Instructions to use dranger003/starcoder2-15b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use dranger003/starcoder2-15b-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="dranger003/starcoder2-15b-GGUF", filename="ggml-starcoder2-15b-f16.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use dranger003/starcoder2-15b-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf dranger003/starcoder2-15b-GGUF:F16 # Run inference directly in the terminal: llama-cli -hf dranger003/starcoder2-15b-GGUF:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf dranger003/starcoder2-15b-GGUF:F16 # Run inference directly in the terminal: llama-cli -hf dranger003/starcoder2-15b-GGUF:F16
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 dranger003/starcoder2-15b-GGUF:F16 # Run inference directly in the terminal: ./llama-cli -hf dranger003/starcoder2-15b-GGUF:F16
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 dranger003/starcoder2-15b-GGUF:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf dranger003/starcoder2-15b-GGUF:F16
Use Docker
docker model run hf.co/dranger003/starcoder2-15b-GGUF:F16
- LM Studio
- Jan
- vLLM
How to use dranger003/starcoder2-15b-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "dranger003/starcoder2-15b-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dranger003/starcoder2-15b-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/dranger003/starcoder2-15b-GGUF:F16
- Ollama
How to use dranger003/starcoder2-15b-GGUF with Ollama:
ollama run hf.co/dranger003/starcoder2-15b-GGUF:F16
- Unsloth Studio new
How to use dranger003/starcoder2-15b-GGUF 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 dranger003/starcoder2-15b-GGUF 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 dranger003/starcoder2-15b-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for dranger003/starcoder2-15b-GGUF to start chatting
- Docker Model Runner
How to use dranger003/starcoder2-15b-GGUF with Docker Model Runner:
docker model run hf.co/dranger003/starcoder2-15b-GGUF:F16
- Lemonade
How to use dranger003/starcoder2-15b-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull dranger003/starcoder2-15b-GGUF:F16
Run and chat with the model
lemonade run user.starcoder2-15b-GGUF-F16
List all available models
lemonade list
GGUF quants for https://huggingface.co/bigcode/starcoder2-15b
StarCoder2-15B model is a 15B parameter model trained on 600+ programming languages from The Stack v2, with opt-out requests excluded. The model uses Grouped Query Attention, a context window of 16,384 tokens with a sliding window attention of 4,096 tokens, and was trained using the Fill-in-the-Middle objective on 4+ trillion tokens.
The model was trained on GitHub code as well as additional selected data sources such as Arxiv and Wikipedia. As such it is not an instruction model and commands like "Write a function that computes the square root." do not work well.
| Layers | Context | Template (None/Base Model) |
|---|---|---|
40 |
16384 |
{prompt} |
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