Instructions to use electricsheepafrica/chewie-1.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use electricsheepafrica/chewie-1.2 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("google/medgemma-4b-it")
model = PeftModel.from_pretrained(base_model, "electricsheepafrica/chewie-1.2")

Transformers

How to use electricsheepafrica/chewie-1.2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="electricsheepafrica/chewie-1.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("electricsheepafrica/chewie-1.2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use electricsheepafrica/chewie-1.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "electricsheepafrica/chewie-1.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "electricsheepafrica/chewie-1.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/electricsheepafrica/chewie-1.2

SGLang

How to use electricsheepafrica/chewie-1.2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "electricsheepafrica/chewie-1.2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "electricsheepafrica/chewie-1.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "electricsheepafrica/chewie-1.2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "electricsheepafrica/chewie-1.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use electricsheepafrica/chewie-1.2 with Docker Model Runner:
```
docker model run hf.co/electricsheepafrica/chewie-1.2
```

chewie-1.2 / README.md

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	---
	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