Instructions to use bartowski/stable-code-instruct-3b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bartowski/stable-code-instruct-3b-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bartowski/stable-code-instruct-3b-GGUF") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bartowski/stable-code-instruct-3b-GGUF", dtype="auto") - llama-cpp-python
How to use bartowski/stable-code-instruct-3b-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="bartowski/stable-code-instruct-3b-GGUF", filename="stable-code-instruct-3b-IQ3_M.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use bartowski/stable-code-instruct-3b-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf bartowski/stable-code-instruct-3b-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf bartowski/stable-code-instruct-3b-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf bartowski/stable-code-instruct-3b-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf bartowski/stable-code-instruct-3b-GGUF: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 bartowski/stable-code-instruct-3b-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf bartowski/stable-code-instruct-3b-GGUF: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 bartowski/stable-code-instruct-3b-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf bartowski/stable-code-instruct-3b-GGUF:Q4_K_M
Use Docker
docker model run hf.co/bartowski/stable-code-instruct-3b-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use bartowski/stable-code-instruct-3b-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bartowski/stable-code-instruct-3b-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bartowski/stable-code-instruct-3b-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/bartowski/stable-code-instruct-3b-GGUF:Q4_K_M
- SGLang
How to use bartowski/stable-code-instruct-3b-GGUF with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "bartowski/stable-code-instruct-3b-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bartowski/stable-code-instruct-3b-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "bartowski/stable-code-instruct-3b-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bartowski/stable-code-instruct-3b-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use bartowski/stable-code-instruct-3b-GGUF with Ollama:
ollama run hf.co/bartowski/stable-code-instruct-3b-GGUF:Q4_K_M
- Unsloth Studio new
How to use bartowski/stable-code-instruct-3b-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 bartowski/stable-code-instruct-3b-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 bartowski/stable-code-instruct-3b-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for bartowski/stable-code-instruct-3b-GGUF to start chatting
- Docker Model Runner
How to use bartowski/stable-code-instruct-3b-GGUF with Docker Model Runner:
docker model run hf.co/bartowski/stable-code-instruct-3b-GGUF:Q4_K_M
- Lemonade
How to use bartowski/stable-code-instruct-3b-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull bartowski/stable-code-instruct-3b-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.stable-code-instruct-3b-GGUF-Q4_K_M
List all available models
lemonade list
llm.create_chat_completion(
messages = [
{
"role": "user",
"content": "What is the capital of France?"
}
]
)Llamacpp Quantizations of stable-code-instruct-3b
Using llama.cpp release b2440 for quantization.
Original model: https://huggingface.co/stabilityai/stable-code-instruct-3b
Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| stable-code-instruct-3b-Q8_0.gguf | Q8_0 | 2.97GB | Extremely high quality, generally unneeded but max available quant. |
| stable-code-instruct-3b-Q6_K.gguf | Q6_K | 2.29GB | Very high quality, near perfect, recommended. |
| stable-code-instruct-3b-Q5_K_M.gguf | Q5_K_M | 1.99GB | High quality, very usable. |
| stable-code-instruct-3b-Q5_K_S.gguf | Q5_K_S | 1.94GB | High quality, very usable. |
| stable-code-instruct-3b-Q5_0.gguf | Q5_0 | 1.94GB | High quality, older format, generally not recommended. |
| stable-code-instruct-3b-Q4_K_M.gguf | Q4_K_M | 1.70GB | Good quality, similar to 4.25 bpw. |
| stable-code-instruct-3b-Q4_K_S.gguf | Q4_K_S | 1.62GB | Slightly lower quality with small space savings. |
| stable-code-instruct-3b-IQ4_NL.gguf | IQ4_NL | 1.61GB | Good quality, similar to Q4_K_S, new method of quanting, |
| stable-code-instruct-3b-IQ4_XS.gguf | IQ4_XS | 1.53GB | Decent quality, new method with similar performance to Q4. |
| stable-code-instruct-3b-Q4_0.gguf | Q4_0 | 1.60GB | Decent quality, older format, generally not recommended. |
| stable-code-instruct-3b-IQ3_M.gguf | IQ3_M | 1.31GB | Medium-low quality, new method with decent performance. |
| stable-code-instruct-3b-IQ3_S.gguf | IQ3_S | 1.25GB | Lower quality, new method with decent performance, recommended over Q3 quants. |
| stable-code-instruct-3b-Q3_K_L.gguf | Q3_K_L | 1.50GB | Lower quality but usable, good for low RAM availability. |
| stable-code-instruct-3b-Q3_K_M.gguf | Q3_K_M | 1.39GB | Even lower quality. |
| stable-code-instruct-3b-Q3_K_S.gguf | Q3_K_S | 1.25GB | Low quality, not recommended. |
| stable-code-instruct-3b-Q2_K.gguf | Q2_K | 1.08GB | Extremely low quality, not recommended. |
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
- Downloads last month
- 695
2-bit
3-bit
4-bit
5-bit
6-bit
8-bit
Evaluation results
- pass@1 on MultiPL-HumanEval (Python)self-reported32.400
- pass@1 on MultiPL-HumanEval (C++)self-reported30.900
- pass@1 on MultiPL-HumanEval (Java)self-reported32.100
- pass@1 on MultiPL-HumanEval (JavaScript)self-reported32.100
- pass@1 on MultiPL-HumanEval (PHP)self-reported24.200
- pass@1 on MultiPL-HumanEval (Rust)self-reported23.000
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="bartowski/stable-code-instruct-3b-GGUF", filename="", )