
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
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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.
5. Sexual solicitation
6. Any other criminal activity
2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
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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
2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Meta Llama 3 related to the following:
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
2. Guns and illegal weapons (including weapon development)
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5. Self-harm or harm to others, including suicide, cutting, and eating disorders
6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
3. Intentionally deceive or mislead others, including use of Meta Llama 3 related to the following:
1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
3. Generating, promoting, or further distributing spam
4. Impersonating another individual without consent, authorization, or legal right
5. Representing that the use of Meta Llama 3 or outputs are human-generated
6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement
4. Fail to appropriately disclose to end users any known dangers of your AI system
Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation
of this Policy through one of the following means:
* Reporting issues with the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3)
* Reporting risky content generated by the model:
developers.facebook.com/llama_output_feedback
* Reporting bugs and security concerns: facebook.com/whitehat/info
* Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected]
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quantized_by: bartowski
---
## Exllama v2 Quantizations of Meta-Llama-3-70B-Instruct
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.19">turboderp's ExLlamaV2 v0.0.19</a> for quantization.
<b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b>
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Conversion was done using the default calibration dataset.
Default arguments used except when the bits per weight is above 6.0, at that point the lm_head layer is quantized at 8 bits per weight instead of the default 6.
Original model: https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct
<a href="https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-exl2/tree/6_5">6.5 bits per weight</a>
<a href="https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-exl2/tree/4_25">4.25 bits per weight</a>
<a href="https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-exl2/tree/3_5">3.5 bits per weight</a>
<a href="https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-exl2/tree/3_0">3.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-exl2/tree/2_5">2.5 bits per weight</a>
<a href="https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-exl2/tree/2_2">2.2 bits per weight</a>
## Download instructions
With git:
```shell
git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-exl2
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download the `main` (only useful if you only care about measurement.json) branch to a folder called `Meta-Llama-3-70B-Instruct-exl2`:
```shell
mkdir Meta-Llama-3-70B-Instruct-exl2
huggingface-cli download bartowski/Meta-Llama-3-70B-Instruct-exl2 --local-dir Meta-Llama-3-70B-Instruct-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
Linux:
```shell
mkdir Meta-Llama-3-70B-Instruct-exl2-6_5
huggingface-cli download bartowski/Meta-Llama-3-70B-Instruct-exl2 --revision 6_5 --local-dir Meta-Llama-3-70B-Instruct-exl2-6_5 --local-dir-use-symlinks False
```
Windows (which apparently doesn't like _ in folders sometimes?):
```shell
mkdir Meta-Llama-3-70B-Instruct-exl2-6.5
huggingface-cli download bartowski/Meta-Llama-3-70B-Instruct-exl2 --revision 6_5 --local-dir Meta-Llama-3-70B-Instruct-exl2-6.5 --local-dir-use-symlinks False
```
|
mgoin/Meta-Llama-3-8B-Instruct-Marlin
|
mgoin
| 2024-04-19T13:52:09Z | 112 | 0 |
transformers
|
[
"transformers",
"llama",
"text-generation",
"facebook",
"meta",
"pytorch",
"llama-3",
"marlin",
"conversational",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"gptq",
"region:us"
] |
text-generation
| 2024-04-18T19:03:30Z |
---
language:
- en
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
- marlin
license: other
license_name: llama3
license_link: LICENSE
---
## Model Details
Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety.
**Model developers** Meta
**Variations** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants.
**Input** Models input text only.
**Output** Models generate text and code only.
**Model Architecture** Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
<table>
<tr>
<td>
</td>
<td><strong>Training Data</strong>
</td>
<td><strong>Params</strong>
</td>
<td><strong>Context length</strong>
</td>
<td><strong>GQA</strong>
</td>
<td><strong>Token count</strong>
</td>
<td><strong>Knowledge cutoff</strong>
</td>
</tr>
<tr>
<td rowspan="2" >Llama 3
</td>
<td rowspan="2" >A new mix of publicly available online data.
</td>
<td>8B
</td>
<td>8k
</td>
<td>Yes
</td>
<td rowspan="2" >15T+
</td>
<td>March, 2023
</td>
</tr>
<tr>
<td>70B
</td>
<td>8k
</td>
<td>Yes
</td>
<td>December, 2023
</td>
</tr>
</table>
**Llama 3 family of models**. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date** April 18, 2024.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license)
Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
**Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**.
**Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy.
## How to use
This repository contains two versions of Meta-Llama-3-8B-Instruct, for use with transformers and with the original `llama3` codebase.
### Use with transformers
See the snippet below for usage with Transformers:
```python
import transformers
import torch
model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
prompt = pipeline.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = pipeline(
prompt,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])
```
### Use with `llama3`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3)
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Meta-Llama-3-8B-Instruct --include "original/*" --local-dir Meta-Llama-3-8B-Instruct
```
For Hugging Face support, we recommend using transformers or TGI, but a similar command works.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint Pretraining utilized a cumulative** 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program.
<table>
<tr>
<td>
</td>
<td><strong>Time (GPU hours)</strong>
</td>
<td><strong>Power Consumption (W)</strong>
</td>
<td><strong>Carbon Emitted(tCO2eq)</strong>
</td>
</tr>
<tr>
<td>Llama 3 8B
</td>
<td>1.3M
</td>
<td>700
</td>
<td>390
</td>
</tr>
<tr>
<td>Llama 3 70B
</td>
<td>6.4M
</td>
<td>700
</td>
<td>1900
</td>
</tr>
<tr>
<td>Total
</td>
<td>7.7M
</td>
<td>
</td>
<td>2290
</td>
</tr>
</table>
**CO2 emissions during pre-training**. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively.
## Benchmarks
In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md).
### Base pretrained models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama2 7B</strong>
</td>
<td><strong>Llama2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama2 70B</strong>
</td>
</tr>
<tr>
<td rowspan="6" >General
</td>
<td>MMLU (5-shot)
</td>
<td>66.6
</td>
<td>45.7
</td>
<td>53.8
</td>
<td>79.5
</td>
<td>69.7
</td>
</tr>
<tr>
<td>AGIEval English (3-5 shot)
</td>
<td>45.9
</td>
<td>28.8
</td>
<td>38.7
</td>
<td>63.0
</td>
<td>54.8
</td>
</tr>
<tr>
<td>CommonSenseQA (7-shot)
</td>
<td>72.6
</td>
<td>57.6
</td>
<td>67.6
</td>
<td>83.8
</td>
<td>78.7
</td>
</tr>
<tr>
<td>Winogrande (5-shot)
</td>
<td>76.1
</td>
<td>73.3
</td>
<td>75.4
</td>
<td>83.1
</td>
<td>81.8
</td>
</tr>
<tr>
<td>BIG-Bench Hard (3-shot, CoT)
</td>
<td>61.1
</td>
<td>38.1
</td>
<td>47.0
</td>
<td>81.3
</td>
<td>65.7
</td>
</tr>
<tr>
<td>ARC-Challenge (25-shot)
</td>
<td>78.6
</td>
<td>53.7
</td>
<td>67.6
</td>
<td>93.0
</td>
<td>85.3
</td>
</tr>
<tr>
<td>Knowledge reasoning
</td>
<td>TriviaQA-Wiki (5-shot)
</td>
<td>78.5
</td>
<td>72.1
</td>
<td>79.6
</td>
<td>89.7
</td>
<td>87.5
</td>
</tr>
<tr>
<td rowspan="4" >Reading comprehension
</td>
<td>SQuAD (1-shot)
</td>
<td>76.4
</td>
<td>72.2
</td>
<td>72.1
</td>
<td>85.6
</td>
<td>82.6
</td>
</tr>
<tr>
<td>QuAC (1-shot, F1)
</td>
<td>44.4
</td>
<td>39.6
</td>
<td>44.9
</td>
<td>51.1
</td>
<td>49.4
</td>
</tr>
<tr>
<td>BoolQ (0-shot)
</td>
<td>75.7
</td>
<td>65.5
</td>
<td>66.9
</td>
<td>79.0
</td>
<td>73.1
</td>
</tr>
<tr>
<td>DROP (3-shot, F1)
</td>
<td>58.4
</td>
<td>37.9
</td>
<td>49.8
</td>
<td>79.7
</td>
<td>70.2
</td>
</tr>
</table>
### Instruction tuned models
<table>
<tr>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama 2 7B</strong>
</td>
<td><strong>Llama 2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama 2 70B</strong>
</td>
</tr>
<tr>
<td>MMLU (5-shot)
</td>
<td>68.4
</td>
<td>34.1
</td>
<td>47.8
</td>
<td>82.0
</td>
<td>52.9
</td>
</tr>
<tr>
<td>GPQA (0-shot)
</td>
<td>34.2
</td>
<td>21.7
</td>
<td>22.3
</td>
<td>39.5
</td>
<td>21.0
</td>
</tr>
<tr>
<td>HumanEval (0-shot)
</td>
<td>62.2
</td>
<td>7.9
</td>
<td>14.0
</td>
<td>81.7
</td>
<td>25.6
</td>
</tr>
<tr>
<td>GSM-8K (8-shot, CoT)
</td>
<td>79.6
</td>
<td>25.7
</td>
<td>77.4
</td>
<td>93.0
</td>
<td>57.5
</td>
</tr>
<tr>
<td>MATH (4-shot, CoT)
</td>
<td>30.0
</td>
<td>3.8
</td>
<td>6.7
</td>
<td>50.4
</td>
<td>11.6
</td>
</tr>
</table>
### Responsibility & Safety
We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.
Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.
Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.
As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started.
#### Llama 3-Instruct
As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case.
<span style="text-decoration:underline;">Safety</span>
For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable.
<span style="text-decoration:underline;">Refusals</span>
In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2.
We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date.
#### Responsible release
In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.
Misuse
If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/).
#### Critical risks
<span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives)
We have conducted a two fold assessment of the safety of the model in this area:
* Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks.
* Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model).
### <span style="text-decoration:underline;">Cyber Security </span>
We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval).
### <span style="text-decoration:underline;">Child Safety</span>
Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
### Community
Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.
Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide)
## Citation instructions
@article{llama3modelcard,
title={Llama 3 Model Card},
author={AI@Meta},
year={2024},
url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}
## Contributors
Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos
|
IMENMANSOUR/a2c-PandaReachDense-v3
|
IMENMANSOUR
| 2024-04-19T13:52:06Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-04-19T13:47:04Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.20 +/- 0.08
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Ldicet/detr-jan
|
Ldicet
| 2024-04-19T13:48:16Z | 6 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"detr",
"object-detection",
"generated_from_trainer",
"base_model:facebook/detr-resnet-50",
"base_model:finetune:facebook/detr-resnet-50",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2024-04-19T10:43:41Z |
---
license: apache-2.0
base_model: facebook/detr-resnet-50
tags:
- generated_from_trainer
model-index:
- name: detr-jan
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# detr-jan
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1
|
mlabonne/Llama-3-12B
|
mlabonne
| 2024-04-19T13:46:04Z | 15 | 9 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"conversational",
"base_model:meta-llama/Meta-Llama-3-8B",
"base_model:finetune:meta-llama/Meta-Llama-3-8B",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-04-18T19:34:36Z |
---
license: other
tags:
- merge
- mergekit
- lazymergekit
base_model:
- meta-llama/Meta-Llama-3-8B
- meta-llama/Meta-Llama-3-8B
---
# Llama-3-12B
Llama-3-12B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B)
* [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: meta-llama/Meta-Llama-3-8B
layer_range: [0, 24]
- sources:
- model: meta-llama/Meta-Llama-3-8B
layer_range: [8, 32]
merge_method: passthrough
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mlabonne/FrankenLlama-3-12B"
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"])
```
|
Augusto777/swinv2-tiny-patch4-window8-256-dmae-va-U5-42B
|
Augusto777
| 2024-04-19T13:43:20Z | 7 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"swinv2",
"image-classification",
"generated_from_trainer",
"base_model:microsoft/swinv2-tiny-patch4-window8-256",
"base_model:finetune:microsoft/swinv2-tiny-patch4-window8-256",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-04-19T04:23:12Z |
---
license: apache-2.0
base_model: microsoft/swinv2-tiny-patch4-window8-256
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: swinv2-tiny-patch4-window8-256-dmae-va-U5-42B
results: []
---
<!-- 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. -->
# swinv2-tiny-patch4-window8-256-dmae-va-U5-42B
This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9637
- Accuracy: 0.6667
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 42
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.9 | 7 | 7.8663 | 0.1167 |
| 6.936 | 1.94 | 15 | 7.7572 | 0.1167 |
| 6.936 | 2.97 | 23 | 7.1790 | 0.1167 |
| 6.7016 | 4.0 | 31 | 5.9033 | 0.1167 |
| 5.5439 | 4.9 | 38 | 4.6116 | 0.1167 |
| 5.5439 | 5.94 | 46 | 3.2830 | 0.1167 |
| 3.6477 | 6.97 | 54 | 2.2014 | 0.1167 |
| 2.2506 | 8.0 | 62 | 1.5647 | 0.45 |
| 2.2506 | 8.9 | 69 | 1.3160 | 0.45 |
| 1.5088 | 9.94 | 77 | 1.3676 | 0.3333 |
| 1.3868 | 10.97 | 85 | 1.3390 | 0.45 |
| 1.3868 | 12.0 | 93 | 1.3223 | 0.3833 |
| 1.351 | 12.9 | 100 | 1.3156 | 0.45 |
| 1.3271 | 13.94 | 108 | 1.3485 | 0.4833 |
| 1.3271 | 14.97 | 116 | 1.2646 | 0.4833 |
| 1.2322 | 16.0 | 124 | 1.2308 | 0.4833 |
| 1.2322 | 16.9 | 131 | 1.2160 | 0.5 |
| 1.22 | 17.94 | 139 | 1.2015 | 0.5 |
| 1.1899 | 18.97 | 147 | 1.2008 | 0.5 |
| 1.1899 | 20.0 | 155 | 1.1606 | 0.5 |
| 1.109 | 20.9 | 162 | 1.1182 | 0.5667 |
| 1.0603 | 21.94 | 170 | 1.0855 | 0.5333 |
| 1.0603 | 22.97 | 178 | 1.0763 | 0.5667 |
| 1.0264 | 24.0 | 186 | 1.1153 | 0.5833 |
| 1.0086 | 24.9 | 193 | 1.0770 | 0.65 |
| 1.0086 | 25.94 | 201 | 1.0041 | 0.6167 |
| 0.9301 | 26.97 | 209 | 0.9637 | 0.6667 |
| 0.9077 | 28.0 | 217 | 0.9824 | 0.5833 |
| 0.9077 | 28.9 | 224 | 0.9485 | 0.6 |
| 0.8725 | 29.94 | 232 | 0.9294 | 0.6167 |
| 0.8203 | 30.97 | 240 | 0.9348 | 0.6167 |
| 0.8203 | 32.0 | 248 | 0.9295 | 0.6 |
| 0.8211 | 32.9 | 255 | 0.9167 | 0.6 |
| 0.8211 | 33.94 | 263 | 0.9281 | 0.5833 |
| 0.7916 | 34.97 | 271 | 0.8803 | 0.6333 |
| 0.7822 | 36.0 | 279 | 0.8785 | 0.6333 |
| 0.7822 | 36.9 | 286 | 0.8906 | 0.6 |
| 0.7937 | 37.94 | 294 | 0.8899 | 0.6 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
sosoai/Hansoldeco-llama3-8b-unsloth-v0.1
|
sosoai
| 2024-04-19T13:41:45Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-04-19T13:30:37Z |
llama3 finetuned with domain dataset
via unsloth method.
|
michaelw37/sc40
|
michaelw37
| 2024-04-19T13:39:33Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-04-19T13:38:03Z |
---
library_name: transformers
tags: []
---
# 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]
|
GladiusTn/llama3_ocr_to_xml_A1
|
GladiusTn
| 2024-04-19T13:36:42Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-04-19T13:30:52Z |
---
library_name: transformers
tags: []
---
# 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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## Uses
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### Direct Use
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
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## Training Details
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|
paloalma/ECE-TW3-JRGL-V1
|
paloalma
| 2024-04-19T13:32:48Z | 4,222 | 1 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"ShinojiResearch/Senku-70B-Full",
"152334H/miqu-1-70b-sf",
"conversational",
"arxiv:2312.06281",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-04-03T20:54:20Z |
---
license: apache-2.0
tags:
- merge
- mergekit
- ShinojiResearch/Senku-70B-Full
- 152334H/miqu-1-70b-sf
---
# ECE-TW3-JRGL-V1
## This model has been produced by :
- [Louis Garcia](https://www.linkedin.com/in/louis-garcia-profil/), engineering student at [French Engineering School ECE](https://www.ece.fr/en/)
- [Matthieu Jollard](https://www.linkedin.com/in/matthieu-jollard/), engineering student at [French Engineering School ECE](https://www.ece.fr/en/)
## Under the supervision of :
- [Andre-Louis Rochet](https://www.linkedin.com/in/andrelouisrochet/), Lecturer at ECE & Co-Founder of [TW3 Partners](https://tw3partners.fr/)
- [Paul Lemaistre](https://www.linkedin.com/in/paul-lemaistre/), CTO of [TW3 Partners](https://tw3partners.fr/)
## With the contribution of :
- ECE engineering school as sponsor and financial contributor
- RunPod as financial contributor
## About ECE
>_**ECE**, a multi-program, multi-campus, and multi-sector engineering school specializing in digital engineering,
> trains engineers and technology experts for the 21st century, capable of meeting the challenges of the dual digital and sustainable development revolutions.
