Text Generation
Transformers
Safetensors
GGUF
English
stablelm
causal-lm
code
conversational
Eval Results (legacy)
Instructions to use stabilityai/stable-code-instruct-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stabilityai/stable-code-instruct-3b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="stabilityai/stable-code-instruct-3b") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("stabilityai/stable-code-instruct-3b") model = AutoModelForCausalLM.from_pretrained("stabilityai/stable-code-instruct-3b", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use stabilityai/stable-code-instruct-3b 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 stabilityai/stable-code-instruct-3b:Q4_K_M # Run inference directly in the terminal: llama cli -hf stabilityai/stable-code-instruct-3b:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf stabilityai/stable-code-instruct-3b:Q4_K_M # Run inference directly in the terminal: llama cli -hf stabilityai/stable-code-instruct-3b: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 stabilityai/stable-code-instruct-3b:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf stabilityai/stable-code-instruct-3b: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 stabilityai/stable-code-instruct-3b:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf stabilityai/stable-code-instruct-3b:Q4_K_M
Use Docker
docker model run hf.co/stabilityai/stable-code-instruct-3b:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use stabilityai/stable-code-instruct-3b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "stabilityai/stable-code-instruct-3b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "stabilityai/stable-code-instruct-3b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/stabilityai/stable-code-instruct-3b:Q4_K_M
- SGLang
How to use stabilityai/stable-code-instruct-3b 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 "stabilityai/stable-code-instruct-3b" \ --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": "stabilityai/stable-code-instruct-3b", "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 "stabilityai/stable-code-instruct-3b" \ --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": "stabilityai/stable-code-instruct-3b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use stabilityai/stable-code-instruct-3b with Ollama:
ollama run hf.co/stabilityai/stable-code-instruct-3b:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use stabilityai/stable-code-instruct-3b with Docker Model Runner:
docker model run hf.co/stabilityai/stable-code-instruct-3b:Q4_K_M
- Lemonade
How to use stabilityai/stable-code-instruct-3b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull stabilityai/stable-code-instruct-3b:Q4_K_M
Run and chat with the model
lemonade run user.stable-code-instruct-3b-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Commit History
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