Instructions to use QuantFactory/wavecoder-ds-6.7b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/wavecoder-ds-6.7b-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="QuantFactory/wavecoder-ds-6.7b-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/wavecoder-ds-6.7b-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/wavecoder-ds-6.7b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/wavecoder-ds-6.7b-GGUF",
	filename="wavecoder-ds-6.7b.Q2_K.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/wavecoder-ds-6.7b-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/wavecoder-ds-6.7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/wavecoder-ds-6.7b-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/wavecoder-ds-6.7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/wavecoder-ds-6.7b-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/wavecoder-ds-6.7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/wavecoder-ds-6.7b-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/wavecoder-ds-6.7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/wavecoder-ds-6.7b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/wavecoder-ds-6.7b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/wavecoder-ds-6.7b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/wavecoder-ds-6.7b-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/wavecoder-ds-6.7b-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/QuantFactory/wavecoder-ds-6.7b-GGUF:Q4_K_M

SGLang

How to use QuantFactory/wavecoder-ds-6.7b-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/wavecoder-ds-6.7b-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/wavecoder-ds-6.7b-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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/wavecoder-ds-6.7b-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/wavecoder-ds-6.7b-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use QuantFactory/wavecoder-ds-6.7b-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/wavecoder-ds-6.7b-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/wavecoder-ds-6.7b-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/wavecoder-ds-6.7b-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/wavecoder-ds-6.7b-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/wavecoder-ds-6.7b-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/wavecoder-ds-6.7b-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/wavecoder-ds-6.7b-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/wavecoder-ds-6.7b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/wavecoder-ds-6.7b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.wavecoder-ds-6.7b-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/wavecoder-ds-6.7b-GGUF

This is quantized version of microsoft/wavecoder-ds-6.7b created using llama.cpp

Original Model Card

🌊 WaveCoder: Widespread And Versatile Enhanced Code LLM

[📜 Paper] • [🐱 GitHub]
[🐦 Twitter] • [💬 Reddit] • [🍀 Unofficial Blog]

Repo for "WaveCoder: Widespread And Versatile Enhanced Instruction Tuning with Refined Data Generation"

🔥 News

[2024/04/10] 🔥🔥🔥 WaveCoder repo, models released at 🤗 HuggingFace!
[2023/12/26] WaveCoder paper released.

💡 Introduction

WaveCoder 🌊 is a series of large language models (LLMs) for the coding domain, designed to solve relevant problems in the field of code through instruction-following learning. Its training dataset was generated from a subset of code-search-net data using a generator-discriminator framework based on LLMs that we proposed, covering four general code-related tasks: code generation, code summary, code translation, and code repair.

Model	HumanEval	MBPP(500)	HumanEval Fix(Avg.)	HumanEval Explain(Avg.)
GPT-4	85.4	-	47.8	52.1
🌊 WaveCoder-DS-6.7B	65.8	63.0	49.5	40.8
🌊 WaveCoder-Pro-6.7B	74.4	63.4	52.1	43.0
🌊 WaveCoder-Ultra-6.7B	79.9	64.6	52.3	45.7

🪁 Evaluation

Please refer to WaveCoder's GitHub repo for inference, evaluation, and training code.

How to get start with the model

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("microsoft/wavecoder-ds-6.7b")
model = AutoModelForCausalLM.from_pretrained("microsoft/wavecoder-ds-6.7b")

📖 License

This code repository is licensed under the MIT License. The use of DeepSeek Coder models is subject to the its License.

☕️ Citation

If you find this repository helpful, please consider citing our paper:

@article{yu2023wavecoder,
  title={Wavecoder: Widespread and versatile enhanced instruction tuning with refined data generation},
  author={Yu, Zhaojian and Zhang, Xin and Shang, Ning and Huang, Yangyu and Xu, Can and Zhao, Yishujie and Hu, Wenxiang and Yin, Qiufeng},
  journal={arXiv preprint arXiv:2312.14187},
  year={2023}
}

Note

WaveCoder models are trained on the synthetic data generated by OpenAI models. Please pay attention to OpenAI's terms of use when using the models and the datasets.

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GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Paper for QuantFactory/wavecoder-ds-6.7b-GGUF

WaveCoder: Widespread And Versatile Enhanced Instruction Tuning with Refined Data Generation

Paper • 2312.14187 • Published Dec 20, 2023 • 49