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_token": "<|audio|>", | |
| "backend": "tokenizers", | |
| "boa_token": "<|audio>", | |
| "boi_token": "<|image>", | |
| "bos_token": "<bos>", | |
| "eoa_token": "<audio|>", | |
| "eoc_token": "<channel|>", | |
| "eoi_token": "<image|>", | |
| "eos_token": "<turn|>", | |
| "eot_token": "<turn|>", | |
| "escape_token": "<|\"|>", | |
| "etc_token": "<tool_call|>", | |
| "etd_token": "<tool|>", | |
| "etr_token": "<tool_response|>", | |
| "extra_special_tokens": [ | |
| "<|video|>" | |
| ], | |
| "image_token": "<|image|>", | |
| "is_local": false, | |
| "mask_token": "<mask>", | |
| "model_max_length": 131072, | |
| "model_specific_special_tokens": { | |
| "audio_token": "<|audio|>", | |
| "boa_token": "<|audio>", | |
| "boi_token": "<|image>", | |
| "eoa_token": "<audio|>", | |
| "eoc_token": "<channel|>", | |
| "eoi_token": "<image|>", | |
| "eot_token": "<turn|>", | |
| "escape_token": "<|\"|>", | |
| "etc_token": "<tool_call|>", | |
| "etd_token": "<tool|>", | |
| "etr_token": "<tool_response|>", | |
| "image_token": "<|image|>", | |
| "soc_token": "<|channel>", | |
| "sot_token": "<|turn>", | |
| "stc_token": "<|tool_call>", | |
| "std_token": "<|tool>", | |
| "str_token": "<|tool_response>", | |
| "think_token": "<|think|>" | |
| }, | |
| "pad_token": "<pad>", | |
| "padding_side": "right", | |
| "processor_class": "Gemma4Processor", | |
| "response_schema": { | |
| "properties": { | |
| "content": { | |
| "type": "string" | |
| }, | |
| "role": { | |
| "const": "assistant" | |
| }, | |
| "thinking": { | |
| "type": "string" | |
| }, | |
| "tool_calls": { | |
| "items": { | |
| "properties": { | |
| "function": { | |
| "properties": { | |
| "arguments": { | |
| "additionalProperties": {}, | |
| "type": "object", | |
| "x-parser": "gemma4-tool-call" | |
| }, | |
| "name": { | |
| "type": "string" | |
| } | |
| }, | |
| "type": "object", | |
| "x-regex": "call\\:(?P<name>\\w+)(?P<arguments>\\{.*\\})" | |
| }, | |
| "type": { | |
| "const": "function" | |
| } | |
| }, | |
| "type": "object" | |
| }, | |
| "type": "array", | |
| "x-regex-iterator": "<\\|tool_call>(.*?)<tool_call\\|>" | |
| } | |
| }, | |
| "type": "object", | |
| "x-regex": "(\\<\\|channel\\>thought\\n(?P<thinking>.*?)\\<channel\\|\\>)?(?P<tool_calls>\\<\\|tool_call\\>.*\\<tool_call\\|\\>)?(?P<content>(?:(?!\\<turn\\|\\>)(?!\\<\\|tool_response\\>).)+)?(?:\\<turn\\|\\>|\\<\\|tool_response\\>)?" | |
| }, | |
| "soc_token": "<|channel>", | |
| "sot_token": "<|turn>", | |
| "stc_token": "<|tool_call>", | |
| "std_token": "<|tool>", | |
| "str_token": "<|tool_response>", | |
| "think_token": "<|think|>", | |
| "tokenizer_class": "GemmaTokenizer", | |
| "unk_token": "<unk>", | |
| "chat_template": "{{ bos_token }}{%- if messages[0]['role'] == 'system' -%}\n {%- set first_user_prefix = messages[0]['content'] + '\n\n' -%}\n {%- set loop_messages = messages[1:] -%}\n{%- else -%}\n {%- set first_user_prefix = \"\" -%}\n {%- set loop_messages = messages -%}\n{%- endif -%}\n{%- for message in loop_messages -%}\n {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}\n {{ raise_exception(\"Conversation roles must alternate user/assistant/user/assistant/...\") }}\n {%- endif -%}\n {%- if (message['role'] == 'assistant') -%}\n {%- set role = \"model\" -%}\n {%- else -%}\n {%- set role = message['role'] -%}\n {%- endif -%}\n {{ '<|turn>' + role + '\n' + (first_user_prefix if loop.first else \"\") }}\n {%- if message['content'] is string -%}\n {{ message['content'] | trim }}\n {%- elif message['content'] is iterable -%}\n {%- for item in message['content'] -%}\n {%- if item['type'] == 'audio' -%}\n {{ '<|audio|>' }}\n {%- elif item['type'] == 'image' -%}\n {{ '<|image|>' }}\n {%- elif item['type'] == 'video' -%}\n {{ '<|video|>' }}\n {%- elif item['type'] == 'text' -%}\n {{ item['text'] | trim }}\n {%- endif -%}\n {%- endfor -%}\n {%- else -%}\n {{ raise_exception(\"Invalid content type\") }}\n {%- endif -%}\n {{ '<turn|>\n' }}\n{%- endfor -%}\n{%- if add_generation_prompt -%}\n {{'<|turn>model\n'}}\n{%- endif -%}\n" | |
| } |