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README.md
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base_model: mistralai/Mistral-7B-Instruct-v0.2
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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### Framework versions
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- PEFT 0.10.0
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base_model: mistralai/Mistral-7B-Instruct-v0.2
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# Medical-Mixtral-7B-v2k
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## Description
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Fine-tuned Mixtral model for answering medical assistance questions. This model is a novel version of mistralai/Mixtral-8x7B-Instruct-v0.1, adapted to a subset of 2.0k records from the AI Medical Chatbot dataset, which contains 250k records (https://huggingface.co/datasets/ruslanmv/ai-medical-chatbot). The purpose of this model is to provide a ready chatbot to answer questions related to medical assistance.
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## Intended Use
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This model is intended for providing assistance and answering questions related to medical inquiries. It is suitable for use in chatbot applications where users seek medical advice, information, or assistance.
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## Installation
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pip install -qU transformers==4.36.2 datasets python-dotenv peft bitsandbytes accelerate
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## Example Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# Define the name of your fine-tuned model
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finetuned_model = 'ruslanmv/Medical-Mixtral-7B-v2k'
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained(finetuned_model, trust_remote_code=True)
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# Load the model with the provided adapter configuration and weights
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model_pretrained = AutoModelForCausalLM.from_pretrained(finetuned_model, trust_remote_code=True, torch_dtype=torch.float16)
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messages = [
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{'role': 'user', 'content': 'What should I do to reduce my weight gained due to genetic hypothyroidism?'},
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{'role': 'assistant', 'content': ''},
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]
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input_ids = tokenizer.apply_chat_template(messages, return_tensors='pt').to('cuda')
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outputs = model_pretrained.generate(input_ids, max_new_tokens=500)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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For Gpus
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```python
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# Define the name of your fine-tuned model
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finetuned_model = 'ruslanmv/{new_model}'
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# Load fine-tuned model
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bnb_config = BitsAndBytesConfig(
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load_in_4bit= True,
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bnb_4bit_quant_type= "nf4",
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bnb_4bit_compute_dtype= torch.bfloat16,
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bnb_4bit_use_double_quant= False,
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)
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model_pretrained = AutoModelForCausalLM.from_pretrained(
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finetuned_model,
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load_in_4bit=True,
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quantization_config=bnb_config,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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trust_remote_code=True,
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cache_dir=cache_dir
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)
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained(finetuned_model,
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trust_remote_code=True,
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cache_dir=cache_dir)
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pipe = pipeline(task="text-generation",
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model=model_pretrained,
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tokenizer=tokenizer, max_length=200)
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def build_prompt(question):
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prompt=f"[INST]@Enlighten. {question} [/INST]"
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return prompt
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question = "What does abutment of the nerve root mean?"
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prompt = build_prompt(question)
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result = pipe(prompt)
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```
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## Training Data
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- **Dataset Name:** AI Medical Chatbot
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- **Dataset URL:** https://huggingface.co/datasets/ruslanmv/ai-medical-chatbot
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- **Dataset Size:** 250k records
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- **Subset Used:** 2.0k records
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## Limitations
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The model's performance may vary depending on the complexity and specificity of the medical questions.
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The model may not provide accurate answers for every medical query, and users should consult medical professionals for critical healthcare concerns.
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## Ethical Considerations
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Users should be informed that the model's responses are generated based on patterns in the training data and may not always be accurate or suitable for medical decision-making.
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The model should not be used as a replacement for professional medical advice or diagnosis.
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Sensitive patient data should not be shared with the model, and user privacy should be protected.
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