From 1cb87c7ff9e1d05474e1e37db964c61b79d2e8d1 Mon Sep 17 00:00:00 2001
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Model: adityak74/medfit-llm-3B
Source: Original Platform
---
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 README.md                        |  216 ++++
 config.json                      |   39 +
 model-00001-of-00002.safetensors |    3 +
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diff --git a/README.md b/README.md
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+---
+license: llama3.2
+language:
+- en
+base_model:
+- meta-llama/Llama-3.2-3B-Instruct
+library_name: transformers
+tags:
+- healthcare
+- medical
+- fine-tuned
+- chatbot
+- medical-qa
+- domain-specific
+- lora
+- healthcare-ai
+pipeline_tag: text-generation
+datasets:
+- synthetic-medical-qa
+---
+
+# MEDFIT-LLM-3B: Fine-tuned Llama-3.2-3B for Medical QA
+
+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.
+
+## Model Details
+
+### Model Description
+
+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.
+
+- **Developed by:** Aditya Karnam Gururaj Rao, Arjun Jaggi, Sonam Naidu
+- **Model type:** Causal Language Model (Fine-tuned)
+- **Language(s):** English
+- **License:** Llama 3.2 Community License
+- **Finetuned from model:** meta-llama/Llama-3.2-3B-Instruct
+- **Fine-tuning method:** LoRA (Low-Rank Adaptation)
+- **Training framework:** MLX
+
+### Model Sources
+
+- **Repository:** https://huggingface.co/adityak74/medfit-llm-3B
+- **Paper:** [MEDFIT-LLM: Medical Enhancements through Domain-Focused Fine Tuning of Small Language Models](https://github.com/adityak74/medfit-llm)
+- **Code:** https://github.com/adityak74/medfit-llm
+
+## Performance Highlights
+
+Based on comprehensive evaluation against the base Llama-3.2-3B-Instruct model:
+
+- **Direct Answer Improvement:** 30 percentage point increase (from 6.0% to 36.0%)
+- **Response Structure:** 18% increase in numbered list usage for better organization
+- **Overall Improvement Score:** 108.2 (highest among evaluated models)
+- **Response Length:** Slight increase (+2.84%) with more comprehensive answers
+
+## Uses
+
+### Direct Use
+
+MEDFIT-LLM-3B is designed for healthcare chatbot applications where accurate, well-structured medical information delivery is crucial. The model excels at:
+
+- **Medical Question Answering:** Providing direct, accurate responses to healthcare queries
+- **Patient Education:** Delivering structured, easy-to-understand medical information
+- **Healthcare Information Dissemination:** Supporting healthcare providers with reliable AI assistance
+- **Medical Chatbot Applications:** Serving as the backbone for healthcare conversational agents
+
+### Downstream Use
+
+The model can be integrated into:
+- Healthcare mobile applications
+- Medical information systems
+- Patient support platforms
+- Telemedicine chatbots
+- Medical education tools
+
+### Out-of-Scope Use
+
+**Important:** This model is NOT intended for:
+- **Medical diagnosis or treatment recommendations**
+- **Emergency medical situations**
+- **Replacement of professional medical advice**
+- **Clinical decision-making without human oversight**
+- **Prescription or medication recommendations**
+
+## Training Details
+
+### Training Data
+
+The Dataset is available here: https://huggingface.co/datasets/mlx-community/medfit-dataset
+
+The model was trained on a carefully curated dataset comprising:
+- **Total samples:** 6,444 unique healthcare-related question-answer pairs
+- **Training set:** 5,155 samples
+- **Validation set:** 644 samples  
+- **Test set:** 645 samples
+
+The dataset was created using:
+- **Synthetic data generation:** 10,000 initial samples generated using Phi-4
+- **Domain-specific curation:** Healthcare-focused questions derived from existing research
+- **Deduplication:** Filtered to remove duplicates, resulting in 6,444 unique samples
+
+### Training Procedure
+
+#### Fine-tuning Method
+- **Technique:** LoRA (Low-Rank Adaptation)
+- **Framework:** MLX
+- **Base model:** meta-llama/Llama-3.2-3B-Instruct
+- **Focus:** Healthcare domain specialization
+
+#### Training Hyperparameters
+- **Fine-tuning approach:** Domain-focused LoRA adaptation
+- **Dataset split:** 80% training, 10% validation, 10% testing
+- **Training regime:** Optimized for healthcare question-answering performance
+
+## Evaluation
+
+### Testing Data & Metrics
+
+The model was evaluated on:
+- **50 healthcare-specific validation questions**
+- **Comparative analysis** against base Llama-3.2-3B-Instruct
+- **Multi-dimensional assessment** including direct answer capability, response structure, and generation efficiency
+
+### Key Results
+
+**Direct Answer Performance:**
+- Base model: 6.0% direct answer rate
+- Fine-tuned model: 36.0% direct answer rate
+- **Improvement: +30.0 percentage points**
+
+**Response Quality:**
+- Enhanced structure with increased use of numbered lists (+18%)
+- Improved organization and systematic presentation
+- Better alignment with healthcare communication standards
+
+**Generation Efficiency:**
+- Slight increase in generation time (+1.6%)
+- Trade-off between response quality and speed
+- Overall positive impact on response comprehensiveness
+
+## Bias, Risks, and Limitations
+
+### Limitations
+- **Not a substitute for professional medical advice**
+- **May generate plausible-sounding but incorrect medical information**
+- **Limited to English language medical contexts**
+- **Training data may not cover all medical specialties equally**
+- **Performance may vary across different healthcare subdomains**
+
+### Recommendations
+
+- **Always verify medical information** with qualified healthcare professionals
+- **Use as a supplementary tool** rather than primary medical resource
+- **Implement human oversight** in all healthcare applications
+- **Regular updates** needed to maintain medical accuracy as knowledge evolves
+- **Consider integration with retrieval-augmented generation (RAG)** for enhanced factual accuracy
+
+## How to Get Started with the Model
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load the model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("adityak74/medfit-llm-3B")
+model = AutoModelForCausalLM.from_pretrained("adityak74/medfit-llm-3B")
+
+# Example usage
+prompt = "What are the common symptoms of diabetes?"