>[French Engineering School ECE](https://www.ece.fr/en/)_
## Description
ECE-TW3-JRGL-V1 is a merge of the following models using **[mergekit](https://github.com/cg123/mergekit)**:
* [ShinojiResearch/Senku-70B-Full](https://huggingface.co/ShinojiResearch/Senku-70B-Full)
* [152334H/miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf)
```yaml
slices:
- sources:
- model: ShinojiResearch/Senku-70B-Full
layer_range: [0, 80]
- model: 152334H/miqu-1-70b-sf
layer_range: [0, 80]
merge_method: slerp
base_model: 152334H/miqu-1-70b-sf
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: float16
```
## Results
- ECE-TW3-JRGL-v1 scores 83.07 on [EQ-Bench V2](https://eqbench.com/index.html)
---
@misc{paech2023eqbench,
title={EQ-Bench: An Emotional Intelligence Benchmark for Large Language Models},
author={Samuel J. Paech},
year={2023},
eprint={2312.06281},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
---
|
Alefiah/UrduSum6
|
Alefiah
| 2024-04-19T13:26:44Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-04-19T13:22:17Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: UrduSum6
results: []
---
<!-- 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. -->
# UrduSum6
This model is a fine-tuned version of [ahmed0189/mT5-Arabic-text-summarization](https://huggingface.co/ahmed0189/mT5-Arabic-text-summarization) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 147 | 3.6410 | 4.6547 | 1.7375 | 4.8048 | 4.8048 | 31.8514 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.13.3
|
LoneStriker/opus-v1.2-llama-3-8b-5.0bpw-h6-exl2
|
LoneStriker
| 2024-04-19T13:25:53Z | 8 | 2 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"unsloth",
"axolotl",
"conversational",
"en",
"license:cc-by-nc-nd-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"5-bit",
"exl2",
"region:us"
] |
text-generation
| 2024-04-19T13:23:27Z |
---
language:
- en
pipeline_tag: text-generation
tags:
- unsloth
- axolotl
license: cc-by-nc-nd-4.0
---
# Llama 3 DreamGen Opus V1
<div style="display: flex; flex-direction: row; align-items: center;">
<img src="/dreamgen/opus-v1.2-llama-3-8b/resolve/main/images/logo-1024.png" alt="model logo" style="
border-radius: 12px;
margin-right: 12px;
margin-top: 0px;
margin-bottom: 0px;
max-width: 100px;
height: auto;
"/>
Models for **(steerable) story-writing and role-playing**.
<br/>[All Opus V1 models, including quants](https://huggingface.co/collections/dreamgen/opus-v1-65d092a6f8ab7fc669111b31).
</div>
## Resources
- [**Opus V1 prompting guide**](https://dreamgen.com/docs/models/opus/v1) with many (interactive) examples and prompts that you can copy.
- [**Google Colab**](https://colab.research.google.com/drive/1J178fH6IdQOXNi-Njgdacf5QgAxsdT20?usp=sharing) for interactive role-play using `opus-v1.2-7b`.
- [Python code](example/prompt/format.py) to format the prompt correctly.
- Join the community on [**Discord**](https://dreamgen.com/discord) to get early access to new models.
<img src="/dreamgen/opus-v1.2-llama-3-8b/resolve/main/images/story_writing.webp" alt="story writing on dreamgen.com" style="
padding: 12px;
border-radius: 12px;
border: 2px solid #f9a8d4;
background: rgb(9, 9, 11);
"/>
## Prompting
<details>
<summary>The models use an extended version of ChatML.</summary>
```
<|im_start|>system
(Story description in the right format here)
(Typically consists of plot description, style description and characters)<|im_end|>
<|im_start|>user
(Your instruction on how the story should continue)<|im_end|>
<|im_start|>text names= Alice
(Continuation of the story from the Alice character)<|im_end|>
<|im_start|>text
(Continuation of the story from no character in particular (pure narration))<|im_end|>
<|im_start|>user
(Your instruction on how the story should continue)<|im_end|>
<|im_start|>text names= Bob
(Continuation of the story from the Bob character)<|im_end|>
```
The Opus V1 extension is the addition of the `text` role, and the addition / modification of role names.
Pay attention to the following:
- The `text` messages can (but don't have to have) `names`, names are used to indicate the "active" character during role-play.
- There can be multiple subsequent message with a `text` role, especially if names are involved.
- There can be multiple names attached to a message.
- The format for names is `names= {{name[0]}}; {{name[1]}}`, beware of the spaces after `names=` and after the `;`. This spacing leads to most natural tokenization for the names.
</details>
While the main goal for the models is great story-writing and role-playing performance, the models are also capable of several writing related tasks as well as general assistance.
Here's how you can prompt the model for the following tasks
### Steerable Story-writing and Role-playing:
- [Story-writing prompt guide](https://dreamgen.com/docs/models/opus/v1#task-story-writing)
- [Role-playing prompt guide](https://dreamgen.com/docs/models/opus/v1#task-role-playing)
- Input:
- System prompt: You provide story / role-play description, which consists of:
- Plot description
- Style description
- Characters and their descriptions
- Conversation turns:
- Text / message turn: This represents part of the story or role play
- Instruction: This tells the model what should happen next
- Output: Continuation of the story / role-play.
### Story plot summarization
- [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-plot-description)
- Input: A story, or a few chapters of a story.
- Output: A description of the story or chapters.
#### Examples:
<details>
<summary>Summary of <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">short fiction story</a> in 50 words</summary>
<blockquote>
Malygris, a powerful magician, summons his long-dead lover Nylissa from the grave using necromancy. However, upon seeing her, he realizes that she is not the same person he once knew, leading him to question the nature of love, memory, and the power of magic.
</blockquote>
</details>
<details>
<summary>Summary of <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">short fiction story</a> in 200 words</summary>
<blockquote>
The story is a dark fantasy set in the city of Susran, where an ancient and powerful magician named Malygris dwells in a tower atop a conical hill. Malygris, who has mastered all forms of sorcery and necromancy, is plagued by a deep sense of ennui and melancholy. He seeks solace in the memory of Nylissa, a girl he once loved in his youth.
One day, Malygris decides to summon Nylissa's spirit from the grave using his necromantic powers. He performs the necessary rituals and incantations, and Nylissa appears before him as a ghostly apparition. However, as Malygris gazes upon her, he becomes increasingly disillusioned. He notices subtle changes in her appearance and behavior that do not match his memories of her. He begins to question whether this is truly the same Nylissa he once knew, or if it is just another illusion conjured by his magic.
As Malygris grapples with this uncertainty, he becomes increasingly despondent and disillusioned with life itself. He realizes that even his magic cannot bring back the past or make him truly happy. In the end, he dismisses Nylissa's spirit and returns to his lonely tower, resigned to his fate as an old and weary sorcerer.
</blockquote>
</details>
<details>
<summary>Summary of <a href="https://edition.cnn.com/2024/02/29/health/pets-diseases-viruses-bacteria-wellness/index.html">CNN News article</a> in 150 words</summary>
<blockquote>
The article discusses the recent human deaths from diseases transmitted by animals, specifically Alaskapox and bubonic plague. Dr. Leana Wen, an emergency physician and former Baltimore health commissioner, explains the various ways household pets can transmit diseases to humans. These include fecal-oral transmission, scratches and bites, and vector-borne diseases like ticks, fleas, and mosquitoes. Wen emphasizes the importance of vaccinating pets, maintaining cleanliness, and taking commonsense precautions to reduce disease transmission. She also advises that individuals who are immunocompromised, babies, and pregnant women should take additional care when interacting with animals.
</blockquote>
</details>
<details>
<summary>Summary of <a href="https://edition.cnn.com/2024/02/29/health/pets-diseases-viruses-bacteria-wellness/index.html">CNN News article</a> in 50 words</summary>
<blockquote>
A doctor discusses the risks of diseases transmitted from animals to humans, focusing on household pets like cats and dogs. They emphasize the importance of vaccination, hygiene, and regular veterinary care to prevent these illnesses.
</blockquote>
</details>
### Story character description
- [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-char-description)
- Input: A story, or a few chapters of a story, set of characters.
- Output: A description of the characters.
#### Examples:
<details>
<summary>Description of the Malygris character from <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">this story</a> in 50 words</summary>
<blockquote>
Malygris is an ancient and powerful magician, characterized by his long white beard, cold green eyes, and a violet robe. He is a master of necromancy and possesses a vast knowledge of forbidden magic. Despite his immense power, he is plagued by a deep sense of melancholy and ennui.
</blockquote>
</details>
<details>
<summary>Description of the prince character from <a href="https://www.libraryofshortstories.com/onlinereader/the-pearl-of-love">this story</a> in 50 words</summary>
<blockquote>
The prince is a devoted and determined individual, driven by his love for his late queen. He is described as a graceful youth in the beginning, but as he grows older, his dedication to the Pearl of Love transforms him into a grave and intent man. His sense of color and design evolves over time, reflecting his growing maturity and artistic vision.
</blockquote>
</details>
### Story style description
- [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-style-description)
- Input: A story, or a few chapters of a story.
- Output: A description the style of the story.
<details>
<summary>Style description of <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">this story</a></summary>
<blockquote>
The story is written in a descriptive and evocative style, with a focus on the atmosphere and setting. The author uses vivid and detailed language to create a sense of place and time, and employs a variety of literary devices such as similes, metaphors, and allusions to enhance the mood and tone of the narrative. The sentence structure is varied, with a mix of short and long sentences that create a rhythmic flow to the prose.
</blockquote>
</details>
<details>
<summary>Style description of <a href="https://www.libraryofshortstories.com/onlinereader/the-pearl-of-love">this story</a></summary>
<blockquote>
The writing style is descriptive and evocative, with a focus on the beauty and grandeur of the Pearl of Love. The author uses vivid imagery and sensory details to create a rich and immersive atmosphere. The tone is reverential and contemplative, reflecting the prince's deep love for his queen and his dedication to creating a lasting monument to her memory.
</blockquote>
</details>
### Story description to chapters
- [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-story-description-to-chapter-descriptions)
- Input: A brief plot description and the desired number of chapters.
- Output: A description for each chapter.
### And more...
## Sampling params
For story-writing and role-play, I recommend "Min P" based sampling with `min_p` in the range `[0.01, 0.1]` and with `temperature` in the range `[0.5, 1.5]`, depending on your preferences. A good starting point would be `min_p=0.1; temperature=0.8`.
You may also benefit from setting presence, frequency and repetition penalties, especially at lower temperatures.
## Dataset
The fine-tuning dataset consisted of ~100M tokens of steerable story-writing, role-playing, writing-assistant and general-assistant examples. Each example was up to 31000 tokens long.
All story-writing and role-playing examples were based on human-written text.
## Running the model
The model is should be compatible with any software that supports the base model, but beware of prompting and tokenization.
I recommend using these model versions:
- 7B: [no quant (opus-v1.2-7b)](https://huggingface.co/dreamgen/opus-v1.2-7b)
- 34B: [no quant (opus-v1-34b)](https://huggingface.co/dreamgen/opus-v1-34b) or [awq (opus-v1-34b-awq)](https://huggingface.co/dreamgen/opus-v1-34b-awq)
- 34B: [no quant (opus-v1.2-70b)](https://huggingface.co/dreamgen/opus-v1.2-70b) or [awq (opus-v1.2-70b-awq)](https://huggingface.co/dreamgen/opus-v1.2-70b-awq)
### Running on DreamGen.com (free)
You can run the models on [dreamgen.com](https://dreamgen.com) for free — you can use the built-in UI for story-writing & role-playing, or use [the API](https://dreamgen.com/docs/api).
### Running Locally
- **Make sure your prompt is as close as possible to the Opus V1**
- Regardless of which backend you use, it's important that you format your prompt well and that the tokenization works correctly.
- [Read the prompt guide](https://dreamgen.com/docs/models/opus/v1)
- [Read the prompt formatting code](example/prompt/format.py)
- Make sure `<|im_start|>` and `<|im_end|>` are tokenized correctly
- **vLLM**
- [**Google Colab**](https://colab.research.google.com/drive/1J178fH6IdQOXNi-Njgdacf5QgAxsdT20?usp=sharing): This is a simple interactive Google Colab to do role-play with the 7B model, it should fit on the T4 GPU.
- [Code](example/prompt/interactive.py): This is simple script for interactive chat for one hard-coded scenario.
- **SillyTavern**
- [Official SillyTavern documentation for DreamGen](https://docs.sillytavern.app/usage/api-connections/dreamgen/) -- applies to both the API an local models
- SillyTavern (staging) comes with built-in DreamGen preset for RP
- Other presets can be found [here](https://huggingface.co/dreamgen/opus-v1.2-llama-3-8b/tree/main/configs/silly_tavern), v2 kindly provided by @MarinaraSpaghetti
- Make sure to unselect `Skip special tokens`, otherwise it won't work
- This is just an attempt at approximating the Opus V1 prompt, it won't be perfect
- Character cards specifically rewritten for the built-in DreamGen preset:
- [Seraphina](configs/silly_tavern/cards/Seraphina.png) (based on the default Seraphina card)
- [Lara Lightland](configs/silly_tavern/cards/LaraLightland.png) (based on the card by Deffcolony)
- **LM Studio**
- [Config](configs/lmstudio/preset.json)
- Just like ChatML, just changed "assistant" to "text" role.
- **There's a bug** in LM Studio if you delete a message or click "Continue", [see here for details](https://discord.com/channels/1110598183144399058/1212665261128417280/1212665261128417280).
- **HuggingFace**
- [Chat template](tokenizer_config.json#L51)
- Just like ChatML, just changed "assistant" to "text" role.
## Known Issues
- **34B repetition**:
- The 34B sometimes gets stuck repeating the same word, or synonyms. This seems to be a common problem across various Yi 34B fine-tunes.
- **GGUF**:
- The tokenization might be messed up. Some users reported that `<|im_start|>` and `<|im_end|>` are tokenized as multiple tokens. Also llama.cpp may not tokenize correctly (the Yi tokenizer is subtly different from the Llama 2 tokenizer).
## License
- This model is intended for personal use only, other use is not permitted.
|
LoneStriker/opus-v1.2-llama-3-8b-4.0bpw-h6-exl2
|
LoneStriker
| 2024-04-19T13:23:22Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"unsloth",
"axolotl",
"conversational",
"en",
"license:cc-by-nc-nd-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"exl2",
"region:us"
] |
text-generation
| 2024-04-19T13:21:13Z |
---
language:
- en
pipeline_tag: text-generation
tags:
- unsloth
- axolotl
license: cc-by-nc-nd-4.0
---
# Llama 3 DreamGen Opus V1
<div style="display: flex; flex-direction: row; align-items: center;">
<img src="/dreamgen/opus-v1.2-llama-3-8b/resolve/main/images/logo-1024.png" alt="model logo" style="
border-radius: 12px;
margin-right: 12px;
margin-top: 0px;
margin-bottom: 0px;
max-width: 100px;
height: auto;
"/>
Models for **(steerable) story-writing and role-playing**.
<br/>[All Opus V1 models, including quants](https://huggingface.co/collections/dreamgen/opus-v1-65d092a6f8ab7fc669111b31).
</div>
## Resources
- [**Opus V1 prompting guide**](https://dreamgen.com/docs/models/opus/v1) with many (interactive) examples and prompts that you can copy.
- [**Google Colab**](https://colab.research.google.com/drive/1J178fH6IdQOXNi-Njgdacf5QgAxsdT20?usp=sharing) for interactive role-play using `opus-v1.2-7b`.
- [Python code](example/prompt/format.py) to format the prompt correctly.
- Join the community on [**Discord**](https://dreamgen.com/discord) to get early access to new models.
<img src="/dreamgen/opus-v1.2-llama-3-8b/resolve/main/images/story_writing.webp" alt="story writing on dreamgen.com" style="
padding: 12px;
border-radius: 12px;
border: 2px solid #f9a8d4;
background: rgb(9, 9, 11);
"/>
## Prompting
<details>
<summary>The models use an extended version of ChatML.</summary>
```
<|im_start|>system
(Story description in the right format here)
(Typically consists of plot description, style description and characters)<|im_end|>
<|im_start|>user
(Your instruction on how the story should continue)<|im_end|>
<|im_start|>text names= Alice
(Continuation of the story from the Alice character)<|im_end|>
<|im_start|>text
(Continuation of the story from no character in particular (pure narration))<|im_end|>
<|im_start|>user
(Your instruction on how the story should continue)<|im_end|>
<|im_start|>text names= Bob
(Continuation of the story from the Bob character)<|im_end|>
```
The Opus V1 extension is the addition of the `text` role, and the addition / modification of role names.
Pay attention to the following:
- The `text` messages can (but don't have to have) `names`, names are used to indicate the "active" character during role-play.
- There can be multiple subsequent message with a `text` role, especially if names are involved.
- There can be multiple names attached to a message.
- The format for names is `names= {{name[0]}}; {{name[1]}}`, beware of the spaces after `names=` and after the `;`. This spacing leads to most natural tokenization for the names.
</details>
While the main goal for the models is great story-writing and role-playing performance, the models are also capable of several writing related tasks as well as general assistance.
Here's how you can prompt the model for the following tasks
### Steerable Story-writing and Role-playing:
- [Story-writing prompt guide](https://dreamgen.com/docs/models/opus/v1#task-story-writing)
- [Role-playing prompt guide](https://dreamgen.com/docs/models/opus/v1#task-role-playing)
- Input:
- System prompt: You provide story / role-play description, which consists of:
- Plot description
- Style description
- Characters and their descriptions
- Conversation turns:
- Text / message turn: This represents part of the story or role play
- Instruction: This tells the model what should happen next
- Output: Continuation of the story / role-play.
### Story plot summarization
- [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-plot-description)
- Input: A story, or a few chapters of a story.
- Output: A description of the story or chapters.
#### Examples:
<details>
<summary>Summary of <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">short fiction story</a> in 50 words</summary>
<blockquote>
Malygris, a powerful magician, summons his long-dead lover Nylissa from the grave using necromancy. However, upon seeing her, he realizes that she is not the same person he once knew, leading him to question the nature of love, memory, and the power of magic.
</blockquote>
</details>
<details>
<summary>Summary of <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">short fiction story</a> in 200 words</summary>
<blockquote>
The story is a dark fantasy set in the city of Susran, where an ancient and powerful magician named Malygris dwells in a tower atop a conical hill. Malygris, who has mastered all forms of sorcery and necromancy, is plagued by a deep sense of ennui and melancholy. He seeks solace in the memory of Nylissa, a girl he once loved in his youth.
One day, Malygris decides to summon Nylissa's spirit from the grave using his necromantic powers. He performs the necessary rituals and incantations, and Nylissa appears before him as a ghostly apparition. However, as Malygris gazes upon her, he becomes increasingly disillusioned. He notices subtle changes in her appearance and behavior that do not match his memories of her. He begins to question whether this is truly the same Nylissa he once knew, or if it is just another illusion conjured by his magic.
As Malygris grapples with this uncertainty, he becomes increasingly despondent and disillusioned with life itself. He realizes that even his magic cannot bring back the past or make him truly happy. In the end, he dismisses Nylissa's spirit and returns to his lonely tower, resigned to his fate as an old and weary sorcerer.
