Instructions to use QuantFactory/stable-code-instruct-3b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/stable-code-instruct-3b-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="QuantFactory/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("QuantFactory/stable-code-instruct-3b-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/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="QuantFactory/stable-code-instruct-3b-GGUF",
	filename="stable-code-instruct-3b.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/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 QuantFactory/stable-code-instruct-3b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/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 QuantFactory/stable-code-instruct-3b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/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 QuantFactory/stable-code-instruct-3b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/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 QuantFactory/stable-code-instruct-3b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/stable-code-instruct-3b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/stable-code-instruct-3b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/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 "QuantFactory/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": "QuantFactory/stable-code-instruct-3b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/stable-code-instruct-3b-GGUF:Q4_K_M

SGLang

How to use QuantFactory/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 "QuantFactory/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": "QuantFactory/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 "QuantFactory/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": "QuantFactory/stable-code-instruct-3b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantFactory/stable-code-instruct-3b-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/stable-code-instruct-3b-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/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 QuantFactory/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 QuantFactory/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 QuantFactory/stable-code-instruct-3b-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/stable-code-instruct-3b-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/stable-code-instruct-3b-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/stable-code-instruct-3b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/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

stable-code-instruct-3b-GGUF / README.md

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	---
	license: other
	language:
	- en
	tags:
	- causal-lm
	- code
	base_model: stabilityai/stable-code-instruct-3b
	metrics:
	- code_eval
	library_name: transformers
	model-index:
	- name: stabilityai/stable-code-instruct-3b
	results:
	- task:
	type: text-generation
	dataset:
	type: nuprl/MultiPL-E
	name: MultiPL-HumanEval (Python)
	metrics:
	- name: pass@1
	type: pass@1
	value: 32.4
	verified: false
	- task:
	type: text-generation
	dataset:
	type: nuprl/MultiPL-E
	name: MultiPL-HumanEval (C++)
	metrics:
	- name: pass@1
	type: pass@1
	value: 30.9
	verified: false
	- task:
	type: text-generation
	dataset:
	type: nuprl/MultiPL-E
	name: MultiPL-HumanEval (Java)
	metrics:
	- name: pass@1
	type: pass@1
	value: 32.1
	verified: false
	- task:
	type: text-generation
	dataset:
	type: nuprl/MultiPL-E
	name: MultiPL-HumanEval (JavaScript)
	metrics:
	- name: pass@1
	type: pass@1
	value: 32.1
	verified: false
	- task:
	type: text-generation
	dataset:
	type: nuprl/MultiPL-E
	name: MultiPL-HumanEval (PHP)
	metrics:
	- name: pass@1
	type: pass@1
	value: 24.2
	verified: false
	- task:
	type: text-generation
	dataset:
	type: nuprl/MultiPL-E
	name: MultiPL-HumanEval (Rust)
	metrics:
	- name: pass@1
	type: pass@1
	value: 23
	verified: false
	pipeline_tag: text-generation
	---

	# QuantFactory/stable-code-instruct-3b-GGUF
	This is quantized version of [stabilityai/stable-code-instruct-3b](https://huggingface.co/stabilityai/stable-code-instruct-3b) created using llama.cpp

	# Model Description

	[Try it out here: https://huggingface.co/spaces/stabilityai/stable-code-instruct-3b](https://huggingface.co/spaces/stabilityai/stable-code-instruct-3b)

	![image/png](https://cdn-uploads.huggingface.co/production/uploads/63466107f7bd6326925fc770/O7ZkLgqoJprQEWAttX7Hj.png)



	`stable-code-instruct-3b` is a 2.7B billion parameter decoder-only language model tuned from [`stable-code-3b`](https://huggingface.co/stabilityai/stable-code-3b/). This model was trained on a mix of publicly available datasets, synthetic datasets using [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290).

	This instruct tune demonstrates state-of-the-art performance (compared to models of similar size) on the MultiPL-E metrics across multiple programming languages tested using [BigCode's Evaluation Harness](https://github.com/bigcode-project/bigcode-evaluation-harness/tree/main), and on the code portions of
	[MT Bench](https://klu.ai/glossary/mt-bench-eval).
	The model is finetuned to make it useable in tasks like,
	- General purpose Code/Software Engineering like conversations.
	- SQL related generation and conversation.

