Instructions to use stduhpf/google-gemma-3-12b-it-qat-q4_0-gguf-small 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 stduhpf/google-gemma-3-12b-it-qat-q4_0-gguf-small 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 stduhpf/google-gemma-3-12b-it-qat-q4_0-gguf-small:Q4_0_S # Run inference directly in the terminal: llama cli -hf stduhpf/google-gemma-3-12b-it-qat-q4_0-gguf-small:Q4_0_S
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf stduhpf/google-gemma-3-12b-it-qat-q4_0-gguf-small:Q4_0_S # Run inference directly in the terminal: llama cli -hf stduhpf/google-gemma-3-12b-it-qat-q4_0-gguf-small:Q4_0_S
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 stduhpf/google-gemma-3-12b-it-qat-q4_0-gguf-small:Q4_0_S # Run inference directly in the terminal: ./llama-cli -hf stduhpf/google-gemma-3-12b-it-qat-q4_0-gguf-small:Q4_0_S
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 stduhpf/google-gemma-3-12b-it-qat-q4_0-gguf-small:Q4_0_S # Run inference directly in the terminal: ./build/bin/llama-cli -hf stduhpf/google-gemma-3-12b-it-qat-q4_0-gguf-small:Q4_0_S
Use Docker
docker model run hf.co/stduhpf/google-gemma-3-12b-it-qat-q4_0-gguf-small:Q4_0_S
- LM Studio
- Jan
- Ollama
How to use stduhpf/google-gemma-3-12b-it-qat-q4_0-gguf-small with Ollama:
ollama run hf.co/stduhpf/google-gemma-3-12b-it-qat-q4_0-gguf-small:Q4_0_S
- Unsloth Desktop
- Docker Model Runner
How to use stduhpf/google-gemma-3-12b-it-qat-q4_0-gguf-small with Docker Model Runner:
docker model run hf.co/stduhpf/google-gemma-3-12b-it-qat-q4_0-gguf-small:Q4_0_S
- Lemonade
How to use stduhpf/google-gemma-3-12b-it-qat-q4_0-gguf-small with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull stduhpf/google-gemma-3-12b-it-qat-q4_0-gguf-small:Q4_0_S
Run and chat with the model
lemonade run user.google-gemma-3-12b-it-qat-q4_0-gguf-small-Q4_0_S
List all available models
lemonade list
- Atomic Chat
Commit History
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Verified signature- Upload gemma-3-12b-it-q4_0_s.gguf (486c6a6ee127f73b175d152a7c6ee87a35816416) Co-authored-by: Dämpfchen <Dampfinchen@users.noreply.huggingface.co>508dfd0
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Verified signatureThis is a "self" merge of https://huggingface.co/google/gemma-3-12b-it-qat-q4_0-gguf and https://huggingface.co/bartowski/google_gemma-3-12b-it-GGUF. The official QAT weights released by google use fp16 (instead of Q6_K) for the embeddings table, which makes this model take a significant extra amount of memory (and storage) compared to what Q4_0 quants are supposed to take. Instead of quantizing the table myself, I extracted it from @bartowski's quantized models, because those were already calibrated with imatrix, which should squeeze some extra performance out of it. Here are some perplexity measurements: | Model | File size ↓ | PPL (wiki.text.raw) ↓ | | --- | --- | --- | | [iQ3_xs (bartowski)](https://huggingface.co/bartowski/google_gemma-3-12b-it-GGUF/blob/main/google_gemma-3-12b-it-IQ3_XS.gguf) | 5.21 GB |10.0755 +/- 0.08024 | | QAT Q4_0 + Q6_k embeddings | 6.89 GB | 9.2637 +/- 0.07216 | | [Q4_0 (bartowski)](https://huggingface.co/bartowski/google_gemma-3-12b-it-GGUF/blob/main…28ca194 -
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