</blockquote>
</details>
<details>
<summary>Summary of <a href="https://edition.cnn.com/2024/02/29/health/pets-diseases-viruses-bacteria-wellness/index.html">CNN News article</a> in 150 words</summary>
<blockquote>
The article discusses the recent human deaths from diseases transmitted by animals, specifically Alaskapox and bubonic plague. Dr. Leana Wen, an emergency physician and former Baltimore health commissioner, explains the various ways household pets can transmit diseases to humans. These include fecal-oral transmission, scratches and bites, and vector-borne diseases like ticks, fleas, and mosquitoes. Wen emphasizes the importance of vaccinating pets, maintaining cleanliness, and taking commonsense precautions to reduce disease transmission. She also advises that individuals who are immunocompromised, babies, and pregnant women should take additional care when interacting with animals.
</blockquote>
</details>
<details>
<summary>Summary of <a href="https://edition.cnn.com/2024/02/29/health/pets-diseases-viruses-bacteria-wellness/index.html">CNN News article</a> in 50 words</summary>
<blockquote>
A doctor discusses the risks of diseases transmitted from animals to humans, focusing on household pets like cats and dogs. They emphasize the importance of vaccination, hygiene, and regular veterinary care to prevent these illnesses.
</blockquote>
</details>
### Story character description
- [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-char-description)
- Input: A story, or a few chapters of a story, set of characters.
- Output: A description of the characters.
#### Examples:
<details>
<summary>Description of the Malygris character from <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">this story</a> in 50 words</summary>
<blockquote>
Malygris is an ancient and powerful magician, characterized by his long white beard, cold green eyes, and a violet robe. He is a master of necromancy and possesses a vast knowledge of forbidden magic. Despite his immense power, he is plagued by a deep sense of melancholy and ennui.
</blockquote>
</details>
<details>
<summary>Description of the prince character from <a href="https://www.libraryofshortstories.com/onlinereader/the-pearl-of-love">this story</a> in 50 words</summary>
<blockquote>
The prince is a devoted and determined individual, driven by his love for his late queen. He is described as a graceful youth in the beginning, but as he grows older, his dedication to the Pearl of Love transforms him into a grave and intent man. His sense of color and design evolves over time, reflecting his growing maturity and artistic vision.
</blockquote>
</details>
### Story style description
- [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-style-description)
- Input: A story, or a few chapters of a story.
- Output: A description the style of the story.
<details>
<summary>Style description of <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">this story</a></summary>
<blockquote>
The story is written in a descriptive and evocative style, with a focus on the atmosphere and setting. The author uses vivid and detailed language to create a sense of place and time, and employs a variety of literary devices such as similes, metaphors, and allusions to enhance the mood and tone of the narrative. The sentence structure is varied, with a mix of short and long sentences that create a rhythmic flow to the prose.
</blockquote>
</details>
<details>
<summary>Style description of <a href="https://www.libraryofshortstories.com/onlinereader/the-pearl-of-love">this story</a></summary>
<blockquote>
The writing style is descriptive and evocative, with a focus on the beauty and grandeur of the Pearl of Love. The author uses vivid imagery and sensory details to create a rich and immersive atmosphere. The tone is reverential and contemplative, reflecting the prince's deep love for his queen and his dedication to creating a lasting monument to her memory.
</blockquote>
</details>
### Story description to chapters
- [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-story-description-to-chapter-descriptions)
- Input: A brief plot description and the desired number of chapters.
- Output: A description for each chapter.
### And more...
## Sampling params
For story-writing and role-play, I recommend "Min P" based sampling with `min_p` in the range `[0.01, 0.1]` and with `temperature` in the range `[0.5, 1.5]`, depending on your preferences. A good starting point would be `min_p=0.1; temperature=0.8`.
You may also benefit from setting presence, frequency and repetition penalties, especially at lower temperatures.
## Dataset
The fine-tuning dataset consisted of ~100M tokens of steerable story-writing, role-playing, writing-assistant and general-assistant examples. Each example was up to 31000 tokens long.
All story-writing and role-playing examples were based on human-written text.
## Running the model
The model is should be compatible with any software that supports the base model, but beware of prompting and tokenization.
I recommend using these model versions:
- 7B: [no quant (opus-v1.2-7b)](https://huggingface.co/dreamgen/opus-v1.2-7b)
- 34B: [no quant (opus-v1-34b)](https://huggingface.co/dreamgen/opus-v1-34b) or [awq (opus-v1-34b-awq)](https://huggingface.co/dreamgen/opus-v1-34b-awq)
- 34B: [no quant (opus-v1.2-70b)](https://huggingface.co/dreamgen/opus-v1.2-70b) or [awq (opus-v1.2-70b-awq)](https://huggingface.co/dreamgen/opus-v1.2-70b-awq)
### Running on DreamGen.com (free)
You can run the models on [dreamgen.com](https://dreamgen.com) for free — you can use the built-in UI for story-writing & role-playing, or use [the API](https://dreamgen.com/docs/api).
### Running Locally
- **Make sure your prompt is as close as possible to the Opus V1**
- Regardless of which backend you use, it's important that you format your prompt well and that the tokenization works correctly.
- [Read the prompt guide](https://dreamgen.com/docs/models/opus/v1)
- [Read the prompt formatting code](example/prompt/format.py)
- Make sure `<|im_start|>` and `<|im_end|>` are tokenized correctly
- **vLLM**
- [**Google Colab**](https://colab.research.google.com/drive/1J178fH6IdQOXNi-Njgdacf5QgAxsdT20?usp=sharing): This is a simple interactive Google Colab to do role-play with the 7B model, it should fit on the T4 GPU.
- [Code](example/prompt/interactive.py): This is simple script for interactive chat for one hard-coded scenario.
- **SillyTavern**
- [Official SillyTavern documentation for DreamGen](https://docs.sillytavern.app/usage/api-connections/dreamgen/) -- applies to both the API an local models
- SillyTavern (staging) comes with built-in DreamGen preset for RP
- Other presets can be found [here](https://huggingface.co/dreamgen/opus-v1.2-llama-3-8b/tree/main/configs/silly_tavern), v2 kindly provided by @MarinaraSpaghetti
- Make sure to unselect `Skip special tokens`, otherwise it won't work
- This is just an attempt at approximating the Opus V1 prompt, it won't be perfect
- Character cards specifically rewritten for the built-in DreamGen preset:
- [Seraphina](configs/silly_tavern/cards/Seraphina.png) (based on the default Seraphina card)
- [Lara Lightland](configs/silly_tavern/cards/LaraLightland.png) (based on the card by Deffcolony)
- **LM Studio**
- [Config](configs/lmstudio/preset.json)
- Just like ChatML, just changed "assistant" to "text" role.
- **There's a bug** in LM Studio if you delete a message or click "Continue", [see here for details](https://discord.com/channels/1110598183144399058/1212665261128417280/1212665261128417280).
- **HuggingFace**
- [Chat template](tokenizer_config.json#L51)
- Just like ChatML, just changed "assistant" to "text" role.
## Known Issues
- **34B repetition**:
- The 34B sometimes gets stuck repeating the same word, or synonyms. This seems to be a common problem across various Yi 34B fine-tunes.
- **GGUF**:
- The tokenization might be messed up. Some users reported that `<|im_start|>` and `<|im_end|>` are tokenized as multiple tokens. Also llama.cpp may not tokenize correctly (the Yi tokenizer is subtly different from the Llama 2 tokenizer).
## License
- This model is intended for personal use only, other use is not permitted.
|
yujiepan/falcon-tiny-random
|
yujiepan
| 2024-04-19T13:22:24Z | 170 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"falcon",
"text-generation",
"custom_code",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-27T17:32:14Z |
---
library_name: transformers
pipeline_tag: text-generation
inference: true
widget:
- text: Hello!
example_title: Hello world
group: Python
---
This model is randomly initialized, using the config from [https://huggingface.co/tiiuae/falcon-7b-instruct] but with smaller size.
Note the model is in float16.
Codes:
```python
import transformers
from optimum.intel.openvino import OVModelForCausalLM
import torch
import os
from huggingface_hub import create_repo, upload_folder
source_model_id = 'tiiuae/falcon-7b-instruct'
save_path = '/tmp/yujiepan/falcon-tiny-random'
repo_id = 'yujiepan/falcon-tiny-random'
config = transformers.AutoConfig.from_pretrained(
source_model_id, trust_remote_code=True)
config.hidden_size = 8
config.num_attention_heads = 2
config.num_hidden_layers = 2
config.torch_dtype = torch.float16
model = transformers.AutoModelForCausalLM.from_config(
config, trust_remote_code=True)
model = model.half()
model.save_pretrained(save_path)
tokenizer = transformers.AutoTokenizer.from_pretrained(
source_model_id, trust_remote_code=True)
tokenizer.save_pretrained(save_path)
# current not supported, might add this later
# ovmodel = OVModelForCausalLM.from_pretrained(
# save_path, export=True, trust_remote_code=True)
# ovmodel.save_pretrained(save_path)
os.system(f'ls -alh {save_path}')
create_repo(repo_id, exist_ok=True)
upload_folder(repo_id=repo_id, folder_path=save_path)
```
|
yujiepan/chatglm3-tiny-random
|
yujiepan
| 2024-04-19T13:22:22Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"chatglm",
"feature-extraction",
"text-generation",
"conversational",
"custom_code",
"region:us"
] |
text-generation
| 2024-03-30T15:46:40Z |
---
library_name: transformers
pipeline_tag: text-generation
inference: true
widget:
- text: Hello!
example_title: Hello world
group: Python
---
This model is randomly initialized, using the config from [THUDM/chatglm3-6b-128k](https://huggingface.co/THUDM/chatglm3-6b-128k/blob/main/config.json) but with smaller size.
Note the model is in float16.
Codes:
```python
import transformers
import torch
import os
from huggingface_hub import create_repo, upload_folder
source_model_id = 'THUDM/chatglm3-6b-128k'
tiny_random_name = 'chatglm3-tiny-random'
save_path = f'/tmp/yujiepan/{tiny_random_name}'
repo_id = f'yujiepan/{tiny_random_name}'
config = transformers.AutoConfig.from_pretrained(
source_model_id, trust_remote_code=True)
config.hidden_size = 4
config.ffn_hidden_size = 6
config.num_attention_heads = 4
config.kv_channels = 2
config.num_layers = 2
config.torch_dtype = torch.float16
model = transformers.AutoModelForCausalLM.from_config(
config, trust_remote_code=True, torch_dtype=torch.float16)
model = model.half()
tokenizer = transformers.AutoTokenizer.from_pretrained(
source_model_id, trust_remote_code=True)
# result = transformers.pipelines.pipeline(
# 'text-generation',
# model=model, tokenizer=tokenizer,
# device=0,
# max_new_tokens=16,
# )('Hello')
# print(result)
model = model.cuda()
response, history = model.chat(tokenizer, "Hi", history=[], max_length=32)
print(response)
model.save_pretrained(save_path)
tokenizer.save_pretrained(save_path)
os.system(f'ls -alh {save_path}')
create_repo(repo_id, exist_ok=True)
upload_folder(repo_id=repo_id, folder_path=save_path)
```
|
yujiepan/llama-2-tiny-3layers-random
|
yujiepan
| 2024-04-19T13:22:12Z | 139 | 1 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"openvino",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-31T09:30:50Z |
---
library_name: transformers
pipeline_tag: text-generation
inference: true
widget:
- text: Hello!
example_title: Hello world
group: Python
---
# yujiepan/llama-2-tiny-3layers-random
This model is **randomly initialized**, using the config from [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/yujiepan/llama-2-tiny-3layers-random/blob/main/config.json) but with the following modifications:
```json
{
"hidden_size": 8,
"intermediate_size": 32,
"num_attention_heads": 2,
"num_hidden_layers": 3,
"num_key_value_heads": 2,
}
```
|
yujiepan/llama-2-tiny-random
|
yujiepan
| 2024-04-19T13:22:10Z | 2,541 | 1 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"openvino",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-21T05:31:01Z |
---
library_name: transformers
pipeline_tag: text-generation
inference: true
widget:
- text: Hello!
example_title: Hello world
group: Python
---
# yujiepan/llama-2-tiny-random
This model is **randomly initialized**, using the config from [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/yujiepan/llama-2-tiny-random/blob/main/config.json) but with the following modifications:
```json
{
"hidden_size": 8,
"intermediate_size": 32,
"num_attention_heads": 2,
"num_hidden_layers": 1,
"num_key_value_heads": 2,
}
```
|
spietari/Reinforce-Pixelcopter-PLE-v0
|
spietari
| 2024-04-19T13:20:05Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-04-18T15:31:11Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 103.20 +/- 55.03
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
mayacinka/OkapiLlama-3-dpo
|
mayacinka
| 2024-04-19T13:16:45Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"dpo",
"conversational",
"en",
"dataset:mlabonne/orpo-dpo-mix-40k",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-04-19T13:06:52Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- dpo
base_model: unsloth/llama-3-8b-bnb-4bit
datasets:
- mlabonne/orpo-dpo-mix-40k
---
# Uploaded model
- **Developed by:** mayacinka
- **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)
|
birdy654/CHECK_P_MISTRAL
|
birdy654
| 2024-04-19T13:16:37Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2",
"region:us"
] | null | 2024-04-19T13:13:15Z |
---
library_name: peft
base_model: mistralai/Mistral-7B-Instruct-v0.2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.10.0
|
OwOOwO/dumbo-krillin103
|
OwOOwO
| 2024-04-19T13:11:45Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-04-19T13:08:42Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
DandinPower/deberta-v3-base-cotat
|
DandinPower
| 2024-04-19T13:11:34Z | 12 | 0 |
transformers
|
[
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"nycu-112-2-datamining-hw2",
"generated_from_trainer",
"en",
"dataset:DandinPower/review_cleanonlytitleandtext",
"base_model:microsoft/deberta-v3-base",
"base_model:finetune:microsoft/deberta-v3-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-04-19T12:42:34Z |
---
language:
- en
license: mit
base_model: microsoft/deberta-v3-base
tags:
- nycu-112-2-datamining-hw2
- generated_from_trainer
datasets:
- DandinPower/review_cleanonlytitleandtext
metrics:
- accuracy
model-index:
- name: deberta-v3-base-cotat
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: DandinPower/review_cleanonlytitleandtext
type: DandinPower/review_cleanonlytitleandtext
metrics:
- name: Accuracy
type: accuracy
value: 0.623
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-v3-base-cotat
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the DandinPower/review_cleanonlytitleandtext dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4985
- Accuracy: 0.623
- Macro F1: 0.6247
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 1.0223 | 0.14 | 500 | 0.9610 | 0.592 | 0.5971 |
| 1.0108 | 0.29 | 1000 | 0.9378 | 0.6044 | 0.6083 |
| 0.9323 | 0.43 | 1500 | 0.9605 | 0.589 | 0.5652 |
| 0.9651 | 0.57 | 2000 | 0.9845 | 0.5797 | 0.5687 |
| 0.928 | 0.71 | 2500 | 0.9521 | 0.5907 | 0.5656 |
| 0.9205 | 0.86 | 3000 | 0.9073 | 0.603 | 0.5740 |
| 0.9243 | 1.0 | 3500 | 0.8876 | 0.616 | 0.6113 |
| 0.8545 | 1.14 | 4000 | 0.8631 | 0.6267 | 0.6290 |
| 0.8267 | 1.29 | 4500 | 0.8908 | 0.624 | 0.6185 |
| 0.8175 | 1.43 | 5000 | 0.8771 | 0.6173 | 0.6222 |
| 0.8613 | 1.57 | 5500 | 0.9564 | 0.6209 | 0.6081 |
| 0.8138 | 1.71 | 6000 | 0.9246 | 0.6089 | 0.6063 |
| 0.7314 | 1.86 | 6500 | 0.9030 | 0.6329 | 0.6313 |
| 0.8287 | 2.0 | 7000 | 0.8753 | 0.6211 | 0.6235 |
| 0.6963 | 2.14 | 7500 | 0.9700 | 0.6247 | 0.6257 |
| 0.7034 | 2.29 | 8000 | 0.9592 | 0.6234 | 0.6220 |
| 0.679 | 2.43 | 8500 | 0.8994 | 0.6233 | 0.6272 |
| 0.7207 | 2.57 | 9000 | 1.0013 | 0.6236 | 0.6183 |
| 0.6992 | 2.71 | 9500 | 0.9385 | 0.6169 | 0.6219 |
| 0.7032 | 2.86 | 10000 | 0.9247 | 0.6366 | 0.6364 |
| 0.6949 | 3.0 | 10500 | 0.9615 | 0.6239 | 0.6281 |
| 0.5581 | 3.14 | 11000 | 1.0439 | 0.6217 | 0.6267 |
| 0.55 | 3.29 | 11500 | 1.1205 | 0.6259 | 0.6232 |
| 0.5496 | 3.43 | 12000 | 1.1122 | 0.6226 | 0.6267 |
| 0.5462 | 3.57 | 12500 | 1.0692 | 0.6251 | 0.6263 |
| 0.5121 | 3.71 | 13000 | 1.1563 | 0.6197 | 0.6214 |
| 0.531 | 3.86 | 13500 | 1.1123 | 0.6261 | 0.6256 |
| 0.5256 | 4.0 | 14000 | 1.1194 | 0.6247 | 0.6264 |
| 0.3908 | 4.14 | 14500 | 1.3631 | 0.6204 | 0.6210 |
| 0.4439 | 4.29 | 15000 | 1.4810 | 0.6204 | 0.6211 |
| 0.4252 | 4.43 | 15500 | 1.4454 | 0.6211 | 0.6217 |
| 0.3721 | 4.57 | 16000 | 1.5315 | 0.6204 | 0.6231 |
| 0.369 | 4.71 | 16500 | 1.4797 | 0.6184 | 0.6190 |
| 0.3907 | 4.86 | 17000 | 1.4857 | 0.6219 | 0.6234 |
| 0.4022 | 5.0 | 17500 | 1.4985 | 0.623 | 0.6247 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
LoneStriker/opus-v1.2-llama-3-8b-GGUF
|
LoneStriker
| 2024-04-19T13:11:11Z | 86 | 14 | null |
[
"gguf",
"unsloth",
"axolotl",
"text-generation",
"en",
"license:cc-by-nc-nd-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-04-19T12:59:08Z |
---
language:
- en
pipeline_tag: text-generation
tags:
- unsloth
- axolotl
license: cc-by-nc-nd-4.0
---
# Llama 3 DreamGen Opus V1
<div style="display: flex; flex-direction: row; align-items: center;">
<img src="/dreamgen/opus-v1.2-llama-3-8b/resolve/main/images/logo-1024.png" alt="model logo" style="
border-radius: 12px;
margin-right: 12px;
margin-top: 0px;
margin-bottom: 0px;
max-width: 100px;
height: auto;
"/>
Models for **(steerable) story-writing and role-playing**.
