Instructions to use mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF 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 mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF:Q4_K_M
Use Docker
docker model run hf.co/mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF with Ollama:
ollama run hf.co/mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF with Docker Model Runner:
docker model run hf.co/mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF:Q4_K_M
- Lemonade
How to use mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.OpenMath-CodeLlama-34b-Python-hf-i1-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
auto-patch README.md
Browse files
README.md
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| [GGUF](https://huggingface.co/mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF/resolve/main/OpenMath-CodeLlama-34b-Python-hf.i1-Q2_K.gguf) | i1-Q2_K | 12.6 | IQ3_XXS probably better |
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| [GGUF](https://huggingface.co/mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF/resolve/main/OpenMath-CodeLlama-34b-Python-hf.i1-IQ3_M.gguf) | i1-IQ3_M | 15.3 | |
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| [GGUF](https://huggingface.co/mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF/resolve/main/OpenMath-CodeLlama-34b-Python-hf.i1-Q4_K_S.gguf) | i1-Q4_K_S | 19.3 | optimal size/speed/quality |
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Here is a handy graph by ikawrakow comparing some lower-quality quant
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types (lower is better):
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| Link | Type | Size/GB | Notes |
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| [GGUF](https://huggingface.co/mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF/resolve/main/OpenMath-CodeLlama-34b-Python-hf.i1-IQ1_S.gguf) | i1-IQ1_S | 7.4 | for the desperate |
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| [GGUF](https://huggingface.co/mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF/resolve/main/OpenMath-CodeLlama-34b-Python-hf.i1-IQ1_M.gguf) | i1-IQ1_M | 8.0 | mostly desperate |
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| [GGUF](https://huggingface.co/mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF/resolve/main/OpenMath-CodeLlama-34b-Python-hf.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 9.1 | |
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| [GGUF](https://huggingface.co/mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF/resolve/main/OpenMath-CodeLlama-34b-Python-hf.i1-IQ2_XS.gguf) | i1-IQ2_XS | 10.1 | |
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| [GGUF](https://huggingface.co/mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF/resolve/main/OpenMath-CodeLlama-34b-Python-hf.i1-IQ2_S.gguf) | i1-IQ2_S | 10.7 | |
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| [GGUF](https://huggingface.co/mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF/resolve/main/OpenMath-CodeLlama-34b-Python-hf.i1-IQ2_M.gguf) | i1-IQ2_M | 11.6 | |
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| [GGUF](https://huggingface.co/mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF/resolve/main/OpenMath-CodeLlama-34b-Python-hf.i1-Q2_K_S.gguf) | i1-Q2_K_S | 11.7 | very low quality |
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| [GGUF](https://huggingface.co/mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF/resolve/main/OpenMath-CodeLlama-34b-Python-hf.i1-Q2_K.gguf) | i1-Q2_K | 12.6 | IQ3_XXS probably better |
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| [GGUF](https://huggingface.co/mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF/resolve/main/OpenMath-CodeLlama-34b-Python-hf.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 13.1 | lower quality |
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| [GGUF](https://huggingface.co/mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF/resolve/main/OpenMath-CodeLlama-34b-Python-hf.i1-IQ3_XS.gguf) | i1-IQ3_XS | 14.0 | |
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| [GGUF](https://huggingface.co/mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF/resolve/main/OpenMath-CodeLlama-34b-Python-hf.i1-Q3_K_S.gguf) | i1-Q3_K_S | 14.7 | IQ3_XS probably better |
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| [GGUF](https://huggingface.co/mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF/resolve/main/OpenMath-CodeLlama-34b-Python-hf.i1-IQ3_S.gguf) | i1-IQ3_S | 14.8 | beats Q3_K* |
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| [GGUF](https://huggingface.co/mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF/resolve/main/OpenMath-CodeLlama-34b-Python-hf.i1-IQ3_M.gguf) | i1-IQ3_M | 15.3 | |
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| [GGUF](https://huggingface.co/mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF/resolve/main/OpenMath-CodeLlama-34b-Python-hf.i1-Q3_K_M.gguf) | i1-Q3_K_M | 16.4 | IQ3_S probably better |
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| [GGUF](https://huggingface.co/mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF/resolve/main/OpenMath-CodeLlama-34b-Python-hf.i1-Q3_K_L.gguf) | i1-Q3_K_L | 17.9 | IQ3_M probably better |
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| [GGUF](https://huggingface.co/mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF/resolve/main/OpenMath-CodeLlama-34b-Python-hf.i1-IQ4_XS.gguf) | i1-IQ4_XS | 18.2 | |
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| [GGUF](https://huggingface.co/mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF/resolve/main/OpenMath-CodeLlama-34b-Python-hf.i1-Q4_0.gguf) | i1-Q4_0 | 19.2 | fast, low quality |
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| [GGUF](https://huggingface.co/mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF/resolve/main/OpenMath-CodeLlama-34b-Python-hf.i1-Q4_K_S.gguf) | i1-Q4_K_S | 19.3 | optimal size/speed/quality |
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| [GGUF](https://huggingface.co/mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF/resolve/main/OpenMath-CodeLlama-34b-Python-hf.i1-Q4_K_M.gguf) | i1-Q4_K_M | 20.3 | fast, recommended |
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| [GGUF](https://huggingface.co/mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF/resolve/main/OpenMath-CodeLlama-34b-Python-hf.i1-Q4_1.gguf) | i1-Q4_1 | 21.2 | |
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| [GGUF](https://huggingface.co/mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF/resolve/main/OpenMath-CodeLlama-34b-Python-hf.i1-Q5_K_S.gguf) | i1-Q5_K_S | 23.3 | |
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| [GGUF](https://huggingface.co/mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF/resolve/main/OpenMath-CodeLlama-34b-Python-hf.i1-Q5_K_M.gguf) | i1-Q5_K_M | 23.9 | |
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| [GGUF](https://huggingface.co/mradermacher/OpenMath-CodeLlama-34b-Python-hf-i1-GGUF/resolve/main/OpenMath-CodeLlama-34b-Python-hf.i1-Q6_K.gguf) | i1-Q6_K | 27.8 | practically like static Q6_K |
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Here is a handy graph by ikawrakow comparing some lower-quality quant
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types (lower is better):
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