
## Model Card for mistral-7b-sft-open-orca-flan-50k

### Model Details
- **Model Name:** mistral-7b-sft-open-orca-flan-50k
- **Developed by:** [kz919](https://huggingface.co/kz919)
- **Model Type:** Language Model
- **Version:** 1.0
- **Release Date:** 1/12/2024
- **Model Size:** 7 billion parameters
- **Training Data:** kz919/open-orca-flan-50k-synthetic-5-models

### Training Data
The `mistral-7b-sft-open-orca-flan-50k` model was trained on the dataset provided by `kz919/open-orca-flan-50k-synthetic-5-models`. The dataset contains 50K CoT entries from [Open-Orca/Flan](https://huggingface.co/datasets/Open-Orca/FLAN)

### Training Procedure
The model was trained using a supervised fine-tuning approach on the base Mistral-7B model. The training process involved multiple iterations to ensure the model's proficiency in understanding and generating natural language text. Special attention was given to maintaining the ethical standards and reducing biases in the model. The training code is modeled after [sft_llama2](https://github.com/huggingface/trl/blob/main/examples/research_projects/stack_llama_2/scripts/sft_llama2.py).

### Model Capabilities
`mistral-7b-sft-open-orca-flan-50k` is capable of performing a wide range of natural language processing tasks, including but not limited to:
- Text generation
- Question answering
- Text summarization
- Language translation
- Sentiment analysis

This model is built to be an ablation "reward model" for the synthetic responses generated from the 5 models.

### Intended Use
This model is intended for use in applications requiring natural language understanding and generation. It can be employed in chatbots, content creation tools, language translation services, and more. It is especially useful for developers and researchers working on NLP and AI projects.

### Limitations
While `mistral-7b-sft-open-orca-flan-50k` is a powerful model, it has certain limitations:
- It may not fully comprehend or generate text accurately in less-represented languages or dialects.
- Like all AI models, it may inadvertently perpetuate biases present in the training data.
- It should not be used for decision-making in critical applications without human oversight.

### Ethical Considerations
Users are encouraged to use this model responsibly. It should not be used to generate harmful, misleading, or biased content. Users should be aware of the potential for the model to reflect or amplify societal biases.

### How to Use
This model is available on the Hugging Face Model Hub. Users can integrate it into their applications using the Hugging Face Transformers library. Example usage:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "kz919/mistral-7b-sft-open-orca-flan-50k"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype = torch.bfloat16)

# Example usage here
inputs = tokenizer("Hello, how are you?", return_tensors = "pt")
outputs = model.generate(**inputs, use_cache = True)
print(tokenizer.decode(outputs[0]))
```

{"language": ["en"], "license": "apache-2.0", "datasets": ["kz919/open-orca-flan-50k-synthetic-5-models"], "pipeline_tag": "text-generation"}
transformers safetensors mistral text-generation en dataset:kz919/open-orca-flan-50k-synthetic-5-models license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us