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

Libraries

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

Use Docker

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

LM Studio
Jan

vLLM

How to use leafspark/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 "leafspark/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": "leafspark/wavecoder-ds-6.7b-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

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

Unsloth Studio new

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

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

Lemonade

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

Pull the model

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

Model Card for wavecoder-ds-6.7b-GGUF

WaveCoder 🌊 is a series of large language models (LLMs) for the coding domain.

Model Details

WaveCoder-6.7b-ds = Trained using CodeOcean dataset
WaveCoder-6.7b-pro = Trained using GPT-4 synthetic data
WaveCoder-6.7b-ultra = Trained using enhanced GPT-4 synthetic data

Model Description

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.

Developed by: Yu, Zhaojian and Zhang, Xin and Shang, Ning and Huang, Yangyu and Xu, Can and Zhao, Yishujie and Hu, Wenxiang and Yin, Qiufeng
Model type: Large Language Model
Language(s) (NLP): English
License: DeepSeek License (Model)

Model Sources

Repository: https://huggingface.co/microsoft/wavecoder-ds-6.7b
Paper : [More Information Needed]
Demo : [More Information Needed]

Uses

Coding/Refactoring/Cleanup/Fixing Code

Original: https://huggingface.co/microsoft/wavecoder-ds-6.7b

Downloads last month: 30

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

4-bit

6-bit

8-bit

32-bit