Instructions to use michelinolinolino/gemma4-4b-sci with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use michelinolinolino/gemma4-4b-sci with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="michelinolinolino/gemma4-4b-sci",
	filename="model.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use michelinolinolino/gemma4-4b-sci with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf michelinolinolino/gemma4-4b-sci
# Run inference directly in the terminal:
llama cli -hf michelinolinolino/gemma4-4b-sci

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf michelinolinolino/gemma4-4b-sci
# Run inference directly in the terminal:
llama cli -hf michelinolinolino/gemma4-4b-sci

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf michelinolinolino/gemma4-4b-sci
# Run inference directly in the terminal:
./llama-cli -hf michelinolinolino/gemma4-4b-sci

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf michelinolinolino/gemma4-4b-sci
# Run inference directly in the terminal:
./build/bin/llama-cli -hf michelinolinolino/gemma4-4b-sci

Use Docker

docker model run hf.co/michelinolinolino/gemma4-4b-sci

LM Studio
Jan

vLLM

How to use michelinolinolino/gemma4-4b-sci with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "michelinolinolino/gemma4-4b-sci"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "michelinolinolino/gemma4-4b-sci",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/michelinolinolino/gemma4-4b-sci

Ollama
How to use michelinolinolino/gemma4-4b-sci with Ollama:
```
ollama run hf.co/michelinolinolino/gemma4-4b-sci
```

Unsloth Studio

How to use michelinolinolino/gemma4-4b-sci 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 michelinolinolino/gemma4-4b-sci 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 michelinolinolino/gemma4-4b-sci to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for michelinolinolino/gemma4-4b-sci to start chatting

Atomic Chat new
Docker Model Runner
How to use michelinolinolino/gemma4-4b-sci with Docker Model Runner:
```
docker model run hf.co/michelinolinolino/gemma4-4b-sci
```

Lemonade

How to use michelinolinolino/gemma4-4b-sci with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull michelinolinolino/gemma4-4b-sci

Run and chat with the model

lemonade run user.gemma4-4b-sci-{{QUANT_TAG}}

List all available models

lemonade list

gemma4-4b-sci / README.md

michelinolinolino

Update README.md

0c3ef7b verified 3 months ago

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	---
	license: gemma
	language:
	- en
	library_name: gguf
	tags:
	- gemma
	- gemma-4
	- scientific
	- qlora
	- unsloth
	- gguf
	- ollama
	- openscholar
	- sciriff
	- text-generation
	base_model: unsloth/gemma-4-E4B-it
	datasets:
	- OpenSciLM/OS_Train_Data
	- allenai/SciRIFF-train-mix
	pipeline_tag: text-generation
	---

	# gemma4-4b-sci

	> [!WARNING]
	> Early-stage research experiment. Trained for 1 epoch on 30K examples. Expect hallucinations and factual errors.

	gemma4-4b-sci is a scientific-domain fine-tune of [Gemma 4 E4B](https://huggingface.co/unsloth/gemma-4-E4B-it) via QLoRA on 30,000 examples from [OpenSciLM/OS_Train_Data](https://huggingface.co/datasets/OpenSciLM/OS_Train_Data) and [SciRIFF](https://huggingface.co/datasets/allenai/SciRIFF-train-mix). Inspired by [OpenScholar](https://allenai.org/blog/nature-openscilm) — this is a generation-only model without a retrieval pipeline.

	### Model Description

	- Developed by: Michele Banfi
	- Base model: `unsloth/gemma-4-E4B-it`
	- Method: QLoRA (4-bit) + SFT via Unsloth, language layers only (vision encoder frozen)
	- Training: 1 epoch, 30K examples (15K OS_Train_Data + 15K SciRIFF), NVIDIA RTX 5090
	- License: [Gemma Terms of Use](https://ai.google.dev/gemma/terms)

	### Model Sources

	- Repository: https://github.com/michelebanfi/gemma-4-finetuning
	- Evaluation: [ScholarQABench](https://github.com/AkariAsai/ScholarQABench)
	- Ollama: `ollama run hf.co/michelinolinolino/gemma4-4b-sci`

	## Quick Start

	```bash
	ollama run hf.co/michelinolinolino/gemma4-4b-sci
	```

	```python
	from transformers import AutoTokenizer, AutoModelForCausalLM
	import torch

	model = AutoModelForCausalLM.from_pretrained("michelinolinolino/gemma4-4b-sci", torch_dtype=torch.bfloat16, device_map="auto")
	tokenizer = AutoTokenizer.from_pretrained("michelinolinolino/gemma4-4b-sci")

	messages = [{"role": "user", "content": "Explain the role of CRISPR-Cas9 in gene editing."}]
	input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
	print(tokenizer.decode(model.generate(input_ids, max_new_tokens=512)[0][input_ids.shape[1]:], skip_special_tokens=True))
	```

	## Evaluation

	[ScholarQABench](https://github.com/AkariAsai/ScholarQABench) — draft results, 1-epoch run. Gold paper contexts provided (fair comparison with OpenScholar-8B).

	\| Task \| Metric \| gemma4-4b-sci \| OpenScholar-8B \|
	\|---\|---\|---:\|---:\|
	\| SciFact (208) \| Accuracy \| 77.9% \| 76.4% \|
	\| PubMedQA (843) \| Accuracy \| 81.5% \| 76.0% \|
	\| QASA (1375) \| ROUGE-L \| 20.9 \| 23.0 \|
	\| SciFact \| Citation F1 \| 0.0 \| 68.9 \|
	\| PubMedQA \| Citation F1 \| 0.0 \| 43.6 \|
	\| QASA \| Citation F1 \| 4.3 \| 56.3 \|

	Correctness matches or exceeds OpenScholar-8B (2× the parameters) at 1 epoch. Citation gap is entirely due to the missing retrieval pipeline.

	## Citation

	```bibtex
	@article{asai2024openscholar,
	title = {OpenScholar: Synthesizing Scientific Literature with Retrieval-Augmented LMs},
	author = {Asai, Akari and others},
	journal = {Nature},
	year = {2024},
	url = {https://allenai.org/blog/nature-openscilm}
	}
	```