Instructions to use salimlko/XML_Qwen2_5_Coder_3B_bnb_4bit_Model_V3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use salimlko/XML_Qwen2_5_Coder_3B_bnb_4bit_Model_V3 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("salimlko/XML_Qwen2_5_Coder_3B_bnb_4bit_Model_V3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use salimlko/XML_Qwen2_5_Coder_3B_bnb_4bit_Model_V3 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 salimlko/XML_Qwen2_5_Coder_3B_bnb_4bit_Model_V3:Q8_0 # Run inference directly in the terminal: llama cli -hf salimlko/XML_Qwen2_5_Coder_3B_bnb_4bit_Model_V3:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf salimlko/XML_Qwen2_5_Coder_3B_bnb_4bit_Model_V3:Q8_0 # Run inference directly in the terminal: llama cli -hf salimlko/XML_Qwen2_5_Coder_3B_bnb_4bit_Model_V3:Q8_0
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 salimlko/XML_Qwen2_5_Coder_3B_bnb_4bit_Model_V3:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf salimlko/XML_Qwen2_5_Coder_3B_bnb_4bit_Model_V3:Q8_0
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 salimlko/XML_Qwen2_5_Coder_3B_bnb_4bit_Model_V3:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf salimlko/XML_Qwen2_5_Coder_3B_bnb_4bit_Model_V3:Q8_0
Use Docker
docker model run hf.co/salimlko/XML_Qwen2_5_Coder_3B_bnb_4bit_Model_V3:Q8_0
- LM Studio
- Jan
- Ollama
How to use salimlko/XML_Qwen2_5_Coder_3B_bnb_4bit_Model_V3 with Ollama:
ollama run hf.co/salimlko/XML_Qwen2_5_Coder_3B_bnb_4bit_Model_V3:Q8_0
- Unsloth Studio
How to use salimlko/XML_Qwen2_5_Coder_3B_bnb_4bit_Model_V3 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for salimlko/XML_Qwen2_5_Coder_3B_bnb_4bit_Model_V3 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for salimlko/XML_Qwen2_5_Coder_3B_bnb_4bit_Model_V3 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for salimlko/XML_Qwen2_5_Coder_3B_bnb_4bit_Model_V3 to start chatting
- Atomic Chat new
- Docker Model Runner
How to use salimlko/XML_Qwen2_5_Coder_3B_bnb_4bit_Model_V3 with Docker Model Runner:
docker model run hf.co/salimlko/XML_Qwen2_5_Coder_3B_bnb_4bit_Model_V3:Q8_0
- Lemonade
How to use salimlko/XML_Qwen2_5_Coder_3B_bnb_4bit_Model_V3 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull salimlko/XML_Qwen2_5_Coder_3B_bnb_4bit_Model_V3:Q8_0
Run and chat with the model
lemonade run user.XML_Qwen2_5_Coder_3B_bnb_4bit_Model_V3-Q8_0
List all available models
lemonade list
File size: 4,892 Bytes
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