<br/>[All Opus V1 models, including quants](https://huggingface.co/collections/dreamgen/opus-v1-65d092a6f8ab7fc669111b31).
</div>
## Resources
- [**Opus V1 prompting guide**](https://dreamgen.com/docs/models/opus/v1) with many (interactive) examples and prompts that you can copy.
- [**Google Colab**](https://colab.research.google.com/drive/1J178fH6IdQOXNi-Njgdacf5QgAxsdT20?usp=sharing) for interactive role-play using `opus-v1.2-7b`.
- [Python code](example/prompt/format.py) to format the prompt correctly.
- Join the community on [**Discord**](https://dreamgen.com/discord) to get early access to new models.
<img src="/dreamgen/opus-v1.2-llama-3-8b/resolve/main/images/story_writing.webp" alt="story writing on dreamgen.com" style="
padding: 12px;
border-radius: 12px;
border: 2px solid #f9a8d4;
background: rgb(9, 9, 11);
"/>
## Prompting
<details>
<summary>The models use an extended version of ChatML.</summary>
```
<|im_start|>system
(Story description in the right format here)
(Typically consists of plot description, style description and characters)<|im_end|>
<|im_start|>user
(Your instruction on how the story should continue)<|im_end|>
<|im_start|>text names= Alice
(Continuation of the story from the Alice character)<|im_end|>
<|im_start|>text
(Continuation of the story from no character in particular (pure narration))<|im_end|>
<|im_start|>user
(Your instruction on how the story should continue)<|im_end|>
<|im_start|>text names= Bob
(Continuation of the story from the Bob character)<|im_end|>
```
The Opus V1 extension is the addition of the `text` role, and the addition / modification of role names.
Pay attention to the following:
- The `text` messages can (but don't have to have) `names`, names are used to indicate the "active" character during role-play.
- There can be multiple subsequent message with a `text` role, especially if names are involved.
- There can be multiple names attached to a message.
- The format for names is `names= {{name[0]}}; {{name[1]}}`, beware of the spaces after `names=` and after the `;`. This spacing leads to most natural tokenization for the names.
</details>
While the main goal for the models is great story-writing and role-playing performance, the models are also capable of several writing related tasks as well as general assistance.
Here's how you can prompt the model for the following tasks
### Steerable Story-writing and Role-playing:
- [Story-writing prompt guide](https://dreamgen.com/docs/models/opus/v1#task-story-writing)
- [Role-playing prompt guide](https://dreamgen.com/docs/models/opus/v1#task-role-playing)
- Input:
- System prompt: You provide story / role-play description, which consists of:
- Plot description
- Style description
- Characters and their descriptions
- Conversation turns:
- Text / message turn: This represents part of the story or role play
- Instruction: This tells the model what should happen next
- Output: Continuation of the story / role-play.
### Story plot summarization
- [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-plot-description)
- Input: A story, or a few chapters of a story.
- Output: A description of the story or chapters.
#### Examples:
<details>
<summary>Summary of <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">short fiction story</a> in 50 words</summary>
<blockquote>
Malygris, a powerful magician, summons his long-dead lover Nylissa from the grave using necromancy. However, upon seeing her, he realizes that she is not the same person he once knew, leading him to question the nature of love, memory, and the power of magic.
</blockquote>
</details>
<details>
<summary>Summary of <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">short fiction story</a> in 200 words</summary>
<blockquote>
The story is a dark fantasy set in the city of Susran, where an ancient and powerful magician named Malygris dwells in a tower atop a conical hill. Malygris, who has mastered all forms of sorcery and necromancy, is plagued by a deep sense of ennui and melancholy. He seeks solace in the memory of Nylissa, a girl he once loved in his youth.
One day, Malygris decides to summon Nylissa's spirit from the grave using his necromantic powers. He performs the necessary rituals and incantations, and Nylissa appears before him as a ghostly apparition. However, as Malygris gazes upon her, he becomes increasingly disillusioned. He notices subtle changes in her appearance and behavior that do not match his memories of her. He begins to question whether this is truly the same Nylissa he once knew, or if it is just another illusion conjured by his magic.
As Malygris grapples with this uncertainty, he becomes increasingly despondent and disillusioned with life itself. He realizes that even his magic cannot bring back the past or make him truly happy. In the end, he dismisses Nylissa's spirit and returns to his lonely tower, resigned to his fate as an old and weary sorcerer.
</blockquote>
</details>
<details>
<summary>Summary of <a href="https://edition.cnn.com/2024/02/29/health/pets-diseases-viruses-bacteria-wellness/index.html">CNN News article</a> in 150 words</summary>
<blockquote>
The article discusses the recent human deaths from diseases transmitted by animals, specifically Alaskapox and bubonic plague. Dr. Leana Wen, an emergency physician and former Baltimore health commissioner, explains the various ways household pets can transmit diseases to humans. These include fecal-oral transmission, scratches and bites, and vector-borne diseases like ticks, fleas, and mosquitoes. Wen emphasizes the importance of vaccinating pets, maintaining cleanliness, and taking commonsense precautions to reduce disease transmission. She also advises that individuals who are immunocompromised, babies, and pregnant women should take additional care when interacting with animals.
</blockquote>
</details>
<details>
<summary>Summary of <a href="https://edition.cnn.com/2024/02/29/health/pets-diseases-viruses-bacteria-wellness/index.html">CNN News article</a> in 50 words</summary>
<blockquote>
A doctor discusses the risks of diseases transmitted from animals to humans, focusing on household pets like cats and dogs. They emphasize the importance of vaccination, hygiene, and regular veterinary care to prevent these illnesses.
</blockquote>
</details>
### Story character description
- [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-char-description)
- Input: A story, or a few chapters of a story, set of characters.
- Output: A description of the characters.
#### Examples:
<details>
<summary>Description of the Malygris character from <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">this story</a> in 50 words</summary>
<blockquote>
Malygris is an ancient and powerful magician, characterized by his long white beard, cold green eyes, and a violet robe. He is a master of necromancy and possesses a vast knowledge of forbidden magic. Despite his immense power, he is plagued by a deep sense of melancholy and ennui.
</blockquote>
</details>
<details>
<summary>Description of the prince character from <a href="https://www.libraryofshortstories.com/onlinereader/the-pearl-of-love">this story</a> in 50 words</summary>
<blockquote>
The prince is a devoted and determined individual, driven by his love for his late queen. He is described as a graceful youth in the beginning, but as he grows older, his dedication to the Pearl of Love transforms him into a grave and intent man. His sense of color and design evolves over time, reflecting his growing maturity and artistic vision.
</blockquote>
</details>
### Story style description
- [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-style-description)
- Input: A story, or a few chapters of a story.
- Output: A description the style of the story.
<details>
<summary>Style description of <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">this story</a></summary>
<blockquote>
The story is written in a descriptive and evocative style, with a focus on the atmosphere and setting. The author uses vivid and detailed language to create a sense of place and time, and employs a variety of literary devices such as similes, metaphors, and allusions to enhance the mood and tone of the narrative. The sentence structure is varied, with a mix of short and long sentences that create a rhythmic flow to the prose.
</blockquote>
</details>
<details>
<summary>Style description of <a href="https://www.libraryofshortstories.com/onlinereader/the-pearl-of-love">this story</a></summary>
<blockquote>
The writing style is descriptive and evocative, with a focus on the beauty and grandeur of the Pearl of Love. The author uses vivid imagery and sensory details to create a rich and immersive atmosphere. The tone is reverential and contemplative, reflecting the prince's deep love for his queen and his dedication to creating a lasting monument to her memory.
</blockquote>
</details>
### Story description to chapters
- [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-story-description-to-chapter-descriptions)
- Input: A brief plot description and the desired number of chapters.
- Output: A description for each chapter.
### And more...
## Sampling params
For story-writing and role-play, I recommend "Min P" based sampling with `min_p` in the range `[0.01, 0.1]` and with `temperature` in the range `[0.5, 1.5]`, depending on your preferences. A good starting point would be `min_p=0.1; temperature=0.8`.
You may also benefit from setting presence, frequency and repetition penalties, especially at lower temperatures.
## Dataset
The fine-tuning dataset consisted of ~100M tokens of steerable story-writing, role-playing, writing-assistant and general-assistant examples. Each example was up to 31000 tokens long.
All story-writing and role-playing examples were based on human-written text.
## Running the model
The model is should be compatible with any software that supports the base model, but beware of prompting and tokenization.
I recommend using these model versions:
- 7B: [no quant (opus-v1.2-7b)](https://huggingface.co/dreamgen/opus-v1.2-7b)
- 34B: [no quant (opus-v1-34b)](https://huggingface.co/dreamgen/opus-v1-34b) or [awq (opus-v1-34b-awq)](https://huggingface.co/dreamgen/opus-v1-34b-awq)
- 34B: [no quant (opus-v1.2-70b)](https://huggingface.co/dreamgen/opus-v1.2-70b) or [awq (opus-v1.2-70b-awq)](https://huggingface.co/dreamgen/opus-v1.2-70b-awq)
### Running on DreamGen.com (free)
You can run the models on [dreamgen.com](https://dreamgen.com) for free — you can use the built-in UI for story-writing & role-playing, or use [the API](https://dreamgen.com/docs/api).
### Running Locally
- **Make sure your prompt is as close as possible to the Opus V1**
- Regardless of which backend you use, it's important that you format your prompt well and that the tokenization works correctly.
- [Read the prompt guide](https://dreamgen.com/docs/models/opus/v1)
- [Read the prompt formatting code](example/prompt/format.py)
- Make sure `<|im_start|>` and `<|im_end|>` are tokenized correctly
- **vLLM**
- [**Google Colab**](https://colab.research.google.com/drive/1J178fH6IdQOXNi-Njgdacf5QgAxsdT20?usp=sharing): This is a simple interactive Google Colab to do role-play with the 7B model, it should fit on the T4 GPU.
- [Code](example/prompt/interactive.py): This is simple script for interactive chat for one hard-coded scenario.
- **SillyTavern**
- [Official SillyTavern documentation for DreamGen](https://docs.sillytavern.app/usage/api-connections/dreamgen/) -- applies to both the API an local models
- SillyTavern (staging) comes with built-in DreamGen preset for RP
- Other presets can be found [here](https://huggingface.co/dreamgen/opus-v1.2-llama-3-8b/tree/main/configs/silly_tavern), v2 kindly provided by @MarinaraSpaghetti
- Make sure to unselect `Skip special tokens`, otherwise it won't work
- This is just an attempt at approximating the Opus V1 prompt, it won't be perfect
- Character cards specifically rewritten for the built-in DreamGen preset:
- [Seraphina](configs/silly_tavern/cards/Seraphina.png) (based on the default Seraphina card)
- [Lara Lightland](configs/silly_tavern/cards/LaraLightland.png) (based on the card by Deffcolony)
- **LM Studio**
- [Config](configs/lmstudio/preset.json)
- Just like ChatML, just changed "assistant" to "text" role.
- **There's a bug** in LM Studio if you delete a message or click "Continue", [see here for details](https://discord.com/channels/1110598183144399058/1212665261128417280/1212665261128417280).
- **HuggingFace**
- [Chat template](tokenizer_config.json#L51)
- Just like ChatML, just changed "assistant" to "text" role.
## Known Issues
- **34B repetition**:
- The 34B sometimes gets stuck repeating the same word, or synonyms. This seems to be a common problem across various Yi 34B fine-tunes.
- **GGUF**:
- The tokenization might be messed up. Some users reported that `<|im_start|>` and `<|im_end|>` are tokenized as multiple tokens. Also llama.cpp may not tokenize correctly (the Yi tokenizer is subtly different from the Llama 2 tokenizer).
## License
- This model is intended for personal use only, other use is not permitted.
|
numen-tech/Meta-Llama-3-8B-Instruct-w4a16g128asym
|
numen-tech
| 2024-04-19T13:08:47Z | 0 | 1 | null |
[
"arxiv:2308.13137",
"license:other",
"region:us"
] | null | 2024-04-19T13:06:04Z |
---
license: other
license_name: llama3
license_link: LICENSE
---
4-bit [OmniQuant](https://arxiv.org/abs/2308.13137) quantized version of [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct).
|
LendeaViva/bert-base-pundec
|
LendeaViva
| 2024-04-19T13:08:36Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:neuralmind/bert-base-portuguese-cased",
"base_model:finetune:neuralmind/bert-base-portuguese-cased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-04-19T13:08:18Z |
---
license: mit
base_model: neuralmind/bert-base-portuguese-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bertimbau_base_pos_neg_2
results: []
---
<!-- 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. -->
# bertimbau_base_pos_neg_2
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1013
- Precision: 0.5365
- Recall: 0.5216
- F1: 0.5290
- Accuracy: 0.9539
## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0962 | 2.0040 | 500 | 0.1122 | 0.5457 | 0.5228 | 0.5340 | 0.9526 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1
|
mani-a-i/mistral-7b-v2-ckpt-2400
|
mani-a-i
| 2024-04-19T13:06:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T13:03:56Z |
---
library_name: transformers
tags: []
---
# 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]
|
NikolayKozloff/Mistral-7B-v0.1-capybara-orpo-en-de-GGUF
|
NikolayKozloff
| 2024-04-19T13:05:04Z | 4 | 1 | null |
[
"gguf",
"alignment-handbook",
"generated_from_trainer",
"llama-cpp",
"gguf-my-repo",
"dataset:maxidl/distilabel-capybara-dpo-7k-binarized_en_de",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:quantized:mistralai/Mistral-7B-v0.1",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-19T13:04:37Z |
---
tags:
- alignment-handbook
- generated_from_trainer
- llama-cpp
- gguf-my-repo
base_model: mistralai/Mistral-7B-v0.1
datasets:
- maxidl/distilabel-capybara-dpo-7k-binarized_en_de
model-index:
- name: Mistral-7B-v0.1-capybara-orpo-en-de
results: []
---
# NikolayKozloff/Mistral-7B-v0.1-capybara-orpo-en-de-Q8_0-GGUF
This model was converted to GGUF format from [`maxidl/Mistral-7B-v0.1-capybara-orpo-en-de`](https://huggingface.co/maxidl/Mistral-7B-v0.1-capybara-orpo-en-de) 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/maxidl/Mistral-7B-v0.1-capybara-orpo-en-de) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo NikolayKozloff/Mistral-7B-v0.1-capybara-orpo-en-de-Q8_0-GGUF --model mistral-7b-v0.1-capybara-orpo-en-de.Q8_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo NikolayKozloff/Mistral-7B-v0.1-capybara-orpo-en-de-Q8_0-GGUF --model mistral-7b-v0.1-capybara-orpo-en-de.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 mistral-7b-v0.1-capybara-orpo-en-de.Q8_0.gguf -n 128
```
|
himanshue2e/gemma-2b-g
|
himanshue2e
| 2024-04-19T13:03:26Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"license:gemma",
"region:us"
] | null | 2024-04-18T13:00:38Z |
---
license: gemma
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: google/gemma-2b
model-index:
- name: gemma-2b-g
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gemma-2b-g
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: 0.9563
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.016 | 2 | 0.9410 |
| No log | 0.032 | 4 | 0.9443 |
| No log | 0.048 | 6 | 0.9413 |
| No log | 0.064 | 8 | 0.9398 |
| No log | 0.08 | 10 | 0.9401 |
| No log | 0.096 | 12 | 0.9406 |
| No log | 0.112 | 14 | 0.9404 |
| No log | 0.128 | 16 | 0.9409 |
| No log | 0.144 | 18 | 0.9412 |
| No log | 0.16 | 20 | 0.9412 |
| No log | 0.176 | 22 | 0.9411 |
| No log | 0.192 | 24 | 0.9408 |
| No log | 0.208 | 26 | 0.9412 |
| No log | 0.224 | 28 | 0.9411 |
| No log | 0.24 | 30 | 0.9408 |
| No log | 0.256 | 32 | 0.9406 |
| No log | 0.272 | 34 | 0.9404 |
| No log | 0.288 | 36 | 0.9406 |
| No log | 0.304 | 38 | 0.9409 |
| No log | 0.32 | 40 | 0.9414 |
| No log | 0.336 | 42 | 0.9419 |
| No log | 0.352 | 44 | 0.9425 |
| No log | 0.368 | 46 | 0.9425 |
| No log | 0.384 | 48 | 0.9416 |
| No log | 0.4 | 50 | 0.9408 |
| No log | 0.416 | 52 | 0.9403 |
| No log | 0.432 | 54 | 0.9398 |
| No log | 0.448 | 56 | 0.9393 |
| No log | 0.464 | 58 | 0.9385 |
| No log | 0.48 | 60 | 0.9390 |
| No log | 0.496 | 62 | 0.9394 |
| No log | 0.512 | 64 | 0.9392 |
| No log | 0.528 | 66 | 0.9386 |
| No log | 0.544 | 68 | 0.9385 |
| No log | 0.56 | 70 | 0.9380 |
| No log | 0.576 | 72 | 0.9373 |
| No log | 0.592 | 74 | 0.9369 |
| No log | 0.608 | 76 | 0.9367 |
| No log | 0.624 | 78 | 0.9369 |
| No log | 0.64 | 80 | 0.9370 |
| No log | 0.656 | 82 | 0.9371 |
| No log | 0.672 | 84 | 0.9366 |
| No log | 0.688 | 86 | 0.9361 |
| No log | 0.704 | 88 | 0.9361 |
| No log | 0.72 | 90 | 0.9354 |
| No log | 0.736 | 92 | 0.9352 |
| No log | 0.752 | 94 | 0.9354 |
| No log | 0.768 | 96 | 0.9352 |
| No log | 0.784 | 98 | 0.9350 |
| No log | 0.8 | 100 | 0.9349 |
| No log | 0.816 | 102 | 0.9353 |
| No log | 0.832 | 104 | 0.9349 |
| No log | 0.848 | 106 | 0.9346 |
| No log | 0.864 | 108 | 0.9341 |
| No log | 0.88 | 110 | 0.9335 |
| No log | 0.896 | 112 | 0.9327 |
