Instructions to use mradermacher/MathCoder-L-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mradermacher/MathCoder-L-7B-GGUF with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mradermacher/MathCoder-L-7B-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mradermacher/MathCoder-L-7B-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/MathCoder-L-7B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/MathCoder-L-7B-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/MathCoder-L-7B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/MathCoder-L-7B-GGUF: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 mradermacher/MathCoder-L-7B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf mradermacher/MathCoder-L-7B-GGUF: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 mradermacher/MathCoder-L-7B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf mradermacher/MathCoder-L-7B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/mradermacher/MathCoder-L-7B-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use mradermacher/MathCoder-L-7B-GGUF with Ollama:
ollama run hf.co/mradermacher/MathCoder-L-7B-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use mradermacher/MathCoder-L-7B-GGUF with Docker Model Runner:
docker model run hf.co/mradermacher/MathCoder-L-7B-GGUF:Q4_K_M
- Lemonade
How to use mradermacher/MathCoder-L-7B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mradermacher/MathCoder-L-7B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.MathCoder-L-7B-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download MathCoder-L-7B.Q6_K.gguf from mradermacher/MathCoder-L-7B-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 5.53 GB
-
https://huggingface.co/mradermacher/MathCoder-L-7B-GGUF/resolve/main/MathCoder-L-7B.Q6_K.gguf
- Command line
-
hf download hf://mradermacher/MathCoder-L-7B-GGUF/MathCoder-L-7B.Q6_K.gguf
-
curl -L -o MathCoder-L-7B.Q6_K.gguf https://huggingface.co/mradermacher/MathCoder-L-7B-GGUF/resolve/main/MathCoder-L-7B.Q6_K.gguf
5.53 GB
- Xet hash:
- b3931aa5e410fbc1538e78d414a7943e1866cc0810126b73d342dacdacea009a
- Size of remote file:
- 5.53 GB
- SHA256:
- 8b7671b619f409f404d7c480b84e27da19ee2db628f04c163b8b589153d923de
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