README.md

---
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

<img src="https://huggingface.co/electricsheepafrica/chewie-1.2/resolve/main/chewie-banner.png" alt="Chewie Banner" width="100%"/>

**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