	Please note: For commercial use, please refer to https://stability.ai/license.

	## Usage
	Here's how you can run the model use the model:

	```python

	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]
	```

	## Model Details

	* Developed by: [Stability AI](https://stability.ai/)
	* Model type: `Stable Code Instruct 3B` model is an auto-regressive language model based on the transformer decoder architecture.
	* Language(s): English
	* Paper: [Stable Code Technical Report](https://drive.google.com/file/d/16-DGsR5-qwoPztZ6HcM7KSRUxIXrjlSm/view)
	* Library: [Alignment Handbook](https://github.com/huggingface/alignment-handbook.git)
	* Finetuned from model: [https://huggingface.co/stabilityai/stable-code-3b](https://huggingface.co/stabilityai/stable-code-3b)
	* License: [StabilityAI Community License](https://huggingface.co/stabilityai/stable-code-instruct-3b/blob/main/LICENSE.md).
	* Commercial License: to use this model commercially, please refer to https://stability.ai/license
	* Contact: For questions and comments about the model, please email `lm@stability.ai`


	## Performance
	### Multi-PL Benchmark:
	\| Model \| Size \| Avg \| Python \| C++ \| JavaScript \| Java \| PHP \| Rust \|
	\|------------------------------\|------\|------\|--------\|------\|------------\|------\|------\|------\|
	\| Codellama Instruct \| 7B \| 0.30 \| 0.33 \| 0.31 \| 0.31 \| 0.29 \| 0.31 \| 0.25 \|
	\| Deepseek Instruct \| 1.3B \| 0.44 \| 0.52 \| 0.52 \| 0.41 \| 0.46 \| 0.45 \| 0.28 \|
	\| Stable Code Instruct (SFT) \| 3B \| 0.44 \| 0.55 \| 0.45 \| 0.42 \| 0.42 \| 0.44 \| 0.32 \|
	\| Stable Code Instruct (DPO) \| 3B \| 0.47 \| 0.59 \| 0.49 \| 0.49 \| 0.44 \| 0.45 \| 0.37 \|

	### MT-Bench Coding:
	\| Model \| Size \| Score \|
	\|-----------------------------\|------\|-----------------\|
	\| DeepSeek Coder \| 1.3B \| 4.6 \|
	\| Stable Code Instruct (DPO) \| 3B \| 5.8(ours) \|
	\| Stable Code Instruct (SFT) \| 3B \| 5.5 \|
	\| DeepSeek Coder \| 6.7B \| 6.9 \|
	\| CodeLlama Instruct \| 7B \| 3.55 \|
	\| StarChat2 \| 15B \| 5.7 \|

	### SQL Performance
	\| Model \| Size \| Date \| Group By \| Order By \| Ratio \| Join \| Where \|
	\|-----------------------------\|------\|-------\|----------\|----------\|-------\|-------\|-------\|
	\| Stable Code Instruct (DPO) \| 3B \| 24.0% \| 54.2% \| 68.5% \| 40.0% \| 54.2% \| 42.8% \|
	\| DeepSeek-Coder Instruct \| 1.3B \| 24.0% \| 37.1% \| 51.4% \| 34.3% \| 45.7% \| 45.7% \|
	\| SQLCoder \| 7B \| 64.0% \| 82.9% \| 74.3% \| 54.3% \| 74.3% \| 74.3% \|




	## How to Cite Original Model
	```bibtex
	@misc{stable-code-instruct-3b,
	url={[https://huggingface.co/stabilityai/stable-code-3b](https://huggingface.co/stabilityai/stable-code-instruct-3b)},
	title={Stable Code 3B},
	author={Phung, Duy, and Pinnaparaju, Nikhil and Adithyan, Reshinth and Zhuravinskyi, Maksym and Tow, Jonathan and Cooper, Nathan}
	}
	```