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Model: adityak74/medfit-llm-3B Source: Original Platform
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README.md
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---
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license: llama3.2
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language:
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- en
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base_model:
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- meta-llama/Llama-3.2-3B-Instruct
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library_name: transformers
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tags:
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- healthcare
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- medical
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- fine-tuned
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- chatbot
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- medical-qa
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- domain-specific
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- lora
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- healthcare-ai
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pipeline_tag: text-generation
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datasets:
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- synthetic-medical-qa
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---
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# MEDFIT-LLM-3B: Fine-tuned Llama-3.2-3B for Medical QA
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MEDFIT-LLM-3B is a specialized language model fine-tuned from Meta's Llama-3.2-3B-Instruct for healthcare and medical question-answering applications. This model demonstrates significant improvements in direct answer capabilities and medical domain understanding through domain-focused fine-tuning.
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## Model Details
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### Model Description
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MEDFIT-LLM-3B is a 3 billion parameter language model specifically optimized for healthcare chatbot applications. The model was fine-tuned using LoRA (Low-Rank Adaptation) techniques on a carefully curated dataset of healthcare-related questions and answers, resulting in enhanced performance for medical information dissemination and patient education.
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- **Developed by:** Aditya Karnam Gururaj Rao, Arjun Jaggi, Sonam Naidu
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- **Model type:** Causal Language Model (Fine-tuned)
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- **Language(s):** English
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- **License:** Llama 3.2 Community License
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- **Finetuned from model:** meta-llama/Llama-3.2-3B-Instruct
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- **Fine-tuning method:** LoRA (Low-Rank Adaptation)
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- **Training framework:** MLX
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### Model Sources
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- **Repository:** https://huggingface.co/adityak74/medfit-llm-3B
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- **Paper:** [MEDFIT-LLM: Medical Enhancements through Domain-Focused Fine Tuning of Small Language Models](https://github.com/adityak74/medfit-llm)
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- **Code:** https://github.com/adityak74/medfit-llm
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## Performance Highlights
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Based on comprehensive evaluation against the base Llama-3.2-3B-Instruct model:
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- **Direct Answer Improvement:** 30 percentage point increase (from 6.0% to 36.0%)
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- **Response Structure:** 18% increase in numbered list usage for better organization
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- **Overall Improvement Score:** 108.2 (highest among evaluated models)
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- **Response Length:** Slight increase (+2.84%) with more comprehensive answers
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## Uses
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### Direct Use
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MEDFIT-LLM-3B is designed for healthcare chatbot applications where accurate, well-structured medical information delivery is crucial. The model excels at:
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- **Medical Question Answering:** Providing direct, accurate responses to healthcare queries
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- **Patient Education:** Delivering structured, easy-to-understand medical information
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- **Healthcare Information Dissemination:** Supporting healthcare providers with reliable AI assistance
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- **Medical Chatbot Applications:** Serving as the backbone for healthcare conversational agents
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### Downstream Use
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The model can be integrated into:
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- Healthcare mobile applications
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- Medical information systems
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- Patient support platforms
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- Telemedicine chatbots
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- Medical education tools
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### Out-of-Scope Use
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**Important:** This model is NOT intended for:
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- **Medical diagnosis or treatment recommendations**
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- **Emergency medical situations**
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- **Replacement of professional medical advice**
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- **Clinical decision-making without human oversight**
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- **Prescription or medication recommendations**
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## Training Details
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### Training Data
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The Dataset is available here: https://huggingface.co/datasets/mlx-community/medfit-dataset
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The model was trained on a carefully curated dataset comprising:
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- **Total samples:** 6,444 unique healthcare-related question-answer pairs
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- **Training set:** 5,155 samples
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- **Validation set:** 644 samples
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- **Test set:** 645 samples
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The dataset was created using:
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- **Synthetic data generation:** 10,000 initial samples generated using Phi-4
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- **Domain-specific curation:** Healthcare-focused questions derived from existing research
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- **Deduplication:** Filtered to remove duplicates, resulting in 6,444 unique samples
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### Training Procedure
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#### Fine-tuning Method
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- **Technique:** LoRA (Low-Rank Adaptation)
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- **Framework:** MLX
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- **Base model:** meta-llama/Llama-3.2-3B-Instruct
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- **Focus:** Healthcare domain specialization
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#### Training Hyperparameters
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- **Fine-tuning approach:** Domain-focused LoRA adaptation
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- **Dataset split:** 80% training, 10% validation, 10% testing
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- **Training regime:** Optimized for healthcare question-answering performance
