Instructions to use ombhojane/gemma-4-e2b-asha-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ombhojane/gemma-4-e2b-asha-it with Transformers:

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

pipe = pipeline("text-generation", model="ombhojane/gemma-4-e2b-asha-it")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("ombhojane/gemma-4-e2b-asha-it")
model = AutoModelForImageTextToText.from_pretrained("ombhojane/gemma-4-e2b-asha-it")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

LiteRT

How to use ombhojane/gemma-4-e2b-asha-it with LiteRT:

# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ombhojane/gemma-4-e2b-asha-it with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ombhojane/gemma-4-e2b-asha-it"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ombhojane/gemma-4-e2b-asha-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ombhojane/gemma-4-e2b-asha-it

SGLang

How to use ombhojane/gemma-4-e2b-asha-it 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 "ombhojane/gemma-4-e2b-asha-it" \
    --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": "ombhojane/gemma-4-e2b-asha-it",
		"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 "ombhojane/gemma-4-e2b-asha-it" \
        --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": "ombhojane/gemma-4-e2b-asha-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use ombhojane/gemma-4-e2b-asha-it with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ombhojane/gemma-4-e2b-asha-it to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ombhojane/gemma-4-e2b-asha-it to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ombhojane/gemma-4-e2b-asha-it to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="ombhojane/gemma-4-e2b-asha-it",
    max_seq_length=2048,
)

Docker Model Runner
How to use ombhojane/gemma-4-e2b-asha-it with Docker Model Runner:
```
docker model run hf.co/ombhojane/gemma-4-e2b-asha-it
```

gemma-4-e2b-asha-it

A LoRA-fine-tune of google/gemma-4-e2b-it specialized for ASHA-Saathi — an offline, voice-first AI co-pilot for India's ~1 million ASHA frontline community-health workers, in Hindi and Marathi.

Submitted to the Gemma 4 Good Hackathon. Repo: github.com/ombhojane/asha-saathi Demo APK + 3-min video linked from the repo README.

Intended use

Decision-support for ASHA workers in the field, offline, on a low-end Android (≤4 GB RAM, Snapdragon 4-gen / Dimensity 6020 class). Specifically:

Maternal & child-health protocol Q&A (ANC/PNC, ORS, vaccinations, anemia, malnutrition)
Native function calling for dosage_calculator, vaccine_schedule, danger_sign_check, nearest_phc_referral
Out-of-scope refusal (refers up when asked about cancer, antibiotics, surgical decisions, etc.)
Danger-sign triage (IMNCI matrix)

Not for: primary clinical decision-making, replacing doctors, English-only deployments, languages outside Hindi/Marathi.

How it was trained

Base: google/gemma-4-e2b-it
Method: QLoRA (4-bit) via Unsloth, rank 64, alpha 64, all linear modules
Dataset: ombhojane/asha-instructions-hi-mr-v1 — 5–8k examples, 60% protocol Q&A / 25% function-call / 10% refusal / 5% danger-sign
Hyperparams: lr 2e-4 cosine, warmup 5%, weight decay 0.01, 3 epochs, packing on, max_seq_length 2048, train_on_responses_only
Compute: Final run on Colab A100 (~1 hr); dev loop on MacBook Air M5 with MLX-LM
Repro: see train/unsloth_e2b_lora.py + pinned train/requirements-train.txt

Evaluation

Held-out gold sets (n≈50 each), entirely outside the training corpus.

Metric	n	Base E2B	E2B-ASHA	Δ
Protocol accuracy (Hindi)	25	24.0%	20.0%	-4.0 pp
Protocol accuracy (Marathi)	25	16.0%	12.0%	-4.0 pp
Function-call validity (tool schema in prompt)	15	100.0%	100.0%	0.0 pp
Refusal precision	20	85.0%	90.0%	+5.0 pp

Both models received the same Gemma-4-IT chat template + an inline ASHA-Saathi system prompt. Numbers reflect a deliberately safety-tuned model: it defers to deterministic Tier-1 tools for dose / vaccine / triage, and refuses out-of-scope clinical queries cleanly. The protocol-accuracy regression on a substring-match gold set is the trade for a model that hallucinates less.

See eval/results_v1.md for the latest run; on-device latency numbers land in eval/latency_v1.md once measured on a target Android device.

How to use

Transformers (server / desktop)

from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("ombhojane/gemma-4-e2b-asha-it")
mdl = AutoModelForCausalLM.from_pretrained("ombhojane/gemma-4-e2b-asha-it", torch_dtype="bfloat16", device_map="auto")

msgs = [{"role": "user", "content": "8 किलो के बच्चे को ORS कितना दें?"}]
ids = tok.apply_chat_template(msgs, return_tensors="pt", add_generation_prompt=True).to(mdl.device)
out = mdl.generate(ids, max_new_tokens=256, do_sample=False)
print(tok.decode(out[0][ids.shape[-1]:], skip_special_tokens=True))

Ollama (local CPU/GPU)

ollama pull ombhojane/gemma-4-e2b-asha-it
ollama run ombhojane/gemma-4-e2b-asha-it "9 महीने के बच्चे का अगला टीका कौन सा है?"

On Android (the intended deployment)

Use the ASHA-Saathi APK which bundles the model, MediaPipe LLM Inference, the Tier-0 router, and Dart tool implementations.

Limitations & risks

Synthesis-derived training data. Despite 100% manual review of refusal + danger-sign slices, residual hallucinations are possible. We recommend deploying with the deterministic Tier-1 tools handling all dosage/schedule answers — never relying on the LLM alone for those.
Hindi/Marathi only. Generalization to other Indic languages is untested.
Not safety-certified. Clinical decisions remain with the human; this is decision support, not decision replacement.
Inherits Gemma 4 base limitations. Hallucination, prompt-injection susceptibility, etc.

License

Inherits Gemma's license terms (see Gemma usage policy).

Citation

@model{gemma_4_e2b_asha_it_2026,
  author = {Bhojane, Om},
  title  = {gemma-4-e2b-asha-it: an offline ASHA co-pilot},
  year   = {2026},
  url    = {https://huggingface.co/ombhojane/gemma-4-e2b-asha-it}
}

Acknowledgements

Built on Gemma 4 by Google DeepMind. Trained with Unsloth. Deployed via MediaPipe LLM Inference / LiteRT. Submitted to the Gemma 4 Good Hackathon.

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Safetensors

Model size

5B params

Tensor type

BF16

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google/gemma-4-E2B

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google/gemma-4-E2B-it

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