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

Libraries

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", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use bartowski/stable-code-instruct-3b-GGUF 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 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 serve -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

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

Atomic Chat

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

bartowski

Llamacpp quants

d45fd81 verified over 2 years ago

preview code

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	---
	license: other
	language:
	- en
	tags:
	- causal-lm
	- code
	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: 23.0
	verified: false
	quantized_by: bartowski
	pipeline_tag: text-generation
	---

	## Llamacpp Quantizations of stable-code-instruct-3b

	Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b2440">b2440</a> 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](https://huggingface.co/bartowski/stable-code-instruct-3b-GGUF/blob/main/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](https://huggingface.co/bartowski/stable-code-instruct-3b-GGUF/blob/main/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](https://huggingface.co/bartowski/stable-code-instruct-3b-GGUF/blob/main/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](https://huggingface.co/bartowski/stable-code-instruct-3b-GGUF/blob/main/stable-code-instruct-3b-Q5_K_S.gguf) \| Q5_K_S \| 1.94GB \| High quality, very usable. \|
	\| [stable-code-instruct-3b-Q5_0.gguf](https://huggingface.co/bartowski/stable-code-instruct-3b-GGUF/blob/main/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](https://huggingface.co/bartowski/stable-code-instruct-3b-GGUF/blob/main/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](https://huggingface.co/bartowski/stable-code-instruct-3b-GGUF/blob/main/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](https://huggingface.co/bartowski/stable-code-instruct-3b-GGUF/blob/main/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](https://huggingface.co/bartowski/stable-code-instruct-3b-GGUF/blob/main/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](https://huggingface.co/bartowski/stable-code-instruct-3b-GGUF/blob/main/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](https://huggingface.co/bartowski/stable-code-instruct-3b-GGUF/blob/main/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](https://huggingface.co/bartowski/stable-code-instruct-3b-GGUF/blob/main/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](https://huggingface.co/bartowski/stable-code-instruct-3b-GGUF/blob/main/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](https://huggingface.co/bartowski/stable-code-instruct-3b-GGUF/blob/main/stable-code-instruct-3b-Q3_K_M.gguf) \| Q3_K_M \| 1.39GB \| Even lower quality. \|
	\| [stable-code-instruct-3b-Q3_K_S.gguf](https://huggingface.co/bartowski/stable-code-instruct-3b-GGUF/blob/main/stable-code-instruct-3b-Q3_K_S.gguf) \| Q3_K_S \| 1.25GB \| Low quality, not recommended. \|
	\| [stable-code-instruct-3b-Q2_K.gguf](https://huggingface.co/bartowski/stable-code-instruct-3b-GGUF/blob/main/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