--- base_model: google/medgemma-4b-it library_name: peft pipeline_tag: text-generation license: apache-2.0 language: - en - sw - ha - yo - am - zu - fr tags: - medical - healthcare - africa - community-health - chw - lora - sft - transformers - trl - medgemma --- # Chewie 1.2: African Community Health Worker AI Assistant Chewie Banner **Chewie 1.2** is a medical AI assistant fine-tuned specifically for **Community Health Workers (CHWs)** in Africa. Built on Google's MedGemma-4B, it provides structured clinical guidance following a consistent **Assessment → Action → Advice** format. ## Model Details ### Model Description Chewie 1.2 is a LoRA adapter fine-tuned on top of [google/medgemma-4b-it](https://huggingface.co/google/medgemma-4b-it) using a curated dataset of 11,880 multilingual medical conversations designed for community health settings in Africa. - **Developed by:** [Electric Sheep Africa](https://huggingface.co/electricsheepafrica) - **Model type:** Causal Language Model (LoRA Adapter) - **Language(s):** English, Swahili, Hausa, Yoruba, Amharic, Zulu, French - **License:** Apache 2.0 - **Finetuned from:** [google/medgemma-4b-it](https://huggingface.co/google/medgemma-4b-it) ### Model Sources - **Repository:** [electricsheepafrica/chewie-1.2](https://huggingface.co/electricsheepafrica/chewie-1.2) - **Previous Version:** [electricsheepafrica/chewie-llama-3b](https://huggingface.co/electricsheepafrica/chewie-llama-3b) ## Intended Use ### Primary Use Cases - **Clinical Decision Support:** Helping CHWs assess symptoms and determine appropriate actions - **Triage Assistance:** Identifying danger signs that require immediate referral - **Health Education:** Providing patient-friendly explanations and preventive advice - **Multilingual Support:** Serving diverse African language communities ### Target Users - Community Health Workers (CHWs) - Primary healthcare providers in resource-limited settings - Health education programs - Mobile health (mHealth) applications ### Out-of-Scope Use - **NOT for direct patient diagnosis** - Always requires human clinical oversight - **NOT a replacement for professional medical care** - **NOT validated for emergency/critical care decisions** - Should not be used without proper clinical supervision ## How to Get Started ### Installation ```bash pip install transformers peft bitsandbytes accelerate ``` ### Inference Code ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig from peft import PeftModel # Load base model with 4-bit quantization base_model_id = "google/medgemma-4b-it" adapter_id = "electricsheepafrica/chewie-1.2" bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16, ) # Load base model base_model = AutoModelForCausalLM.from_pretrained( base_model_id, quantization_config=bnb_config, attn_implementation="eager", device_map="auto", ) # Load adapter model = PeftModel.from_pretrained(base_model, adapter_id) tokenizer = AutoTokenizer.from_pretrained(adapter_id) # Generate response def chat(prompt): messages = [{"role": "user", "content": prompt}] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors="pt" ).to(model.device) with torch.no_grad(): outputs = model.generate( inputs, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.9, ) return tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True) # Example usage response = chat("What are the danger signs in a child with diarrhea?") print(response) ``` ## Training Details ### Training Data - **Dataset Size:** 11,880 examples - **Languages:** English, Swahili, Hausa, Yoruba, Amharic, Zulu, French - **Domain:** Community health, primary care, maternal/child health, infectious diseases - **Format:** Structured Assessment → Action → Advice responses ### Training Procedure - **Base Model:** google/medgemma-4b-it - **Method:** LoRA (Low-Rank Adaptation) - **Framework:** Hugging Face TRL (SFTTrainer) #### Training Hyperparameters | Parameter | Value | |-----------|-------| | LoRA Rank (r) | 16 | | LoRA Alpha | 16 | | LoRA Dropout | 0.05 | | Target Modules | all-linear | | Learning Rate | 2e-4 | | Batch Size | 1-2 | | Gradient Accumulation | 8-16 | | Epochs | 1 | | Precision | bf16/fp16 | | Optimizer | AdamW (fused) | #### Training Results | Metric | Value | |--------|-------| | Final Training Loss | 1.08 | | Final Validation Loss | 1.13 | | Mean Token Accuracy | 72.5% | | Training Time | ~1 hour | ### Compute Infrastructure - **Hardware:** NVIDIA A100 40GB / T4 16GB (compatible with both) - **Cloud Provider:** Google Colab - **Training Framework:** Transformers, PEFT, TRL, BitsAndBytes ## Evaluation ### Sample Outputs **Input:** "What are the symptoms of malaria?" **Output:** ``` **Assessment:** Malaria can cause various symptoms, including fever, chills, sweating, headache, muscle aches, nausea, vomiting, and fatigue. In severe cases, it may lead to anemia, kidney failure, or brain damage. **Action:** If you or someone you know has a high fever (above 102°F or 39°C), persistent headaches, or unusual weakness, seek medical help immediately at a clinic. These could be danger signs for severe malaria. **Advice:** To prevent malaria, use insect repellent when outdoors, sleep under mosquito nets, and take preventative medications if recommended by your healthcare provider. ``` ## Limitations and Risks ### Known Limitations - Model outputs should always be verified by qualified healthcare professionals - May not reflect the most current medical guidelines - Performance may vary across different African languages - Not trained on region-specific drug formularies or protocols ### Ethical Considerations - This model is intended as a decision-support tool, not a replacement for clinical judgment - Healthcare providers should use their professional expertise alongside model outputs - Patient safety must always take precedence over model recommendations ## Citation ```bibtex @misc{chewie-1.2, author = {Electric Sheep Africa}, title = {Chewie 1.2: African Community Health Worker AI Assistant}, year = {2026}, publisher = {Hugging Face}, url = {https://huggingface.co/electricsheepafrica/chewie-1.2} } ``` ## Model Card Contact - **Organization:** [Electric Sheep Africa](https://huggingface.co/electricsheepafrica) - **Issues:** Please open an issue on the model repository --- ### Framework Versions - PEFT: 0.18.0 - Transformers: 4.x - TRL: 0.x - BitsAndBytes: 0.x - PyTorch: 2.x