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## Evaluation
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### Testing Data & Metrics
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The model was evaluated on:
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- **50 healthcare-specific validation questions**
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- **Comparative analysis** against base Llama-3.2-3B-Instruct
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- **Multi-dimensional assessment** including direct answer capability, response structure, and generation efficiency
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### Key Results
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**Direct Answer Performance:**
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- Base model: 6.0% direct answer rate
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- Fine-tuned model: 36.0% direct answer rate
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- **Improvement: +30.0 percentage points**
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**Response Quality:**
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- Enhanced structure with increased use of numbered lists (+18%)
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- Improved organization and systematic presentation
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- Better alignment with healthcare communication standards
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**Generation Efficiency:**
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- Slight increase in generation time (+1.6%)
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- Trade-off between response quality and speed
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- Overall positive impact on response comprehensiveness
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## Bias, Risks, and Limitations
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### Limitations
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- **Not a substitute for professional medical advice**
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- **May generate plausible-sounding but incorrect medical information**
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- **Limited to English language medical contexts**
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- **Training data may not cover all medical specialties equally**
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- **Performance may vary across different healthcare subdomains**
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### Recommendations
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- **Always verify medical information** with qualified healthcare professionals
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- **Use as a supplementary tool** rather than primary medical resource
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- **Implement human oversight** in all healthcare applications
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- **Regular updates** needed to maintain medical accuracy as knowledge evolves
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- **Consider integration with retrieval-augmented generation (RAG)** for enhanced factual accuracy
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## How to Get Started with the Model
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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# Load the model and tokenizer
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tokenizer = AutoTokenizer.from_pretrained("adityak74/medfit-llm-3B")
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model = AutoModelForCausalLM.from_pretrained("adityak74/medfit-llm-3B")
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# Example usage
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prompt = "What are the common symptoms of diabetes?"
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inputs = tokenizer.encode(prompt, return_tensors="pt")
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outputs = model.generate(inputs, max_length=200, temperature=0.7)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(response)
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```
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## Environmental Impact
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The fine-tuning process utilized efficient LoRA techniques to minimize computational requirements while maximizing performance improvements. This approach reduces the environmental impact compared to full model training while achieving significant domain-specific enhancements.
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## Citation
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**BibTeX:**
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```bibtex
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@inproceedings{rao2025medfit,
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title={MEDFIT-LLM: Medical Enhancements through Domain-Focused Fine Tuning of Small Language Models},
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author={Rao, Aditya Karnam Gururaj and Jaggi, Arjun and Naidu, Sonam},
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booktitle={2025 2nd International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE)},
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year={2025},
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organization={IEEE}
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}
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```
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**APA:**
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Rao, A. K. G., Jaggi, A., & Naidu, S. (2025). MEDFIT-LLM: Medical Enhancements through Domain-Focused Fine Tuning of Small Language Models. In *2025 2nd International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE)*. IEEE.
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## Glossary
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- **QA:** Question Answering
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- **EHR:** Electronic Health Record
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- **LoRA:** Low-Rank Adaptation - an efficient fine-tuning technique
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- **MLX:** Machine Learning framework used for training
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- **Direct Answer Rate:** Percentage of responses that begin with direct answers rather than preambles
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## Model Card Authors
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- **Aditya Karnam Gururaj Rao** (Zefr Inc, LA, USA)
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- **Arjun Jaggi** (HCLTech, LA, USA)
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- **Sonam Naidu** (LexisNexis, USA)
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## Model Card Contact
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- **Primary Contact:** https://huggingface.co/adityak74
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- **Email:** akarnam37@gmail.com
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- **GitHub:** https://github.com/adityak74/medfit-llm
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## Disclaimer
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This model is designed for educational and informational purposes in healthcare contexts. It should not be used as a substitute for professional medical advice, diagnosis, or treatment. Always seek the advice of qualified healthcare providers with questions regarding medical conditions or treatments.
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config.json
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"hidden_act": "silu",
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"hidden_size": 3072,
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"transformers_version": "4.45.0.dev0",
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"use_cache": true,
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"vocab_size": 128256
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||||
"model.layers.9.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.norm.weight": "model-00002-of-00002.safetensors"
|
||||
}
|
||||
}
|
||||
16
special_tokens_map.json
Normal file
16
special_tokens_map.json
Normal file
@@ -0,0 +1,16 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|eot_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:6b9e4e7fb171f92fd137b777cc2714bf87d11576700a1dcd7a399e7bbe39537b
|
||||
size 17209920
|
||||
2063
tokenizer_config.json
Normal file
2063
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user