| No log | 0.912 | 114 | 0.9321 |
| No log | 0.928 | 116 | 0.9323 |
| No log | 0.944 | 118 | 0.9327 |
| No log | 0.96 | 120 | 0.9325 |
| No log | 0.976 | 122 | 0.9318 |
| No log | 0.992 | 124 | 0.9316 |
| No log | 1.008 | 126 | 0.9321 |
| No log | 1.024 | 128 | 0.9332 |
| No log | 1.04 | 130 | 0.9351 |
| No log | 1.056 | 132 | 0.9370 |
| No log | 1.072 | 134 | 0.9383 |
| No log | 1.088 | 136 | 0.9390 |
| No log | 1.104 | 138 | 0.9386 |
| No log | 1.12 | 140 | 0.9378 |
| No log | 1.1360 | 142 | 0.9375 |
| No log | 1.152 | 144 | 0.9380 |
| No log | 1.168 | 146 | 0.9380 |
| No log | 1.184 | 148 | 0.9376 |
| No log | 1.2 | 150 | 0.9381 |
| No log | 1.216 | 152 | 0.9390 |
| No log | 1.232 | 154 | 0.9400 |
| No log | 1.248 | 156 | 0.9410 |
| No log | 1.264 | 158 | 0.9411 |
| No log | 1.28 | 160 | 0.9405 |
| No log | 1.296 | 162 | 0.9402 |
| No log | 1.312 | 164 | 0.9400 |
| No log | 1.328 | 166 | 0.9399 |
| No log | 1.3440 | 168 | 0.9397 |
| No log | 1.3600 | 170 | 0.9398 |
| No log | 1.376 | 172 | 0.9403 |
| No log | 1.392 | 174 | 0.9412 |
| No log | 1.408 | 176 | 0.9424 |
| No log | 1.424 | 178 | 0.9432 |
| No log | 1.44 | 180 | 0.9417 |
| No log | 1.456 | 182 | 0.9403 |
| No log | 1.472 | 184 | 0.9397 |
| No log | 1.488 | 186 | 0.9393 |
| No log | 1.504 | 188 | 0.9391 |
| No log | 1.52 | 190 | 0.9385 |
| No log | 1.536 | 192 | 0.9385 |
| No log | 1.552 | 194 | 0.9387 |
| No log | 1.568 | 196 | 0.9393 |
| No log | 1.584 | 198 | 0.9402 |
| No log | 1.6 | 200 | 0.9410 |
| No log | 1.616 | 202 | 0.9410 |
| No log | 1.6320 | 204 | 0.9417 |
| No log | 1.6480 | 206 | 0.9414 |
| No log | 1.6640 | 208 | 0.9410 |
| No log | 1.6800 | 210 | 0.9402 |
| No log | 1.696 | 212 | 0.9400 |
| No log | 1.712 | 214 | 0.9398 |
| No log | 1.728 | 216 | 0.9397 |
| No log | 1.744 | 218 | 0.9395 |
| No log | 1.76 | 220 | 0.9398 |
| No log | 1.776 | 222 | 0.9400 |
| No log | 1.792 | 224 | 0.9403 |
| No log | 1.808 | 226 | 0.9403 |
| No log | 1.8240 | 228 | 0.9399 |
| No log | 1.8400 | 230 | 0.9392 |
| No log | 1.8560 | 232 | 0.9385 |
| No log | 1.8720 | 234 | 0.9385 |
| No log | 1.888 | 236 | 0.9390 |
| No log | 1.904 | 238 | 0.9394 |
| No log | 1.92 | 240 | 0.9395 |
| No log | 1.936 | 242 | 0.9392 |
| No log | 1.952 | 244 | 0.9391 |
| No log | 1.968 | 246 | 0.9390 |
| No log | 1.984 | 248 | 0.9386 |
| No log | 2.0 | 250 | 0.9380 |
| No log | 2.016 | 252 | 0.9381 |
| No log | 2.032 | 254 | 0.9401 |
| No log | 2.048 | 256 | 0.9431 |
| No log | 2.064 | 258 | 0.9469 |
| No log | 2.08 | 260 | 0.9507 |
| No log | 2.096 | 262 | 0.9529 |
| No log | 2.112 | 264 | 0.9524 |
| No log | 2.128 | 266 | 0.9501 |
| No log | 2.144 | 268 | 0.9478 |
| No log | 2.16 | 270 | 0.9466 |
| No log | 2.176 | 272 | 0.9463 |
| No log | 2.192 | 274 | 0.9458 |
| No log | 2.208 | 276 | 0.9454 |
| No log | 2.224 | 278 | 0.9451 |
| No log | 2.24 | 280 | 0.9456 |
| No log | 2.2560 | 282 | 0.9468 |
| No log | 2.2720 | 284 | 0.9477 |
| No log | 2.288 | 286 | 0.9484 |
| No log | 2.304 | 288 | 0.9486 |
| No log | 2.32 | 290 | 0.9479 |
| No log | 2.336 | 292 | 0.9473 |
| No log | 2.352 | 294 | 0.9473 |
| No log | 2.368 | 296 | 0.9473 |
| No log | 2.384 | 298 | 0.9475 |
| No log | 2.4 | 300 | 0.9479 |
| No log | 2.416 | 302 | 0.9490 |
| No log | 2.432 | 304 | 0.9499 |
| No log | 2.448 | 306 | 0.9501 |
| No log | 2.464 | 308 | 0.9498 |
| No log | 2.48 | 310 | 0.9491 |
| No log | 2.496 | 312 | 0.9489 |
| No log | 2.512 | 314 | 0.9490 |
| No log | 2.528 | 316 | 0.9487 |
| No log | 2.544 | 318 | 0.9483 |
| No log | 2.56 | 320 | 0.9483 |
| No log | 2.576 | 322 | 0.9483 |
| No log | 2.592 | 324 | 0.9485 |
| No log | 2.608 | 326 | 0.9487 |
| No log | 2.624 | 328 | 0.9492 |
| No log | 2.64 | 330 | 0.9493 |
| No log | 2.656 | 332 | 0.9488 |
| No log | 2.672 | 334 | 0.9487 |
| No log | 2.6880 | 336 | 0.9486 |
| No log | 2.7040 | 338 | 0.9485 |
| No log | 2.7200 | 340 | 0.9481 |
| No log | 2.7360 | 342 | 0.9477 |
| No log | 2.752 | 344 | 0.9478 |
| No log | 2.768 | 346 | 0.9482 |
| No log | 2.784 | 348 | 0.9487 |
| No log | 2.8 | 350 | 0.9483 |
| No log | 2.816 | 352 | 0.9481 |
| No log | 2.832 | 354 | 0.9480 |
| No log | 2.848 | 356 | 0.9480 |
| No log | 2.864 | 358 | 0.9479 |
| No log | 2.88 | 360 | 0.9481 |
| No log | 2.896 | 362 | 0.9484 |
| No log | 2.912 | 364 | 0.9488 |
| No log | 2.928 | 366 | 0.9490 |
| No log | 2.944 | 368 | 0.9489 |
| No log | 2.96 | 370 | 0.9487 |
| No log | 2.976 | 372 | 0.9484 |
| No log | 2.992 | 374 | 0.9476 |
| No log | 3.008 | 376 | 0.9468 |
| No log | 3.024 | 378 | 0.9471 |
| No log | 3.04 | 380 | 0.9481 |
| No log | 3.056 | 382 | 0.9499 |
| No log | 3.072 | 384 | 0.9521 |
| No log | 3.088 | 386 | 0.9543 |
| No log | 3.104 | 388 | 0.9562 |
| No log | 3.12 | 390 | 0.9572 |
| No log | 3.136 | 392 | 0.9577 |
| No log | 3.152 | 394 | 0.9577 |
| No log | 3.168 | 396 | 0.9577 |
| No log | 3.184 | 398 | 0.9574 |
| No log | 3.2 | 400 | 0.9570 |
| No log | 3.216 | 402 | 0.9569 |
| No log | 3.232 | 404 | 0.9567 |
| No log | 3.248 | 406 | 0.9565 |
| No log | 3.2640 | 408 | 0.9564 |
| No log | 3.2800 | 410 | 0.9562 |
| No log | 3.296 | 412 | 0.9561 |
| No log | 3.312 | 414 | 0.9561 |
| No log | 3.328 | 416 | 0.9562 |
| No log | 3.344 | 418 | 0.9565 |
| No log | 3.36 | 420 | 0.9568 |
| No log | 3.376 | 422 | 0.9570 |
| No log | 3.392 | 424 | 0.9572 |
| No log | 3.408 | 426 | 0.9573 |
| No log | 3.424 | 428 | 0.9572 |
| No log | 3.44 | 430 | 0.9569 |
| No log | 3.456 | 432 | 0.9570 |
| No log | 3.472 | 434 | 0.9572 |
| No log | 3.488 | 436 | 0.9574 |
| No log | 3.504 | 438 | 0.9575 |
| No log | 3.52 | 440 | 0.9577 |
| No log | 3.536 | 442 | 0.9577 |
| No log | 3.552 | 444 | 0.9578 |
| No log | 3.568 | 446 | 0.9579 |
| No log | 3.584 | 448 | 0.9577 |
| No log | 3.6 | 450 | 0.9575 |
| No log | 3.616 | 452 | 0.9575 |
| No log | 3.632 | 454 | 0.9575 |
| No log | 3.648 | 456 | 0.9576 |
| No log | 3.664 | 458 | 0.9576 |
| No log | 3.68 | 460 | 0.9574 |
| No log | 3.6960 | 462 | 0.9573 |
| No log | 3.7120 | 464 | 0.9571 |
| No log | 3.7280 | 466 | 0.9569 |
| No log | 3.7440 | 468 | 0.9567 |
| No log | 3.76 | 470 | 0.9565 |
| No log | 3.776 | 472 | 0.9563 |
| No log | 3.792 | 474 | 0.9563 |
| No log | 3.808 | 476 | 0.9563 |
| No log | 3.824 | 478 | 0.9564 |
| No log | 3.84 | 480 | 0.9565 |
| No log | 3.856 | 482 | 0.9565 |
| No log | 3.872 | 484 | 0.9566 |
| No log | 3.888 | 486 | 0.9566 |
| No log | 3.904 | 488 | 0.9565 |
| No log | 3.92 | 490 | 0.9565 |
| No log | 3.936 | 492 | 0.9565 |
| No log | 3.952 | 494 | 0.9564 |
| No log | 3.968 | 496 | 0.9564 |
| No log | 3.984 | 498 | 0.9564 |
| 0.814 | 4.0 | 500 | 0.9563 |
### Framework versions
- PEFT 0.10.1.dev0
- Transformers 4.40.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1
|
Jacque008/unsloth-llama-3-8b-instruct_4396_ori_refer_fwd_epoch2_merge
|
Jacque008
| 2024-04-19T13:01:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-Instruct",
"base_model:finetune:unsloth/llama-3-8b-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T13:01:02Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-Instruct
---
# Uploaded model
- **Developed by:** Jacque008
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct
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)
|
Jacque008/unsloth-llama-3-8b-instruct_4396_ori_refer_fwd_epoch2
|
Jacque008
| 2024-04-19T13:00:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-Instruct",
"base_model:finetune:unsloth/llama-3-8b-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T13:00:23Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-Instruct
---
# Uploaded model
- **Developed by:** Jacque008
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct
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)
|
MLIsaac/poca-SoccerTwos
|
MLIsaac
| 2024-04-19T13:00:30Z | 33 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2024-04-19T13:00:07Z |
---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
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: MLIsaac/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
larrydai/peft-starcoder-lora-a100
|
larrydai
| 2024-04-19T12:58:09Z | 1 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:bigcode/starcoderbase-1b",
"base_model:adapter:bigcode/starcoderbase-1b",
"license:bigcode-openrail-m",
"region:us"
] | null | 2024-04-19T09:29:44Z |
---
license: bigcode-openrail-m
library_name: peft
tags:
- generated_from_trainer
base_model: bigcode/starcoderbase-1b
model-index:
- name: peft-starcoder-lora-a100
results: []
---
<!-- 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. -->
# peft-starcoder-lora-a100
This model is a fine-tuned version of [bigcode/starcoderbase-1b](https://huggingface.co/bigcode/starcoderbase-1b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9744
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 30
- training_steps: 200
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7228 | 0.5 | 100 | 1.0017 |
| 0.8952 | 1.0 | 200 | 0.9744 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
dixitakriti/lora_summarization_customtrained
|
dixitakriti
| 2024-04-19T12:54:18Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-06T07:33:02Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
Raul569/oufit_recommender_19_Apr_2024_v1
|
Raul569
| 2024-04-19T12:51:21Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T12:51:20Z |
---
library_name: transformers
tags: []
---
# 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]
|
Raul569/SFTOpenLM-Dolly15k
|
Raul569
| 2024-04-19T12:51:07Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:openlm-research/open_llama_3b",
"base_model:adapter:openlm-research/open_llama_3b",
"license:apache-2.0",
"region:us"
] | null | 2024-04-19T12:50:45Z |
---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: openlm-research/open_llama_3b
model-index:
- name: SFTOpenLM-Dolly15k
results: []
---
<!-- 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. -->
# SFTOpenLM-Dolly15k
This model is a fine-tuned version of [openlm-research/open_llama_3b](https://huggingface.co/openlm-research/open_llama_3b) 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: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.10.1.dev0
- Transformers 4.41.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1
|
choprahetarth/starcoder2
|
choprahetarth
| 2024-04-19T12:49:08Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:bigcode/starcoder2-3b",
"base_model:adapter:bigcode/starcoder2-3b",
"license:bigcode-openrail-m",
"region:us"
] | null | 2024-04-19T12:41:24Z |
---
license: bigcode-openrail-m
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: bigcode/starcoder2-3b
model-index:
- name: starcoder2
results: []
---
<!-- 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. -->
# starcoder2
This model is a fine-tuned version of [bigcode/starcoder2-3b](https://huggingface.co/bigcode/starcoder2-3b) 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: 0.0002
- train_batch_size: 10
- eval_batch_size: 8
- seed: 1234
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 160
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 343
### Training results
### Framework versions
- PEFT 0.10.1.dev0
- Transformers 4.41.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1
|
jgrc3/unipelt_adapter_classification_trained_lr0_0001
|
jgrc3
| 2024-04-19T12:43:41Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"roberta",
"dataset:BigTMiami/amazon_helpfulness",
"region:us"
] | null | 2024-04-19T12:43:38Z |
---
tags:
- adapter-transformers
- roberta
datasets:
- BigTMiami/amazon_helpfulness
---
# Adapter `jgrc3/unipelt_adapter_classification_trained_lr0_0001` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/amazon_helpfulness](https://huggingface.co/datasets/BigTMiami/amazon_helpfulness/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("jgrc3/unipelt_adapter_classification_trained_lr0_0001", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
erlend123/emotion-analysis-binary-trans
|
erlend123
| 2024-04-19T12:43:04Z | 7 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-04-19T11:42:45Z |
---
tags:
- generated_from_trainer
model-index:
- name: emotion-analysis-ntnu
results: []
---
<!-- 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. -->
# emotion-analysis-ntnu
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
optimum/mistral-1.1b-testing
|
optimum
| 2024-04-19T12:42:59Z | 1,080 | 1 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-04-19T12:41:27Z |
---
license: apache-2.0
---
mistralized tinyllama since flash attention training on llama w/ flash-attn is buggy.
it's based on the 3t base model (not chat tuned).
not extensively tested.
enjoy!
(model card is repeated due to open llm leaderboard length requirements)
mistralized tinyllama since flash attention training on llama w/ flash-attn is buggy.
it's based on the 3t base model (not chat tuned).
not extensively tested.
enjoy!
mistralized tinyllama since flash attention training on llama w/ flash-attn is buggy.
it's based on the 3t base model (not chat tuned).
not extensively tested.
enjoy!
mistralized tinyllama since flash attention training on llama w/ flash-attn is buggy.
it's based on the 3t base model (not chat tuned).
not extensively tested.
enjoy!
mistralized tinyllama since flash attention training on llama w/ flash-attn is buggy.
it's based on the 3t base model (not chat tuned).
not extensively tested.
enjoy!
mistralized tinyllama since flash attention training on llama w/ flash-attn is buggy.
it's based on the 3t base model (not chat tuned).
not extensively tested.
enjoy!
mistralized tinyllama since flash attention training on llama w/ flash-attn is buggy.
it's based on the 3t base model (not chat tuned).
not extensively tested.
enjoy!
mistralized tinyllama since flash attention training on llama w/ flash-attn is buggy.
it's based on the 3t base model (not chat tuned).
not extensively tested.
enjoy!
mistralized tinyllama since flash attention training on llama w/ flash-attn is buggy.
it's based on the 3t base model (not chat tuned).
not extensively tested.
enjoy!
|
jondurbin/airoboros-34b-3.3-peft
|
jondurbin
| 2024-04-19T12:41:44Z | 0 | 1 | null |
[
"safetensors",
"license:apache-2.0",
"region:us"
] | null | 2024-03-30T08:26:12Z |
---
license: apache-2.0
---
Adapter for https://huggingface.co/jondurbin/airoboros-34b-3.3
|
numen-tech/Meta-Llama-3-8B-Instruct-w3a16g40sym
|
numen-tech
| 2024-04-19T12:40:44Z | 0 | 0 | null |
[
"arxiv:2308.13137",
"license:other",
"region:us"
] | null | 2024-04-19T12:36:58Z |
---
license: other
license_name: llama3
license_link: LICENSE
---
3-bit [OmniQuant](https://arxiv.org/abs/2308.13137) quantized version of [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct).
|
fangzhaoz/hellaswag_dora_llama_merged
|
fangzhaoz
| 2024-04-19T12:34:25Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-04-19T12:31:30Z |
---
library_name: transformers
tags: []
---
# 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]
|
UXAIR/SpaceInvadersNoFrameskipv4
|
UXAIR
| 2024-04-19T12:31:35Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-04-19T12:30:31Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 596.50 +/- 87.89
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga UXAIR -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga UXAIR -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga UXAIR
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
aamonten/gemma-2b-danish-chatml
|
aamonten
| 2024-04-19T12:31:01Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-04-19T12:27:44Z |
---
library_name: transformers
tags: []
---
# 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]
|
ruanmelio/dyu-fr-t5-small
|
ruanmelio
| 2024-04-19T12:24:30Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-04-19T09:13:08Z |
---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_keras_callback
model-index:
- name: ruanmelio/dyu-fr-t5-small
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ruanmelio/dyu-fr-t5-small
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.3119
- Validation Loss: 3.0330
- Epoch: 3
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.6876 | 3.2448 | 0 |
| 3.4669 | 3.1468 | 1 |
| 3.3790 | 3.0830 | 2 |
| 3.3119 | 3.0330 | 3 |
### Framework versions
- Transformers 4.38.2
- TensorFlow 2.15.0
- Datasets 2.18.0
- Tokenizers 0.15.2
|
amine-01/Pyramids
|
amine-01
| 2024-04-19T12:13:54Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2024-04-19T11:01:14Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
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: amine-01/Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
sleeping4cat/Tensorflow-next-word-predictor
|
sleeping4cat
| 2024-04-19T12:09:38Z | 4 | 0 |
keras
|
[
"keras",
"license:apache-2.0",
"region:us"
] | null | 2024-04-19T12:01:36Z |
---
license: apache-2.0
---
### Inference Code
```Python
import numpy as np
import pickle
from keras.preprocessing.sequence import pad_sequences
from keras.models import load_model
def predict_word(seed_text: str, tokenizer, model, next_words: int = 2) -> str:
for _ in range(next_words):
token_list = tokenizer.texts_to_sequences([seed_text])[0]
token_list = pad_sequences([token_list], maxlen=max_sequence_len-1, padding='pre')
predicted = np.argmax(model.predict(token_list), axis=-1)
output_word = ""
for word, index in tokenizer.word_index.items():
if index == predicted:
output_word = word
break
seed_text += " " + output_word
return seed_text
```
|
manishiitg/open-aditi-v6-gemma
|
manishiitg
| 2024-04-19T12:08:14Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"gemma",
"axolotl",
"generated_from_trainer",
"license:gemma",
"region:us"
] | null | 2024-04-07T15:24:55Z |
---
license: gemma
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: google/gemma-7B
model-index:
- name: open-aditi-chat-hi-1.25-gemma
results: []
---
Preview of dataset trained on: https://huggingface.co/datasets/manishiitg/aditi-syn-v2
The synthetic dataset (https://huggingface.co/datasets/manishiitg/aditi-syn-v2) and the full data creation pipeline (https://github.com/manishiitg/aditi_dataset) have been open-sourced, enabling transparency and fostering further research in this domain. The dataset is a rich tapestry of Hinglish (a blend of Hindi and English) data, as well as a diverse array of tasks spanning tools, retrieval-augmented generation (RAG), mathematics, and reasoning – all in the Hindi language.