+inputs = tokenizer.encode(prompt, return_tensors="pt")
+outputs = model.generate(inputs, max_length=200, temperature=0.7)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+## Environmental Impact
+
+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.
+
+## Citation
+
+**BibTeX:**
+```bibtex
+@inproceedings{rao2025medfit,
+  title={MEDFIT-LLM: Medical Enhancements through Domain-Focused Fine Tuning of Small Language Models},
+  author={Rao, Aditya Karnam Gururaj and Jaggi, Arjun and Naidu, Sonam},
+  booktitle={2025 2nd International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE)},
+  year={2025},
+  organization={IEEE}
+}
+```
+
+**APA:**
+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.
+
+## Glossary
+
+- **QA:** Question Answering
+- **EHR:** Electronic Health Record
+- **LoRA:** Low-Rank Adaptation - an efficient fine-tuning technique
+- **MLX:** Machine Learning framework used for training
+- **Direct Answer Rate:** Percentage of responses that begin with direct answers rather than preambles
+
+## Model Card Authors
+
+- **Aditya Karnam Gururaj Rao** (Zefr Inc, LA, USA)
+- **Arjun Jaggi** (HCLTech, LA, USA)  
+- **Sonam Naidu** (LexisNexis, USA)
+
+## Model Card Contact
+
+- **Primary Contact:** https://huggingface.co/adityak74
+- **Email:** akarnam37@gmail.com
+- **GitHub:** https://github.com/adityak74/medfit-llm
+
+## Disclaimer
+
+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.
\ No newline at end of file
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+        128001,
+        128008,
+        128009
+    ],
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+        "rope_type": "llama3"
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+    "tie_word_embeddings": true,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.45.0.dev0",
+    "use_cache": true,
+    "vocab_size": 128256
+}
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128223": {
+      "content": "<|reserved_special_token_215|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128224": {
+      "content": "<|reserved_special_token_216|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128225": {
+      "content": "<|reserved_special_token_217|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128226": {
+      "content": "<|reserved_special_token_218|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128227": {
+      "content": "<|reserved_special_token_219|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128228": {
+      "content": "<|reserved_special_token_220|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128229": {
+      "content": "<|reserved_special_token_221|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128230": {
+      "content": "<|reserved_special_token_222|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128231": {
+      "content": "<|reserved_special_token_223|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128232": {
+      "content": "<|reserved_special_token_224|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128233": {
+      "content": "<|reserved_special_token_225|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128234": {
+      "content": "<|reserved_special_token_226|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128235": {
+      "content": "<|reserved_special_token_227|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128236": {
+      "content": "<|reserved_special_token_228|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128237": {
+      "content": "<|reserved_special_token_229|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128238": {
+      "content": "<|reserved_special_token_230|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128239": {
+      "content": "<|reserved_special_token_231|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128240": {
+      "content": "<|reserved_special_token_232|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128241": {
+      "content": "<|reserved_special_token_233|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128242": {
+      "content": "<|reserved_special_token_234|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128243": {
+      "content": "<|reserved_special_token_235|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128244": {
+      "content": "<|reserved_special_token_236|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128245": {
+      "content": "<|reserved_special_token_237|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128246": {
+      "content": "<|reserved_special_token_238|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128247": {
+      "content": "<|reserved_special_token_239|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128248": {
+      "content": "<|reserved_special_token_240|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128249": {
+      "content": "<|reserved_special_token_241|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128250": {
+      "content": "<|reserved_special_token_242|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128251": {
+      "content": "<|reserved_special_token_243|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128252": {
+      "content": "<|reserved_special_token_244|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128253": {
+      "content": "<|reserved_special_token_245|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128254": {
+      "content": "<|reserved_special_token_246|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128255": {
+      "content": "<|reserved_special_token_247|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|begin_of_text|>",
+  "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n    {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n    {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n    {%- if strftime_now is defined %}\n        {%- set date_string = strftime_now(\"%d %b %Y\") %}\n    {%- else %}\n        {%- set date_string = \"26 Jul 2024\" %}\n    {%- endif %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n    {%- set system_message = messages[0]['content']|trim %}\n    {%- set messages = messages[1:] %}\n{%- else %}\n    {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if tools is not none %}\n    {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n    {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n    {#- Extract the first user message so we can plug it in here #}\n    {%- if messages | length != 0 %}\n        {%- set first_user_message = messages[0]['content']|trim %}\n        {%- set messages = messages[1:] %}\n    {%- else %}\n        {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n    {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n    {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n    {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n    {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n    {%- elif 'tool_calls' in message %}\n        {%- if not message.tool_calls|length == 1 %}\n            {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n        {%- endif %}\n        {%- set tool_call = message.tool_calls[0].function %}\n        {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n        {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n        {{- '\"parameters\": ' }}\n        {{- tool_call.arguments | tojson }}\n        {{- \"}\" }}\n        {{- \"<|eot_id|>\" }}\n    {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n        {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n        {%- if message.content is mapping or message.content is iterable %}\n            {{- message.content | tojson }}\n        {%- else %}\n            {{- message.content }}\n        {%- endif %}\n        {{- \"<|eot_id|>\" }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "extra_special_tokens": {},
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}