Instructions to use TheBloke/WizardCoder-Python-7B-V1.0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TheBloke/WizardCoder-Python-7B-V1.0-GGUF with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("TheBloke/WizardCoder-Python-7B-V1.0-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use TheBloke/WizardCoder-Python-7B-V1.0-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 TheBloke/WizardCoder-Python-7B-V1.0-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf TheBloke/WizardCoder-Python-7B-V1.0-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 TheBloke/WizardCoder-Python-7B-V1.0-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf TheBloke/WizardCoder-Python-7B-V1.0-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 TheBloke/WizardCoder-Python-7B-V1.0-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf TheBloke/WizardCoder-Python-7B-V1.0-GGUF:Q4_K_M
Use Docker
docker model run hf.co/TheBloke/WizardCoder-Python-7B-V1.0-GGUF:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use TheBloke/WizardCoder-Python-7B-V1.0-GGUF with Ollama:
ollama run hf.co/TheBloke/WizardCoder-Python-7B-V1.0-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use TheBloke/WizardCoder-Python-7B-V1.0-GGUF with Docker Model Runner:
docker model run hf.co/TheBloke/WizardCoder-Python-7B-V1.0-GGUF:Q4_K_M
- Lemonade
How to use TheBloke/WizardCoder-Python-7B-V1.0-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull TheBloke/WizardCoder-Python-7B-V1.0-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.WizardCoder-Python-7B-V1.0-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Upload README.md
Browse files
README.md
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<!-- repositories-available start -->
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## Repositories available
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* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/WizardCoder-Python-7B-V1.0-GPTQ)
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* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/WizardCoder-Python-7B-V1.0-GGUF)
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* [WizardLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/WizardLM/WizardCoder-Python-7b-V1.0)
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<!-- repositories-available start -->
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## Repositories available
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* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/WizardCoder-Python-7B-V1.0-AWQ)
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* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/WizardCoder-Python-7B-V1.0-GPTQ)
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* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/WizardCoder-Python-7B-V1.0-GGUF)
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* [WizardLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/WizardLM/WizardCoder-Python-7b-V1.0)
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