LMJudge Eval
============
https://github.com/manishiitg/IndicLMJudge
#### LLM Judge Language: hi
| Model | Language | Score | No# Questions |
| --- | --- | --- | --- |
| mistralai/Mixtral-8x7B-Instruct-v0.1 | hi | 8.7148 | 554 |
| Qwen/Qwen1.5-72B-Chat-AWQ | hi | 8.3695 | 554 |
| manishiitg/open-aditi-v6-llama3 | hi | 8.2659 | 551 |
| Qwen/Qwen1.5-14B-Chat | hi | 8.2404 | 554 |
| google/gemma-7b-it | hi | 7.9152 | 554 |
| manishiitg/open-aditi-v6-gemma | hi | 7.8634 | 549 |
| Qwen/Qwen1.5-7B-Chat | hi | 7.8587 | 554 |
| manishiitg/open-aditi-hi-v3 | hi | 7.7644 | 554 |
| manishiitg/open-aditi-hi-v4 | hi | 7.6150 | 554 |
| manishiitg/open-aditi-hi-v2 | hi | 7.2518 | 554 |
| teknium/OpenHermes-2.5-Mistral-7B | hi | 7.2489 | 554 |
| ai4bharat/Airavata | hi | 6.9468 | 554 |
| 01-ai/Yi-34B-Chat | hi | 6.5801 | 554 |
| manishiitg/open-aditi-hi-v1 | hi | 4.7022 | 554 |
| sarvamai/OpenHathi-7B-Hi-v0.1-Base | hi | 4.2834 | 598 |
| Qwen/Qwen1.5-4B-Chat | hi | 4.1101 | 554 |
#### LLM Judge Language: en
| Model | Language | Score | No# Questions |
| --- | --- | --- | --- |
| Qwen/Qwen1.5-14B-Chat | en | 9.1947 | 356 |
| Qwen/Qwen1.5-72B-Chat-AWQ | en | 9.1618 | 356 |
| Qwen/Qwen1.5-7B-Chat | en | 9.1570 | 356 |
| 01-ai/Yi-34B-Chat | en | 9.1368 | 356 |
| mistralai/Mixtral-8x7B-Instruct-v0.1 | en | 9.1306 | 356 |
| manishiitg/open-aditi-v6-gemma | en | 9.1003 | 356 |
| teknium/OpenHermes-2.5-Mistral-7B | en | 9.0230 | 356 |
| manishiitg/open-aditi-v6-llama3 | en | 9.0197 | 356 |
| manishiitg/open-aditi-hi-v3 | en | 8.9615 | 356 |
| manishiitg/open-aditi-hi-v4 | en | 8.9188 | 356 |
| google/gemma-7b-it | en | 8.8191 | 356 |
| Qwen/Qwen1.5-4B-Chat | en | 8.7500 | 356 |
| google/gemma-2b-it | en | 8.4671 | 356 |
| manishiitg/open-aditi-hi-v2 | en | 8.4584 | 356 |
| ai4bharat/Airavata | en | 7.3834 | 356 |
| manishiitg/open-aditi-hi-v1 | en | 6.6559 | 356 |
| sarvamai/OpenHathi-7B-Hi-v0.1-Base | en | 5.9567 | 312 |
DHARMA TINY EVAL
============
#### Language Hi
| Model | ARC-Easy | bigbench | truthful_qa | BoolQ | winogrande | agieval | ARC-Challenge | MMLU | openbookqa |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| open-aditi-hi-v2 | 0.6245 | 0.4959 | 0.3866 | 0.7192 | 0.5353 | 0.2945 | 0.4828 | 0.3457 | 0.5279 |
| open-aditi-hi-v3 | 0.6803 | 0.4553 | 0.2788 | 0.7385 | 0.5390 | 0.2178 | 0.4914 | 0.3346 | 0.5688 |
| open-aditi-hi-v4 | 0.6989 | 0.4526 | 0.2714 | 0.7231 | 0.5167 | 0.2331 | 0.5302 | 0.3123 | 0.5316 |
| open-aditi-v6-gemma | 0.7212 | 0.4146 | 0.3234 | 0.6923 | 0.4870 | 0.2638 | 0.4957 | 0.3680 | 0.4349 |
| open-aditi-v6-llama3 | 0.5688 | 0.4119 | 0.2268 | 0.6500 | 0.4498 | 0.2331 | 0.4310 | 0.3420 | 0.3792 |
| open-aditi-hi-v1 | 0.4572 | 0.3767 | 0.2230 | 0.6346 | 0.4647 | 0.1840 | 0.3405 | 0.3271 | 0.3532 |
| OpenHermes-2.5-Mistral-7B | 0.3309 | 0.4201 | 0.3197 | 0.6077 | 0.4981 | 0.2331 | 0.3276 | 0.3086 | 0.3086 |
| OpenHathi-7B-Hi-v0.1-Base | 0.2862 | 0.3333 | 0.5130 | 0.6077 | 0.4907 | 0.2301 | 0.3017 | 0.2677 | 0.1933 |
| Airavata | 0.2751 | 0.1274 | 0.2268 | 0.0615 | 0.3866 | 0.1104 | 0.2845 | 0.1450 | 0.3383 |
| gemma-7b-it | 0.1227 | 0.0786 | 0.0743 | 0.1808 | 0.1561 | 0.0491 | 0.1078 | 0.0818 | 0.0855 |
#### Language En
| Model | ARC-Easy | bigbench | truthful_qa | BoolQ | winogrande | agieval | ARC-Challenge | MMLU | openbookqa |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| OpenHermes-2.5-Mistral-7B | 0.8922 | 0.5745 | 0.3197 | 0.8346 | 0.6989 | 0.4908 | 0.7802 | 0.5911 | 0.7621 |
| open-aditi-hi-v2 | 0.8625 | 0.5149 | 0.3532 | 0.8192 | 0.6877 | 0.4571 | 0.7500 | 0.5613 | 0.7732 |
| open-aditi-hi-v4 | 0.8959 | 0.5041 | 0.2862 | 0.8423 | 0.6914 | 0.4571 | 0.7716 | 0.5651 | 0.7138 |
| open-aditi-hi-v3 | 0.8773 | 0.4986 | 0.3048 | 0.8385 | 0.6766 | 0.4663 | 0.7371 | 0.5613 | 0.7249 |
| Qwen1.5-7B-Chat | 0.8922 | 0.5122 | 0.2007 | 0.8000 | 0.6654 | 0.4294 | 0.7759 | 0.5799 | 0.7621 |
| open-aditi-v6-gemma | 0.8699 | 0.4959 | 0.2602 | 0.7385 | 0.5465 | 0.4540 | 0.7371 | 0.5167 | 0.6654 |
| open-aditi-v6-llama3 | 0.8810 | 0.4634 | 0.1822 | 0.7577 | 0.5353 | 0.4110 | 0.7457 | 0.5688 | 0.6506 |
| open-aditi-hi-v1 | 0.8104 | 0.3902 | 0.2491 | 0.6962 | 0.5539 | 0.3681 | 0.6379 | 0.5056 | 0.5911 |
| Airavata | 0.7026 | 0.4282 | 0.3123 | 0.7192 | 0.5651 | 0.3313 | 0.5172 | 0.3792 | 0.5093 |
| OpenHathi-7B-Hi-v0.1-Base | 0.4684 | 0.3062 | 0.4758 | 0.6346 | 0.5167 | 0.2577 | 0.3017 | 0.2788 | 0.2714 |
Task: BoolQ Metric: score
Task: ARC-Easy Metric: score
Task: openbookqa Metric: score
Task: winogrande Metric: score
Task: ARC-Challenge Metric: score
Task: truthful_qa Metric: score
Task: bigbench Metric: score
Task: MMLU Metric: score
Task: agieval Metric: score
<!-- 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. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: google/gemma-7B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_config: philschmid/gemma-tokenizer-chatml
tokenizer_use_fast: true
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: manishiitg/aditi-syn-train-small-v3
type: completion
# 25 has only sythentic data, and has judge removed data
hub_model_id: manishiitg/open-aditi-chat-hi-1.25-gemma
hf_use_auth_token: true
wandb_project: open-aditi-chat-hi-1.25-gemma
dataset_prepared_path: manishiitg
push_dataset_to_hub: manishiitg
val_set_size: .1
output_dir: /sky-notebook/manishiitg/open-aditi-chat-hi-1.25-gemma
adapter: qlora
lora_model_dir:
save_safetensors: true
sequence_len: 2048
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model:
gradient_accumulation_steps: 8
micro_batch_size: 4
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
adam_beta2: 0.95
adam_epsilon: 0.00001
max_grad_norm: 1.0
train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
auto_resume_from_checkpoints: true ## manage check point resume from here
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 10
evals_per_epoch: 2
eval_table_size:
eval_table_max_new_tokens: 128
save_steps: 20 ## increase based on your dataset
save_strategy: steps
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
```
</details><br>
# open-aditi-chat-hi-1.25-gemma
This model is a fine-tuned version of [google/gemma-7B](https://huggingface.co/google/gemma-7B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0992
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.8213 | 0.0 | 1 | 8.4429 |
| 0.9759 | 0.5 | 121 | 2.0992 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.40.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.0
|
manishiitg/open-aditi-v6-llama3
|
manishiitg
| 2024-04-19T12:08:10Z | 0 | 2 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B",
"base_model:adapter:meta-llama/Meta-Llama-3-8B",
"license:other",
"region:us"
] | null | 2024-04-19T07:43:39Z |
---
license: other
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: meta-llama/Meta-Llama-3-8B
model-index:
- name: open-aditi-chat-hi-1.25-llama3
results: []
---
Preview of dataset trained on: https://huggingface.co/datasets/manishiitg/aditi-syn-v2
The synthetic dataset (https://huggingface.co/datasets/manishiitg/aditi-syn-v2) and the full data creation pipeline (https://github.com/manishiitg/aditi_dataset) have been open-sourced, enabling transparency and fostering further research in this domain. The dataset is a rich tapestry of Hinglish (a blend of Hindi and English) data, as well as a diverse array of tasks spanning tools, retrieval-augmented generation (RAG), mathematics, and reasoning – all in the Hindi language.
LMJudge Eval
============
https://github.com/manishiitg/IndicLMJudge
#### LLM Judge Language: hi
| Model | Language | Score | No# Questions |
| --- | --- | --- | --- |
| mistralai/Mixtral-8x7B-Instruct-v0.1 | hi | 8.7148 | 554 |
| Qwen/Qwen1.5-72B-Chat-AWQ | hi | 8.3695 | 554 |
| manishiitg/open-aditi-v6-llama3 | hi | 8.2659 | 551 |
| Qwen/Qwen1.5-14B-Chat | hi | 8.2404 | 554 |
| google/gemma-7b-it | hi | 7.9152 | 554 |
| manishiitg/open-aditi-v6-gemma | hi | 7.8634 | 549 |
| Qwen/Qwen1.5-7B-Chat | hi | 7.8587 | 554 |
| manishiitg/open-aditi-hi-v3 | hi | 7.7644 | 554 |
| manishiitg/open-aditi-hi-v4 | hi | 7.6150 | 554 |
| manishiitg/open-aditi-hi-v2 | hi | 7.2518 | 554 |
| teknium/OpenHermes-2.5-Mistral-7B | hi | 7.2489 | 554 |
| ai4bharat/Airavata | hi | 6.9468 | 554 |
| 01-ai/Yi-34B-Chat | hi | 6.5801 | 554 |
| manishiitg/open-aditi-hi-v1 | hi | 4.7022 | 554 |
| sarvamai/OpenHathi-7B-Hi-v0.1-Base | hi | 4.2834 | 598 |
| Qwen/Qwen1.5-4B-Chat | hi | 4.1101 | 554 |
#### LLM Judge Language: en
| Model | Language | Score | No# Questions |
| --- | --- | --- | --- |
| Qwen/Qwen1.5-14B-Chat | en | 9.1947 | 356 |
| Qwen/Qwen1.5-72B-Chat-AWQ | en | 9.1618 | 356 |
| Qwen/Qwen1.5-7B-Chat | en | 9.1570 | 356 |
| 01-ai/Yi-34B-Chat | en | 9.1368 | 356 |
| mistralai/Mixtral-8x7B-Instruct-v0.1 | en | 9.1306 | 356 |
| manishiitg/open-aditi-v6-gemma | en | 9.1003 | 356 |
| teknium/OpenHermes-2.5-Mistral-7B | en | 9.0230 | 356 |
| manishiitg/open-aditi-v6-llama3 | en | 9.0197 | 356 |
| manishiitg/open-aditi-hi-v3 | en | 8.9615 | 356 |
| manishiitg/open-aditi-hi-v4 | en | 8.9188 | 356 |
| google/gemma-7b-it | en | 8.8191 | 356 |
| Qwen/Qwen1.5-4B-Chat | en | 8.7500 | 356 |
| google/gemma-2b-it | en | 8.4671 | 356 |
| manishiitg/open-aditi-hi-v2 | en | 8.4584 | 356 |
| ai4bharat/Airavata | en | 7.3834 | 356 |
| manishiitg/open-aditi-hi-v1 | en | 6.6559 | 356 |
| sarvamai/OpenHathi-7B-Hi-v0.1-Base | en | 5.9567 | 312 |
DHARMA TINY EVAL
============
#### Language Hi
| Model | ARC-Easy | bigbench | truthful_qa | BoolQ | winogrande | agieval | ARC-Challenge | MMLU | openbookqa |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| open-aditi-hi-v2 | 0.6245 | 0.4959 | 0.3866 | 0.7192 | 0.5353 | 0.2945 | 0.4828 | 0.3457 | 0.5279 |
| open-aditi-hi-v3 | 0.6803 | 0.4553 | 0.2788 | 0.7385 | 0.5390 | 0.2178 | 0.4914 | 0.3346 | 0.5688 |
| open-aditi-hi-v4 | 0.6989 | 0.4526 | 0.2714 | 0.7231 | 0.5167 | 0.2331 | 0.5302 | 0.3123 | 0.5316 |
| open-aditi-v6-gemma | 0.7212 | 0.4146 | 0.3234 | 0.6923 | 0.4870 | 0.2638 | 0.4957 | 0.3680 | 0.4349 |
| open-aditi-v6-llama3 | 0.5688 | 0.4119 | 0.2268 | 0.6500 | 0.4498 | 0.2331 | 0.4310 | 0.3420 | 0.3792 |
| open-aditi-hi-v1 | 0.4572 | 0.3767 | 0.2230 | 0.6346 | 0.4647 | 0.1840 | 0.3405 | 0.3271 | 0.3532 |
| OpenHermes-2.5-Mistral-7B | 0.3309 | 0.4201 | 0.3197 | 0.6077 | 0.4981 | 0.2331 | 0.3276 | 0.3086 | 0.3086 |
| OpenHathi-7B-Hi-v0.1-Base | 0.2862 | 0.3333 | 0.5130 | 0.6077 | 0.4907 | 0.2301 | 0.3017 | 0.2677 | 0.1933 |
| Airavata | 0.2751 | 0.1274 | 0.2268 | 0.0615 | 0.3866 | 0.1104 | 0.2845 | 0.1450 | 0.3383 |
| gemma-7b-it | 0.1227 | 0.0786 | 0.0743 | 0.1808 | 0.1561 | 0.0491 | 0.1078 | 0.0818 | 0.0855 |
#### Language En
| Model | ARC-Easy | bigbench | truthful_qa | BoolQ | winogrande | agieval | ARC-Challenge | MMLU | openbookqa |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| OpenHermes-2.5-Mistral-7B | 0.8922 | 0.5745 | 0.3197 | 0.8346 | 0.6989 | 0.4908 | 0.7802 | 0.5911 | 0.7621 |
| open-aditi-hi-v2 | 0.8625 | 0.5149 | 0.3532 | 0.8192 | 0.6877 | 0.4571 | 0.7500 | 0.5613 | 0.7732 |
| open-aditi-hi-v4 | 0.8959 | 0.5041 | 0.2862 | 0.8423 | 0.6914 | 0.4571 | 0.7716 | 0.5651 | 0.7138 |
| open-aditi-hi-v3 | 0.8773 | 0.4986 | 0.3048 | 0.8385 | 0.6766 | 0.4663 | 0.7371 | 0.5613 | 0.7249 |
| Qwen1.5-7B-Chat | 0.8922 | 0.5122 | 0.2007 | 0.8000 | 0.6654 | 0.4294 | 0.7759 | 0.5799 | 0.7621 |
| open-aditi-v6-gemma | 0.8699 | 0.4959 | 0.2602 | 0.7385 | 0.5465 | 0.4540 | 0.7371 | 0.5167 | 0.6654 |
| open-aditi-v6-llama3 | 0.8810 | 0.4634 | 0.1822 | 0.7577 | 0.5353 | 0.4110 | 0.7457 | 0.5688 | 0.6506 |
| open-aditi-hi-v1 | 0.8104 | 0.3902 | 0.2491 | 0.6962 | 0.5539 | 0.3681 | 0.6379 | 0.5056 | 0.5911 |
| Airavata | 0.7026 | 0.4282 | 0.3123 | 0.7192 | 0.5651 | 0.3313 | 0.5172 | 0.3792 | 0.5093 |
| OpenHathi-7B-Hi-v0.1-Base | 0.4684 | 0.3062 | 0.4758 | 0.6346 | 0.5167 | 0.2577 | 0.3017 | 0.2788 | 0.2714 |
Task: BoolQ Metric: score
Task: ARC-Easy Metric: score
Task: openbookqa Metric: score
Task: winogrande Metric: score
Task: ARC-Challenge Metric: score
Task: truthful_qa Metric: score
Task: bigbench Metric: score
Task: MMLU Metric: score
Task: agieval Metric: score
<!-- 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. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: meta-llama/Meta-Llama-3-8B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: manishiitg/aditi-syn-train-small-v3
type: completion
# 25 has only sythentic data, and has judge removed data
hub_model_id: manishiitg/open-aditi-chat-hi-1.25-llama3
hf_use_auth_token: true
wandb_project: open-aditi-chat-hi-1.25-llama3
dataset_prepared_path: manishiitg
push_dataset_to_hub: manishiitg
val_set_size: .1
output_dir: /sky-notebook/manishiitg/open-aditi-chat-hi-1.25-llama3
adapter: qlora
lora_model_dir:
save_safetensors: true
sequence_len: 2048
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model:
gradient_accumulation_steps: 8
micro_batch_size: 6
num_epochs: 1
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
auto_resume_from_checkpoints: true ## manage check point resume from here
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 10
evals_per_epoch: 2
eval_table_size:
eval_table_max_new_tokens: 128
save_steps: 20 ## increase based on your dataset
save_strategy: steps
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
pad_token: <|end_of_text|>
```
</details><br>
# open-aditi-chat-hi-1.25-llama3
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9727
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 384
- total_eval_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5388 | 0.01 | 1 | 2.5709 |
| 0.8839 | 0.5 | 88 | 1.9648 |
| 0.88 | 1.0 | 176 | 1.9727 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.40.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.0
|
jorgefg03/deberta-v3-base-autext
|
jorgefg03
| 2024-04-19T12:05:43Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-04-19T11:18:04Z |
---
library_name: transformers
tags: []
---
# 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]
|
leliuga/gemma-7b-bnb-4bit
|
leliuga
| 2024-04-19T12:04:01Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"base_model:google/gemma-7b",
"base_model:quantized:google/gemma-7b",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] |
text-generation
| 2024-03-18T20:39:10Z |
---
base_model: google/gemma-7b
license: other
license_name: gemma-terms-of-use
license_link: https://ai.google.dev/gemma/terms
inference: false
model_creator: Google
model_name: gemma-7b
quantized_by: Leliuga
pipeline_tag: text-generation
tags:
- gemma
- arxiv:1910.09700
---
# gemma-7b - bnb 4bit
- Model creator: [Google](https://huggingface.co/google)
- Original model: [gemma-7b](https://huggingface.co/google/gemma-7b)
## Description
This model is 4bit quantized version of [gemma-7b](https://huggingface.co/google/gemma-7b) using bitsandbytes. It's designed for fine-tuning! The PAD token is set as <<pad>>.
