Instructions to use tensorblock/yeontaek_WizardCoder-Python-13B-LoRa-GGUF 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 tensorblock/yeontaek_WizardCoder-Python-13B-LoRa-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 tensorblock/yeontaek_WizardCoder-Python-13B-LoRa-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf tensorblock/yeontaek_WizardCoder-Python-13B-LoRa-GGUF:Q2_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf tensorblock/yeontaek_WizardCoder-Python-13B-LoRa-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf tensorblock/yeontaek_WizardCoder-Python-13B-LoRa-GGUF:Q2_K
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 tensorblock/yeontaek_WizardCoder-Python-13B-LoRa-GGUF:Q2_K # Run inference directly in the terminal: ./build/bin/llama cli -hf tensorblock/yeontaek_WizardCoder-Python-13B-LoRa-GGUF:Q2_K
Use Docker
docker model run hf.co/tensorblock/yeontaek_WizardCoder-Python-13B-LoRa-GGUF:Q2_K
- Llama new
- LM Studio
- Jan
- Ollama
How to use tensorblock/yeontaek_WizardCoder-Python-13B-LoRa-GGUF with Ollama:
ollama run hf.co/tensorblock/yeontaek_WizardCoder-Python-13B-LoRa-GGUF:Q2_K
- Unsloth Desktop
- Docker Model Runner
How to use tensorblock/yeontaek_WizardCoder-Python-13B-LoRa-GGUF with Docker Model Runner:
docker model run hf.co/tensorblock/yeontaek_WizardCoder-Python-13B-LoRa-GGUF:Q2_K
- Lemonade
How to use tensorblock/yeontaek_WizardCoder-Python-13B-LoRa-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tensorblock/yeontaek_WizardCoder-Python-13B-LoRa-GGUF:Q2_K
Run and chat with the model
lemonade run user.yeontaek_WizardCoder-Python-13B-LoRa-GGUF-Q2_K
List all available models
lemonade list
- Atomic Chat
Download WizardCoder-Python-13B-LoRa-Q2_K.gguf from tensorblock/yeontaek_WizardCoder-Python-13B-LoRa-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 4.85 GB
-
https://huggingface.co/tensorblock/yeontaek_WizardCoder-Python-13B-LoRa-GGUF/resolve/0c166ecaaa89830cccd0b5a6ecc3d06bc81d06a3/WizardCoder-Python-13B-LoRa-Q2_K.gguf
- Command line
-
hf download hf://tensorblock/yeontaek_WizardCoder-Python-13B-LoRa-GGUF@0c166ecaaa89830cccd0b5a6ecc3d06bc81d06a3/WizardCoder-Python-13B-LoRa-Q2_K.gguf
-
curl -L -o WizardCoder-Python-13B-LoRa-Q2_K.gguf https://huggingface.co/tensorblock/yeontaek_WizardCoder-Python-13B-LoRa-GGUF/resolve/0c166ecaaa89830cccd0b5a6ecc3d06bc81d06a3/WizardCoder-Python-13B-LoRa-Q2_K.gguf
4.85 GB
- Xet hash:
- a0d8b0366ef2817b46a7b39013cbb462a1a0a65e532db0f44d7700519636d89f
- Size of remote file:
- 4.85 GB
- SHA256:
- 32e0e00b8f4d5a99a800a2e681a492f4e35ae43a967f4572906b0d2e9dbaaf73
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.