Instructions to use QuantFactory/stable-code-instruct-3b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use QuantFactory/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 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 serve -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
- Atomic Chat new
- 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
File size: 6,950 Bytes
31c644c 49d3573 31c644c 49d3573 31c644c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 | ---
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)

`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}
}
``` |