|
leliuga/gemma-2b-it-bnb-4bit
|
leliuga
| 2024-04-19T12:03:37Z | 9 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"conversational",
"base_model:google/gemma-2b-it",
"base_model:quantized:google/gemma-2b-it",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] |
text-generation
| 2024-03-18T20:34:10Z |
---
base_model: google/gemma-2b-it
license: other
license_name: gemma-terms-of-use
license_link: https://ai.google.dev/gemma/terms
inference: false
model_creator: Google
model_name: gemma-2b-it
quantized_by: Leliuga
pipeline_tag: text-generation
tags:
- gemma
- arxiv:1910.09700
---
# gemma-2b-it - bnb 4bit
- Model creator: [Google](https://huggingface.co/google)
- Original model: [gemma-2b-it](https://huggingface.co/google/gemma-2b-it)
## Description
This model is 4bit quantized version of [gemma-2b-it](https://huggingface.co/google/gemma-2b-it) using bitsandbytes. It's designed for fine-tuning! The PAD token is set as "<pad>".
|
kiroloskhela/Sentiment-Bert
|
kiroloskhela
| 2024-04-19T12:03:25Z | 18 | 1 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-04-05T07:19:13Z |
# Emotion Analysis Model (Arabic)
This repository contains a model for emotion analysis in Arabic text. The model predicts the emotion associated with a given text input. It was developed by Kirolos Amgad Khela and trained on the Emotone dataset and the SetFit/Emotion dataset after transcribing text to Arabic.
## Model Details
- **Model type:** Text classification
- **Language(s):** Arabic
- **Finetuned from model:** MarBert
## Usage
### Direct Use
The model can be directly used for emotion analysis. It takes text inputs in Arabic and predicts the corresponding emotion associated with the text.
## Training Details
### Training Data
The model was trained using the Emotone dataset and the SetFit/Emotion dataset after translate text to Arabic.
### Training Procedure
#### Preprocessing
The training data was preprocessed by cleaning the text from English and removing punctuations.
#### Training Hyperparameters
- **Output Directory:** "train"
- **Logging Directory:** "logs"
- **Evaluation Strategy:** "epoch"
- **Per Device Train Batch Size:** 32
- **Per Device Eval Batch Size:** 32
- **Gradient Accumulation Steps:** 1
- **Number of Train Epochs:** 3
- **Learning Rate:** 3e-5
- **Warmup Ratio:** 0.1
- **Weight Decay:** 0.01
- **Adam Beta1:** 0.9
- **Adam Beta2:** 0.999
- **Adam Epsilon:** 1e-8
- **FP16:** True
- **Save Strategy:** "epoch"
- **Save Total Limit:** 3
- **Load Best Model at End:** True
- **Metric for Best Model:** "macro_f1"
- **Greater is Better:** True
## Dataset
The Emotone dataset and the SetFit/Emotion dataset are available for training. These datasets contain Arabic text annotated with emotion labels.
## Fine-tuning in Colab
To fine-tune the model on a custom dataset, you can use the provided Colab notebook. Follow the steps outlined in the notebook to upload your dataset, configure the training parameters, and start the fine-tuning process.
## Fine-tuned Model and Dataset
The fine-tuned model files and dataset are available in this [Google Drive folder](https://drive.google.com/drive/folders/1UWcBal3Myn4SipHhIX9bNpmbkQsO0Yow?usp=sharing). You can download the necessary files from this folder.
## Accuracy
The model achieved an accuracy of 82% on the evaluation dataset.
## Authors
- Kirolos Amgad Khela
## Contact
For any inquiries about the model, please contact Kirolos Amgad Khela at [email protected].
|
leliuga/gemma-2b-bnb-4bit
|
leliuga
| 2024-04-19T12:03:22Z | 18 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:quantized:google/gemma-2b",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] |
text-generation
| 2024-03-18T20:19:24Z |
---
base_model: google/gemma-2b
license: other
license_name: gemma-terms-of-use
license_link: https://ai.google.dev/gemma/terms
inference: false
model_creator: Google
model_name: gemma-2b
quantized_by: Leliuga
pipeline_tag: text-generation
tags:
- gemma
- arxiv:1910.09700
---
# gemma-2b - bnb 4bit
- Model creator: [Google](https://huggingface.co/google)
- Original model: [gemma-2b](https://huggingface.co/google/gemma-2b)
## Description
This model is 4bit quantized version of [gemma-2b](https://huggingface.co/google/gemma-2b) using bitsandbytes. It's designed for fine-tuning! The PAD token is set as "<pad>".
|
leliuga/phi-1-bnb-4bit
|
leliuga
| 2024-04-19T12:02:31Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"phi",
"text-generation",
"phi-1",
"arxiv:1910.09700",
"custom_code",
"base_model:microsoft/phi-1",
"base_model:quantized:microsoft/phi-1",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] |
text-generation
| 2024-03-18T17:21:33Z |
---
base_model: microsoft/phi-1
license: mit
inference: false
model_creator: Microsoft
model_name: phi-1
quantized_by: Leliuga
pipeline_tag: text-generation
tags:
- phi-1
- arxiv:1910.09700
---
# phi-1 - bnb 4bit
- Model creator: [Microsoft](https://huggingface.co/microsoft)
- Original model: [phi-1](https://huggingface.co/microsoft/phi-1)
## Description
This model is 4bit quantized version of [phi-1](https://huggingface.co/microsoft/phi-1) using bitsandbytes. It's designed for fine-tuning! The PAD token is set as "<|endoftext|>".
|
badrex/wav2vec2-large-xls-r-300m-upper-sorbian-pl-frozen-colab
|
badrex
| 2024-04-19T11:59:46Z | 5 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_16_1",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-04-19T09:16:42Z |
---
tags:
- generated_from_trainer
datasets:
- common_voice_16_1
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-upper-sorbian-pl-frozen-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_16_1
type: common_voice_16_1
config: hsb
split: test
args: hsb
metrics:
- name: Wer
type: wer
value: 0.3985473289597001
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-upper-sorbian-pl-frozen-colab
This model is a fine-tuned version of [](https://huggingface.co/) on the common_voice_16_1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7512
- Wer: 0.3985
- Cer: 0.0926
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 60
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 0.6621 | 3.23 | 100 | 0.6431 | 0.7048 | 0.1711 |
| 0.4922 | 6.45 | 200 | 0.5838 | 0.6120 | 0.1468 |
| 0.3591 | 9.68 | 300 | 0.5487 | 0.5621 | 0.1322 |
| 0.2869 | 12.9 | 400 | 0.5812 | 0.5436 | 0.1309 |
| 0.2179 | 16.13 | 500 | 0.6222 | 0.5014 | 0.1212 |
| 0.1731 | 19.35 | 600 | 0.6930 | 0.4808 | 0.1141 |
| 0.1315 | 22.58 | 700 | 0.6681 | 0.4721 | 0.1116 |
| 0.1044 | 25.81 | 800 | 0.6849 | 0.4567 | 0.1088 |
| 0.0876 | 29.03 | 900 | 0.7287 | 0.4623 | 0.1125 |
| 0.0822 | 32.26 | 1000 | 0.7278 | 0.4496 | 0.1097 |
| 0.0736 | 35.48 | 1100 | 0.7534 | 0.4552 | 0.1117 |
| 0.0641 | 38.71 | 1200 | 0.7500 | 0.4220 | 0.1025 |
| 0.0572 | 41.94 | 1300 | 0.7008 | 0.4227 | 0.1024 |
| 0.0495 | 45.16 | 1400 | 0.7697 | 0.4267 | 0.1011 |
| 0.0488 | 48.39 | 1500 | 0.7364 | 0.4051 | 0.0947 |
| 0.0444 | 51.61 | 1600 | 0.7444 | 0.4110 | 0.0952 |
| 0.0416 | 54.84 | 1700 | 0.7621 | 0.3983 | 0.0936 |
| 0.0398 | 58.06 | 1800 | 0.7512 | 0.3985 | 0.0926 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
|
bhutchings/ppo-LunarLander-v2
|
bhutchings
| 2024-04-19T11:59:36Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-04-19T11:59:18Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 258.67 +/- 21.89
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
leliuga/Llama-2-13b-hf-bnb-4bit
|
leliuga
| 2024-04-19T11:59:30Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-2",
"arxiv:2307.09288",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-13b-hf",
"base_model:quantized:meta-llama/Llama-2-13b-hf",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] |
text-generation
| 2024-03-17T22:59:01Z |
---
base_model: meta-llama/Llama-2-13b-hf
license: apache-2.0
inference: false
model_creator: Meta
model_name: Llama-2-13b-hf
quantized_by: Leliuga
pipeline_tag: text-generation
tags:
- llama-2
- arxiv:2307.09288
- arxiv:1910.09700
---
# Llama-2-13b-hf - bnb 4bit
- Model creator: [Meta](https://huggingface.co/meta-llama)
- Original model: [Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf)
## Description
This model is 4bit quantized version of [Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) using bitsandbytes. It's designed for fine-tuning! The PAD token is set as UNK.
|
leliuga/Llama-2-7b-chat-hf-bnb-4bit
|
leliuga
| 2024-04-19T11:58:56Z | 27 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-2",
"arxiv:2307.09288",
"arxiv:1910.09700",
"conversational",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:quantized:meta-llama/Llama-2-7b-chat-hf",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] |
text-generation
| 2024-03-17T22:10:32Z |
---
base_model: meta-llama/Llama-2-7b-chat-hf
license: apache-2.0
inference: false
model_creator: Meta
model_name: Llama-2-7b-chat-hf
quantized_by: Leliuga
pipeline_tag: text-generation
tags:
- llama-2
- arxiv:2307.09288
- arxiv:1910.09700
---
# Llama-2-7b-chat-hf - bnb 4bit
- Model creator: [Meta](https://huggingface.co/meta-llama)
- Original model: [Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)
## Description
This model is 4bit quantized version of [Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) using bitsandbytes. It's designed for fine-tuning! The PAD token is set as UNK.
|
leliuga/Llama-2-7b-hf-bnb-4bit
|
leliuga
| 2024-04-19T11:58:33Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-2",
"arxiv:2307.09288",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:quantized:meta-llama/Llama-2-7b-hf",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] |
text-generation
| 2024-03-17T22:49:43Z |
---
base_model: meta-llama/Llama-2-7b-hf
license: apache-2.0
inference: false
model_creator: Meta
model_name: Llama-2-7b-hf
quantized_by: Leliuga
pipeline_tag: text-generation
tags:
- llama-2
- arxiv:2307.09288
- arxiv:1910.09700
---
# Llama-2-7b-hf - bnb 4bit
- Model creator: [Meta](https://huggingface.co/meta-llama)
- Original model: [Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf)
## Description
This model is 4bit quantized version of [Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) using bitsandbytes. It's designed for fine-tuning! The PAD token is set as UNK.
|
leliuga/Meta-Llama-3-8B-Instruct-bnb-4bit
|
leliuga
| 2024-04-19T11:57:44Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-3",
"conversational",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:quantized:meta-llama/Meta-Llama-3-8B-Instruct",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] |
text-generation
| 2024-04-19T11:07:18Z |
---
base_model: meta-llama/Meta-Llama-3-8B-Instruct
license: other
license_name: llama3
license_link: https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/raw/main/LICENSE
inference: false
model_creator: Meta
model_name: Meta-Llama-3-8B-Instruct
quantized_by: Leliuga
pipeline_tag: text-generation
tags:
- llama
- llama-3
---
# Meta-Llama-3-8B-Instruct - bnb 4bit
- Model creator: [Meta](https://huggingface.co/meta-llama)
- Original model: [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
## Description
This model is 4bit quantized version of [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) using bitsandbytes. It's designed for fine-tuning! The PAD token is set as UNK.
|
TheophileCA/distilroberta_trainedOnCACatalogue
|
TheophileCA
| 2024-04-19T11:52:19Z | 6 | 0 |
transformers
|
[
"transformers",
"tf",
"roberta",
"fill-mask",
"generated_from_keras_callback",
"base_model:TheophileCA/distilroberta_trainedOnCACatalogue",
"base_model:finetune:TheophileCA/distilroberta_trainedOnCACatalogue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2024-04-19T11:27:35Z |
---
license: apache-2.0
base_model: TheophileCA/distilroberta_trainedOnCACatalogue
tags:
- generated_from_keras_callback
model-index:
- name: distilroberta_trainedOnCACatalogue
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distilroberta_trainedOnCACatalogue
This model is a fine-tuned version of [TheophileCA/distilroberta_trainedOnCACatalogue](https://huggingface.co/TheophileCA/distilroberta_trainedOnCACatalogue) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.1564
- Validation Loss: 1.1547
- Epoch: 14
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.7078 | 1.5690 | 0 |
| 1.6687 | 1.5544 | 1 |
| 1.5738 | 1.4494 | 2 |
| 1.4793 | 1.4567 | 3 |
| 1.4418 | 1.4016 | 4 |
| 1.4327 | 1.3699 | 5 |
| 1.3634 | 1.3266 | 6 |
| 1.3034 | 1.2834 | 7 |
| 1.2854 | 1.2797 | 8 |
| 1.2689 | 1.2383 | 9 |
| 1.2379 | 1.2031 | 10 |
| 1.1968 | 1.1526 | 11 |
| 1.1555 | 1.2410 | 12 |
| 1.1486 | 1.2033 | 13 |
| 1.1564 | 1.1547 | 14 |
### Framework versions
- Transformers 4.34.0
- TensorFlow 2.12.1
- Datasets 2.14.5
- Tokenizers 0.14.1
|
HachiML/Swallow-MS-7b-v0.1-ChatSkill-LAB-Evo-v0.9
|
HachiML
| 2024-04-19T11:46:58Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-04-19T11:34:41Z |
---
library_name: transformers
tags: []
---
# 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]
|
weaverlabs/bayard-1
|
weaverlabs
| 2024-04-19T11:37:30Z | 44 | 0 |
transformers
|
[
"transformers",
"pytorch",
"research",
"LGBTQ+ research",
"summarization",
"en",
"dataset:weaverlabs/bayardoneconversations",
"license:mit",
"endpoints_compatible",
"region:us"
] |
summarization
| 2024-04-19T11:25:02Z |
---
license: mit
datasets:
- weaverlabs/bayardoneconversations
language:
- en
metrics:
- accuracy
- character
pipeline_tag: summarization
tags:
- research
- LGBTQ+ research
---
|
MohammadOthman/Mistral-7B-Dolly-15K
|
MohammadOthman
| 2024-04-19T11:32:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T11:31:57Z |
---
library_name: transformers
tags: []
---
# 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]
|
adammoss/gpt-pretrain-lm-w1
|
adammoss
| 2024-04-19T11:30:20Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gptmodel",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-18T14:42:22Z |
---
library_name: transformers
tags: []
---
# 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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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
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### Direct Use
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[More Information Needed]
### Downstream Use [optional]
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### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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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#### 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]
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|
pavi156/my_awesome_qa_model
|
pavi156
| 2024-04-19T11:28:38Z | 5 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2024-04-18T13:28:56Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: my_awesome_qa_model
results: []
---
<!-- 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. -->
# my_awesome_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9998
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 250 | 2.7540 |
| 2.9386 | 2.0 | 500 | 2.1089 |
| 2.9386 | 3.0 | 750 | 1.9998 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
|
jorgefg03/bert-base-uncased-autext
|
jorgefg03
| 2024-04-19T11:22:39Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-04-19T09:45:48Z |
---
library_name: transformers
tags: []
---
# 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]
|
automerger/Multiverseex26Meliodaspercival_01_experiment26t3q-7B
|
automerger
| 2024-04-19T11:20:12Z | 0 | 0 | null |
[
"merge",
"mergekit",
"lazymergekit",
"automerger",
"base_model:MaziyarPanahi/MeliodasPercival_01_Experiment26T3q",
"base_model:merge:MaziyarPanahi/MeliodasPercival_01_Experiment26T3q",
"base_model:allknowingroger/MultiverseEx26-7B-slerp",
"base_model:merge:allknowingroger/MultiverseEx26-7B-slerp",
"license:apache-2.0",
"region:us"
] | null | 2024-04-19T11:20:11Z |
---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- automerger
base_model:
- allknowingroger/MultiverseEx26-7B-slerp
- MaziyarPanahi/MeliodasPercival_01_Experiment26T3q
---
# Multiverseex26Meliodaspercival_01_experiment26t3q-7B
Multiverseex26Meliodaspercival_01_experiment26t3q-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration.
