Text Generation
GGUF
Safetensors
English
gemma4
gemma
gemma-4
scientific
qlora
unsloth
ollama
openscholar
sciriff
conversational
Instructions to use michelinolinolino/gemma4-4b-sci with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
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
| { | |
| "audio_ms_per_token": 40, | |
| "audio_seq_length": 750, | |
| "feature_extractor": { | |
| "dither": 0.0, | |
| "feature_extractor_type": "Gemma4AudioFeatureExtractor", | |
| "feature_size": 128, | |
| "fft_length": 512, | |
| "fft_overdrive": false, | |
| "frame_length": 320, | |
| "hop_length": 160, | |
| "input_scale_factor": 1.0, | |
| "max_frequency": 8000.0, | |
| "mel_floor": 0.001, | |
| "min_frequency": 0.0, | |
| "padding_side": "left", | |
| "padding_value": 0.0, | |
| "per_bin_mean": null, | |
| "per_bin_stddev": null, | |
| "preemphasis": 0.0, | |
| "preemphasis_htk_flavor": true, | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000 | |
| }, | |
| "image_processor": { | |
| "do_convert_rgb": true, | |
| "do_normalize": false, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.0, | |
| 0.0, | |
| 0.0 | |
| ], | |
| "image_processor_type": "Gemma4ImageProcessor", | |
| "image_seq_length": 280, | |
| "image_std": [ | |
| 1.0, | |
| 1.0, | |
| 1.0 | |
| ], | |
| "max_soft_tokens": 280, | |
| "patch_size": 16, | |
| "pooling_kernel_size": 3, | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098 | |
| }, | |
| "image_seq_length": 280, | |
| "processor_class": "Gemma4Processor", | |
| "video_processor": { | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "do_sample_frames": true, | |
| "image_mean": [ | |
| 0.0, | |
| 0.0, | |
| 0.0 | |
| ], | |
| "image_std": [ | |
| 1.0, | |
| 1.0, | |
| 1.0 | |
| ], | |
| "max_soft_tokens": 70, | |
| "num_frames": 32, | |
| "patch_size": 16, | |
| "pooling_kernel_size": 3, | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "return_metadata": false, | |
| "video_processor_type": "Gemma4VideoProcessor" | |
| } | |
| } | |