Instructions to use duyntnet/stable-code-instruct-3b-imatrix-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use duyntnet/stable-code-instruct-3b-imatrix-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="duyntnet/stable-code-instruct-3b-imatrix-GGUF") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("duyntnet/stable-code-instruct-3b-imatrix-GGUF", dtype="auto") - llama-cpp-python
How to use duyntnet/stable-code-instruct-3b-imatrix-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="duyntnet/stable-code-instruct-3b-imatrix-GGUF", filename="stable-code-instruct-3b-IQ1_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 duyntnet/stable-code-instruct-3b-imatrix-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf duyntnet/stable-code-instruct-3b-imatrix-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf duyntnet/stable-code-instruct-3b-imatrix-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 duyntnet/stable-code-instruct-3b-imatrix-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf duyntnet/stable-code-instruct-3b-imatrix-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 duyntnet/stable-code-instruct-3b-imatrix-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf duyntnet/stable-code-instruct-3b-imatrix-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 duyntnet/stable-code-instruct-3b-imatrix-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf duyntnet/stable-code-instruct-3b-imatrix-GGUF:Q4_K_M
Use Docker
docker model run hf.co/duyntnet/stable-code-instruct-3b-imatrix-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use duyntnet/stable-code-instruct-3b-imatrix-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "duyntnet/stable-code-instruct-3b-imatrix-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": "duyntnet/stable-code-instruct-3b-imatrix-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/duyntnet/stable-code-instruct-3b-imatrix-GGUF:Q4_K_M
- SGLang
How to use duyntnet/stable-code-instruct-3b-imatrix-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 "duyntnet/stable-code-instruct-3b-imatrix-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": "duyntnet/stable-code-instruct-3b-imatrix-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 "duyntnet/stable-code-instruct-3b-imatrix-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": "duyntnet/stable-code-instruct-3b-imatrix-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use duyntnet/stable-code-instruct-3b-imatrix-GGUF with Ollama:
ollama run hf.co/duyntnet/stable-code-instruct-3b-imatrix-GGUF:Q4_K_M
- Unsloth Studio new
How to use duyntnet/stable-code-instruct-3b-imatrix-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 duyntnet/stable-code-instruct-3b-imatrix-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 duyntnet/stable-code-instruct-3b-imatrix-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for duyntnet/stable-code-instruct-3b-imatrix-GGUF to start chatting
- Docker Model Runner
How to use duyntnet/stable-code-instruct-3b-imatrix-GGUF with Docker Model Runner:
docker model run hf.co/duyntnet/stable-code-instruct-3b-imatrix-GGUF:Q4_K_M
- Lemonade
How to use duyntnet/stable-code-instruct-3b-imatrix-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull duyntnet/stable-code-instruct-3b-imatrix-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.stable-code-instruct-3b-imatrix-GGUF-Q4_K_M
List all available models
lemonade list
Quantizations of https://huggingface.co/stabilityai/stable-code-instruct-3b
From original readme
Usage
Here's how you can run the model use the model:
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stable-code-instruct-3b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("stabilityai/stable-code-instruct-3b", torch_dtype=torch.bfloat16, trust_remote_code=True)
model.eval()
model = model.cuda()
messages = [
{
"role": "system",
"content": "You are a helpful and polite assistant",
},
{
"role": "user",
"content": "Write a simple website in HTML. When a user clicks the button, it shows a random joke from a list of 4 jokes."
},
]
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
inputs = tokenizer([prompt], return_tensors="pt").to(model.device)
tokens = model.generate(
**inputs,
max_new_tokens=1024,
temperature=0.5,
top_p=0.95,
top_k=100,
do_sample=True,
use_cache=True
)
output = tokenizer.batch_decode(tokens[:, inputs.input_ids.shape[-1]:], skip_special_tokens=False)[0]
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