* [allknowingroger/MultiverseEx26-7B-slerp](https://huggingface.co/allknowingroger/MultiverseEx26-7B-slerp)
* [MaziyarPanahi/MeliodasPercival_01_Experiment26T3q](https://huggingface.co/MaziyarPanahi/MeliodasPercival_01_Experiment26T3q)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: allknowingroger/MultiverseEx26-7B-slerp
layer_range: [0, 32]
- model: MaziyarPanahi/MeliodasPercival_01_Experiment26T3q
layer_range: [0, 32]
merge_method: slerp
base_model: allknowingroger/MultiverseEx26-7B-slerp
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
random_seed: 0
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "automerger/Multiverseex26Meliodaspercival_01_experiment26t3q-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"])
```
|
TwentyNine/byt5-ain-kana-latin-converter-v1
|
TwentyNine
| 2024-04-19T11:17:53Z | 7 | 1 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-04-19T11:15:40Z |
---
library_name: transformers
tags: []
---
# 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]
|
MelanieKoe/w2v2-base-pretrained_lr5e-5_at1_da1-p4
|
MelanieKoe
| 2024-04-19T11:16:41Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-base",
"base_model:finetune:facebook/wav2vec2-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-04-19T05:35:32Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2v2-base-pretrained_lr5e-5_at1_da1-p4
results: []
---
<!-- 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. -->
# w2v2-base-pretrained_lr5e-5_at1_da1-p4
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6341
- Wer: 0.1039
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 18.6256 | 5.21 | 250 | 4.2622 | 1.0 |
| 3.3901 | 10.42 | 500 | 3.2209 | 1.0 |
| 3.0963 | 15.62 | 750 | 3.1175 | 1.0 |
| 2.0992 | 20.83 | 1000 | 0.5962 | 0.4402 |
| 0.2069 | 26.04 | 1250 | 0.4456 | 0.1310 |
| 0.0849 | 31.25 | 1500 | 0.4902 | 0.1200 |
| 0.0596 | 36.46 | 1750 | 0.5079 | 0.1176 |
| 0.0437 | 41.67 | 2000 | 0.5362 | 0.1136 |
| 0.0355 | 46.88 | 2250 | 0.5433 | 0.1156 |
| 0.0281 | 52.08 | 2500 | 0.5994 | 0.1136 |
| 0.0238 | 57.29 | 2750 | 0.6018 | 0.1112 |
| 0.02 | 62.5 | 3000 | 0.5970 | 0.1120 |
| 0.0181 | 67.71 | 3250 | 0.6282 | 0.1083 |
| 0.0167 | 72.92 | 3500 | 0.6120 | 0.1075 |
| 0.0145 | 78.12 | 3750 | 0.6404 | 0.1047 |
| 0.014 | 83.33 | 4000 | 0.6341 | 0.1039 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
reecursion/stress-RoBERTa
|
reecursion
| 2024-04-19T11:16:38Z | 5 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-04-19T11:15:55Z |
---
license: mit
base_model: FacebookAI/roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-chatbot-stress
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-chatbot-stress
This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9676
- Accuracy: 0.8275
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4295 | 1.0 | 249 | 0.4018 | 0.8157 |
| 0.4911 | 2.0 | 498 | 0.4656 | 0.8039 |
| 0.472 | 3.0 | 747 | 0.6054 | 0.8431 |
| 0.1464 | 4.0 | 996 | 0.9441 | 0.8157 |
| 0.08 | 5.0 | 1245 | 0.9676 | 0.8275 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
ninagroot/Baby-Llama-58M-RUN3-old
|
ninagroot
| 2024-04-19T11:14:21Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-04-18T19:17:07Z |
---
tags:
- generated_from_trainer
model-index:
- name: Baby-Llama-58M
results: []
---
<!-- 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. -->
# Baby-Llama-58M
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.7109
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.00025
- train_batch_size: 128
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- num_epochs: 80
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 311.1646 | 1.0 | 3 | 287.5772 |
| 309.9048 | 2.0 | 6 | 282.5104 |
| 295.7833 | 3.0 | 9 | 266.8010 |
| 269.5852 | 4.0 | 12 | 247.3416 |
| 250.6772 | 5.0 | 15 | 231.4105 |
| 243.0754 | 6.0 | 18 | 224.6885 |
| 235.779 | 7.0 | 21 | 217.7554 |
| 235.8358 | 8.0 | 24 | 211.6984 |
| 224.1199 | 9.0 | 27 | 204.9522 |
| 216.0247 | 10.0 | 30 | 197.5209 |
| 206.4354 | 11.0 | 33 | 189.5172 |
| 189.1456 | 12.0 | 36 | 179.2765 |
| 181.0333 | 13.0 | 39 | 157.3401 |
| 152.062 | 14.0 | 42 | 137.4234 |
| 132.3128 | 15.0 | 45 | 120.5469 |
| 118.0474 | 16.0 | 48 | 106.6884 |
| 107.6354 | 17.0 | 51 | 97.7495 |
| 98.2458 | 18.0 | 54 | 88.4898 |
| 86.4009 | 19.0 | 57 | 77.8249 |
| 75.9386 | 20.0 | 60 | 67.9337 |
| 65.627 | 21.0 | 63 | 58.1877 |
| 53.5903 | 22.0 | 66 | 49.0234 |
| 47.114 | 23.0 | 69 | 41.2838 |
| 38.9667 | 24.0 | 72 | 34.4503 |
| 32.8846 | 25.0 | 75 | 29.7438 |
| 27.1886 | 26.0 | 78 | 24.2863 |
| 23.0713 | 27.0 | 81 | 20.1505 |
| 18.9003 | 28.0 | 84 | 16.9556 |
| 15.9133 | 29.0 | 87 | 14.4738 |
| 13.5544 | 30.0 | 90 | 12.6399 |
| 11.6834 | 31.0 | 93 | 11.1016 |
| 10.2371 | 32.0 | 96 | 9.9052 |
| 9.2371 | 33.0 | 99 | 8.9413 |
| 8.352 | 34.0 | 102 | 8.1600 |
| 7.5322 | 35.0 | 105 | 7.6794 |
| 7.0653 | 36.0 | 108 | 7.3031 |
| 6.6853 | 37.0 | 111 | 6.9564 |
| 6.3257 | 38.0 | 114 | 6.7247 |
| 5.9869 | 39.0 | 117 | 6.4649 |
| 5.8618 | 40.0 | 120 | 6.2734 |
| 5.6025 | 41.0 | 123 | 6.1253 |
| 5.4913 | 42.0 | 126 | 6.0822 |
| 5.3086 | 43.0 | 129 | 5.8575 |
| 5.1904 | 44.0 | 132 | 5.6860 |
| 5.1193 | 45.0 | 135 | 5.6821 |
| 5.0846 | 46.0 | 138 | 5.5831 |
| 5.017 | 47.0 | 141 | 5.5245 |
| 4.7435 | 48.0 | 144 | 5.3877 |
| 4.7546 | 49.0 | 147 | 5.3523 |
| 4.8606 | 50.0 | 150 | 5.3845 |
| 4.7146 | 51.0 | 153 | 5.2239 |
| 4.6273 | 52.0 | 156 | 5.1927 |
| 4.4469 | 53.0 | 159 | 5.1898 |
| 4.5135 | 54.0 | 162 | 5.0846 |
| 4.4061 | 55.0 | 165 | 5.0756 |
| 4.3577 | 56.0 | 168 | 5.0474 |
| 4.2169 | 57.0 | 171 | 5.0125 |
| 4.3001 | 58.0 | 174 | 4.9770 |
| 4.2399 | 59.0 | 177 | 4.9469 |
| 4.3372 | 60.0 | 180 | 4.9162 |
| 4.2669 | 61.0 | 183 | 4.9166 |
| 4.2394 | 62.0 | 186 | 4.8618 |
| 4.2965 | 63.0 | 189 | 4.8595 |
| 4.1188 | 64.0 | 192 | 4.8285 |
| 4.2886 | 65.0 | 195 | 4.8265 |
| 4.2688 | 66.0 | 198 | 4.8103 |
| 4.2429 | 67.0 | 201 | 4.7904 |
| 3.9653 | 68.0 | 204 | 4.7787 |
| 4.2676 | 69.0 | 207 | 4.7604 |
| 4.2029 | 70.0 | 210 | 4.7588 |
| 4.0962 | 71.0 | 213 | 4.7560 |
| 4.0643 | 72.0 | 216 | 4.7449 |
| 4.0713 | 73.0 | 219 | 4.7341 |
| 4.1192 | 74.0 | 222 | 4.7275 |
| 4.135 | 75.0 | 225 | 4.7186 |
| 3.9914 | 76.0 | 228 | 4.7135 |
| 4.0225 | 77.0 | 231 | 4.7144 |
| 3.9907 | 78.0 | 234 | 4.7152 |
| 4.0444 | 79.0 | 237 | 4.7123 |
| 4.0321 | 80.0 | 240 | 4.7109 |
### Framework versions
- Transformers 4.39.1
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
RJ3vans/CMV1spanTagger
|
RJ3vans
| 2024-04-19T11:13:24Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:04Z |
This model identifies compound verb phrases (including conjoins and coordinators) in an input sentence.
Try the test sentence:
John kicked the ball [and] chased after it.
Please note that it is necessary for you to highlight the verb phrase coordinator "and" using square brackets. When deployed in a text simplification method, this sign tagging step can be performed using the model at https://huggingface.co/RJ3vans/SignTagger.
The model should tag the tokens in the sentence with information about whether or not they are contained within a compound verb phrase. If you find the model useful, please cite my thesis which presents the dataset used for finetuning:
Evans, R. (2020) Sentence Simplification for Text Processing. Doctoral thesis. University of Wolverhampton. Wolverhampton, UK. (https://rj3vans.github.io/Evans2020_SentenceSimplificationForTextProcessing.pdf)
There you will find more information about the tagging scheme.
The model was derived using code adapted from an original program written by Dr. Le An Ha at the University of Wolverhampton.
|
DaniElAbrazos/ppo-LunarLander-v2
|
DaniElAbrazos
| 2024-04-19T11:10:29Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-04-19T11:10:10Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 256.67 +/- 15.43
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
RJ3vans/SSMNspanTagger
|
RJ3vans
| 2024-04-19T11:09:03Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:04Z |
This model identifies complex NPs modified by non-finite nominal clauses ("appositives") in the input sentence.
Try the test sentence:
My name is Sarah and I live in London[,] the capital of England.
Please note that it is necessary for you to highlight the left boundary of the non-finite nominal clause "," using square brackets. When deployed in a text simplification method, this sign tagging step can be performed using the model at https://huggingface.co/RJ3vans/SignTagger.
The model should tag the tokens in the sentence with information about whether or not they are contained within a non-finite nominal "appositive" clause. If you find the model useful, please cite my thesis which presents the dataset used for finetuning:
Evans, R. (2020) Sentence Simplification for Text Processing. Doctoral thesis. University of Wolverhampton. Wolverhampton, UK. (https://rj3vans.github.io/Evans2020_SentenceSimplificationForTextProcessing.pdf)
The model was derived using code adapted from an original program written by Dr. Le An Ha at the University of Wolverhampton.
|
Dhananjayg22/dpo-legal-extractor
|
Dhananjayg22
| 2024-04-19T11:05:42Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T11:02:46Z |
---
library_name: transformers
tags: []
---
# 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]
|
DavidAU/UNA-dolphin-2.6-mistral-7b-dpo-laser-Q6_K-GGUF
|
DavidAU
| 2024-04-19T11:04:24Z | 13 | 1 | null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"dataset:ehartford/dolphin",
"dataset:jondurbin/airoboros-2.2.1",
"dataset:ehartford/dolphin-coder",
"dataset:teknium/openhermes",
"dataset:ise-uiuc/Magicoder-OSS-Instruct-75K",
"dataset:ise-uiuc/Magicoder-Evol-Instruct-110K",
"dataset:LDJnr/Capybara",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T11:04:10Z |
---
language:
- en
license: apache-2.0
tags:
- llama-cpp
- gguf-my-repo
datasets:
- ehartford/dolphin
- jondurbin/airoboros-2.2.1
- ehartford/dolphin-coder
- teknium/openhermes
- ise-uiuc/Magicoder-OSS-Instruct-75K
- ise-uiuc/Magicoder-Evol-Instruct-110K
- LDJnr/Capybara
model-index:
- name: UNA-dolphin-2.6-mistral-7b-dpo-laser
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 67.15
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-dolphin-2.6-mistral-7b-dpo-laser
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 86.31
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-dolphin-2.6-mistral-7b-dpo-laser
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 63.36
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-dolphin-2.6-mistral-7b-dpo-laser
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 64.15
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-dolphin-2.6-mistral-7b-dpo-laser
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 79.24
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-dolphin-2.6-mistral-7b-dpo-laser
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 44.35
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-dolphin-2.6-mistral-7b-dpo-laser
name: Open LLM Leaderboard
---
# DavidAU/UNA-dolphin-2.6-mistral-7b-dpo-laser-Q6_K-GGUF
This model was converted to GGUF format from [`fblgit/UNA-dolphin-2.6-mistral-7b-dpo-laser`](https://huggingface.co/fblgit/UNA-dolphin-2.6-mistral-7b-dpo-laser) 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/fblgit/UNA-dolphin-2.6-mistral-7b-dpo-laser) 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/UNA-dolphin-2.6-mistral-7b-dpo-laser-Q6_K-GGUF --model una-dolphin-2.6-mistral-7b-dpo-laser.Q6_K.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo DavidAU/UNA-dolphin-2.6-mistral-7b-dpo-laser-Q6_K-GGUF --model una-dolphin-2.6-mistral-7b-dpo-laser.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 una-dolphin-2.6-mistral-7b-dpo-laser.Q6_K.gguf -n 128
```
|
RJ3vans/SSCCVspanTagger
|
RJ3vans
| 2024-04-19T11:03:19Z | 53 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-05-17T14:59:38Z |
Try the test sentences:
My name is Sarah and I live in London[, which] is the largest city in the UK.
John thought [that] that was a strange idea.
It was on Tuesdays [when] Peter took Tess for a walk.
John was so large [that] he had to crouch to fit through the front door.
Please note that it is necessary for you to highlight the left clause boundary using square brackets. When deployed in a text simplification method, this sign tagging step can be performed using the model at https://huggingface.co/RJ3vans/SignTagger.
The model should tag the tokens in the sentence with information about whether or not they are contained within particular types of syntactic constituents. If you find the model useful, please cite my thesis which presents the dataset used for finetuning:
Evans, R. (2020) Sentence Simplification for Text Processing. Doctoral thesis. University of Wolverhampton. Wolverhampton, UK. (https://rj3vans.github.io/Evans2020_SentenceSimplificationForTextProcessing.pdf)
There you will find more information about the tagging scheme.
|
RJ3vans/DeBERTaCCVspanTagger
|
RJ3vans
| 2024-04-19T11:02:24Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-07-14T16:15:52Z |
Try the test sentence:
The woman said "my name is Sarah [and] I live in London."
Please note that it is necessary for you to highlight the clause coordinator "and" using square brackets. When deployed in a text simplification method, this sign tagging step can be performed using the model at https://huggingface.co/RJ3vans/SignTagger.
The model should tag the tokens in the sentence with information about whether or not they are contained within a compound clause. If you find the model useful, please cite my thesis which presents the dataset used for finetuning:
Evans, R. (2020) Sentence Simplification for Text Processing. Doctoral thesis. University of Wolverhampton. Wolverhampton, UK. (https://rj3vans.github.io/Evans2020_SentenceSimplificationForTextProcessing.pdf)
There you will find more information about the tagging scheme.
The model was derived using code adapted from an original program written by Dr. Le An Ha at the University of Wolverhampton.
|
vadhri/whisper-tiny
|
vadhri
| 2024-04-19T11:01:05Z | 6 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:PolyAI/minds14",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-04-19T10:35:45Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- PolyAI/minds14
metrics:
- wer
model-index:
- name: whisper-tiny
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: PolyAI/minds14
type: PolyAI/minds14
config: en-US
split: train
args: en-US
metrics:
- name: Wer
type: wer
value: 0.3569725864123957
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-tiny
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6785
- Wer Ortho: 0.3644
- Wer: 0.3570
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 600
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 0.0006 | 17.86 | 500 | 0.6785 | 0.3644 | 0.3570 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
|
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Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.