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Model: deep-div/MediLlama-3.2
Source: Original Platform

README.md

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+---
+library_name: transformers
+tags:
+- unsloth
+- trl
+- sft
+license: apache-2.0
+language:
+- en
+base_model:
+- meta-llama/Llama-3.2-3B-Instruct
+pipeline_tag: text-generation
+metrics:
+- accuracy
+- bleu
+- rouge
+---
+
+# Model Card for MediLlama-3.2
+
+A fine-tuned version of Meta's LLaMA 3.2 (3B Instruct) for domain-specific applications in healthcare and medicine. This model is optimized for tasks such as medical Q&A, symptom checking, and patient education.
+
+## Model Details
+
+### Model Description
+
+This model is a domain-adapted version of LLaMA 3.2 3B Instruct. It has been fine-tuned using supervised fine-tuning (SFT) on medical datasets to handle English-language healthcare scenarios including diagnostic queries, treatment suggestions, and general medical advice.
+
+- **Developed by:** InferenceLab  
+- **Model type:** Medical Chatbot  
+- **Language(s) (NLP):** English  
+- **License:** Apache 2.0  
+- **Finetuned from model:** meta-llama/Llama-3.2-3B-Instruct  
+
+## Uses
+
+### Direct Use
+
+MediLlama-3.2 can be used directly as a chatbot or virtual assistant in medical and health-related applications. Ideal for educational content, initial symptom triage, and research purposes.
+
+### Downstream Use
+
+Can be integrated into larger telehealth systems, clinical documentation tools, or diagnostic assistants after further task-specific fine-tuning.
+
+### Out-of-Scope Use
+
+- Should not be used for real-time diagnosis or treatment decisions without expert validation.  
+- Not suitable for high-risk or life-threatening emergency response.  
+- Not trained on pediatric or highly specialized medical domains.  
+
+## Bias, Risks, and Limitations
+
+While the model is trained on medical data, it may still exhibit:  
+- Biases from source data  
+- Hallucinations or incorrect suggestions  
+- Outdated or non-region-specific medical advice  
+
+### Recommendations
+
+Users should validate outputs with certified medical professionals. This model is for research and prototyping only, not for clinical deployment without regulatory compliance.
+
+## How to Get Started with the Model
+
+```python
+import torch
+from transformers import pipeline
+
+model_id = "InferenceLab/MediLlama-3.2"
+pipe = pipeline(
+    "text-generation",
+    model=model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+messages = [
+    {"role": "system", "content": "You are a helpful Medical assistant."},
+    {"role": "user", "content": "Hi! How are you?"},
+]
+outputs = pipe(
+    messages,
+    max_new_tokens=256,
+)
+print(outputs[0]["generated_text"][-1])
+
+````
+
+## Training Details
+
+### Training Data
+
+Model trained using cleaned and preprocessed medical QA datasets, synthetic doctor-patient conversations, and publicly available health forums. Protected health information (PHI) was removed.
+
+### Training Procedure
+
+Supervised fine-tuning (SFT) using TRL and Unsloth libraries.
+
+#### Preprocessing
+
+Tokenization using LLaMA tokenizer with special medical instruction formatting.
+
+#### Training Hyperparameters
+
+* **Training regime:** bf16 mixed precision
+* **Learning rate:** 1e-5
+
+#### Speeds, Sizes, Times
+
+* **Training time:** \~12 hours on 4×A100 GPUs
+
+## Evaluation
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+Subset of unseen medical QA pairs, synthetic test cases, and MedQA-derived examples.
+
+#### Factors
+
+* Input prompt complexity
+* Use of medical terminology
+* Chat length
+
+#### Metrics
+
+* **Accuracy:** 81.3%
+* **BLEU:** 34.5
+* **ROUGE-L:** 62.2
+
+### Results
+
+#### Summary
+
+Model shows good generalization to unseen prompts and performs competitively for general medical dialogue. Further tuning needed for specialty areas like oncology or rare diseases.
+
+## Model Examination
+
+Explainability tools like LLaMA-MedLens (if available) are suggested to interpret model decisions.
+
+## Environmental Impact
+
+* **Hardware Type:** 4×NVIDIA A100 40GB
+* **Hours used:** 12
+* **Cloud Provider:** AWS
+* **Compute Region:** us-west-2
+* **Carbon Emitted:** \~35.8 kg CO2eq (estimated)
+
+## Technical Specifications
+
+### Model Architecture and Objective
+
+* Based on Meta LLaMA 3.2 3B Instruct
+* Decoder-only transformer
+* Objective: Causal Language Modeling (CLM) with instruction fine-tuning
+
+### Compute Infrastructure
+
+#### Hardware
+
+* 4×NVIDIA A100 40GB
+
+#### Software
+
+* Python 3.10
+* Transformers (v4.40+)
+* TRL
+* Unsloth
+* PyTorch 2.1
+
+
+## Glossary
+
+* **SFT**: Supervised Fine-Tuning
+* **BLEU**: Bilingual Evaluation Understudy
+* **ROUGE**: Recall-Oriented Understudy for Gisting Evaluation
+
+## More Information
+
+For collaborations, deployment help, or fine-tuning extensions, please contact the developers.
+
+## Model Card Authors
+
+* InferenceLab Team
+
+
+
+