Instructions to use innerloop-dev/gemma3-4b-text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use innerloop-dev/gemma3-4b-text 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 innerloop-dev/gemma3-4b-text:Q4_K_M # Run inference directly in the terminal: llama cli -hf innerloop-dev/gemma3-4b-text:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf innerloop-dev/gemma3-4b-text:Q4_K_M # Run inference directly in the terminal: llama cli -hf innerloop-dev/gemma3-4b-text:Q4_K_M
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 innerloop-dev/gemma3-4b-text:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf innerloop-dev/gemma3-4b-text:Q4_K_M
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 innerloop-dev/gemma3-4b-text:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf innerloop-dev/gemma3-4b-text:Q4_K_M
Use Docker
docker model run hf.co/innerloop-dev/gemma3-4b-text:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use innerloop-dev/gemma3-4b-text with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "innerloop-dev/gemma3-4b-text" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "innerloop-dev/gemma3-4b-text", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/innerloop-dev/gemma3-4b-text:Q4_K_M
- Ollama
How to use innerloop-dev/gemma3-4b-text with Ollama:
ollama run hf.co/innerloop-dev/gemma3-4b-text:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use innerloop-dev/gemma3-4b-text with Docker Model Runner:
docker model run hf.co/innerloop-dev/gemma3-4b-text:Q4_K_M
- Lemonade
How to use innerloop-dev/gemma3-4b-text with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull innerloop-dev/gemma3-4b-text:Q4_K_M
Run and chat with the model
lemonade run user.gemma3-4b-text-Q4_K_M
List all available models
lemonade list
- Atomic Chat
File size: 1,237 Bytes
7483964 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | NOTICE
Gemma is provided under and subject to the Gemma Terms of Use found at ai.google.dev/gemma/terms
This distribution contains a modified Gemma file. See MODIFICATIONS.
Everything in this distribution that is not Gemma is © Innerloop, innerloop.works
MODIFICATIONS
This file is a modified Gemma file.
Source: the model layer of Ollama's library build of google/gemma-3-4b-it,
Q4_K_M, sha256 aeda25e63ebd698fab8638ffb778e68bed908b960d39d0becc650fa981609d25,
3,338,792,448 bytes.
Modification: the SigLIP vision tower and the multimodal projection were removed —
439 tensors (v.*, mm.*) and 9 metadata keys (gemma3.vision.*, gemma3.mm.*).
The 444 text tensors are byte-for-byte the originals: same name, same ggml
quantization type, same shape, same bytes. Nothing was requantized, converted
or retrained. The tokenizer is unchanged.
Result: 2,498,332,864 bytes, sha256
199388f8f8cbec06b80bd63b0b1a774103e00c993c107bf1d480e58047420532.
The file cannot read images. It writes text.
Modified by Innerloop (innerloop.works) on 2026-09-19 with strip-projector.py.
Gemma is provided under and subject to the Gemma Terms of Use found at
ai.google.dev/gemma/terms.
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