Instructions to use lainlives/WizardCoder-Python-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lainlives/WizardCoder-Python-7B with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lainlives/WizardCoder-Python-7B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use lainlives/WizardCoder-Python-7B 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 lainlives/WizardCoder-Python-7B:Q4_K_M # Run inference directly in the terminal: llama cli -hf lainlives/WizardCoder-Python-7B:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf lainlives/WizardCoder-Python-7B:Q4_K_M # Run inference directly in the terminal: llama cli -hf lainlives/WizardCoder-Python-7B: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 lainlives/WizardCoder-Python-7B:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf lainlives/WizardCoder-Python-7B: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 lainlives/WizardCoder-Python-7B:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf lainlives/WizardCoder-Python-7B:Q4_K_M
Use Docker
docker model run hf.co/lainlives/WizardCoder-Python-7B:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use lainlives/WizardCoder-Python-7B with Ollama:
ollama run hf.co/lainlives/WizardCoder-Python-7B:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use lainlives/WizardCoder-Python-7B with Docker Model Runner:
docker model run hf.co/lainlives/WizardCoder-Python-7B:Q4_K_M
- Lemonade
How to use lainlives/WizardCoder-Python-7B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull lainlives/WizardCoder-Python-7B:Q4_K_M
Run and chat with the model
lemonade run user.WizardCoder-Python-7B-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download WizardCoder-Python-7B-V1.0-Q8_0.gguf from lainlives/WizardCoder-Python-7B: direct link, hf CLI and curl.
- Browser
- Download file 7.16 GB
-
https://huggingface.co/lainlives/WizardCoder-Python-7B/resolve/c4c58182dee918c642d7184aa885904f3a996354/WizardCoder-Python-7B-V1.0-Q8_0.gguf
- Command line
-
hf download hf://lainlives/WizardCoder-Python-7B@c4c58182dee918c642d7184aa885904f3a996354/WizardCoder-Python-7B-V1.0-Q8_0.gguf
-
curl -L -o WizardCoder-Python-7B-V1.0-Q8_0.gguf https://huggingface.co/lainlives/WizardCoder-Python-7B/resolve/c4c58182dee918c642d7184aa885904f3a996354/WizardCoder-Python-7B-V1.0-Q8_0.gguf
7.16 GB
- Xet hash:
- aa5b433d74b461841031aa6e14e4d0d16b750d0548c7cbd9fc03d8490f73afd0
- Size of remote file:
- 7.16 GB
- SHA256:
- 079aff71ad7974f3eb46d0b0be4955c6b6ffb8156cb4c01052bfea592c65b4e7
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