Instructions to use lmstudio-community/wavecoder-ultra-6.7b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lmstudio-community/wavecoder-ultra-6.7b-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="lmstudio-community/wavecoder-ultra-6.7b-GGUF")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lmstudio-community/wavecoder-ultra-6.7b-GGUF", dtype="auto") - llama-cpp-python
How to use lmstudio-community/wavecoder-ultra-6.7b-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="lmstudio-community/wavecoder-ultra-6.7b-GGUF", filename="wavecoder-ultra-6.7b-IQ1_M.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use lmstudio-community/wavecoder-ultra-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 lmstudio-community/wavecoder-ultra-6.7b-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf lmstudio-community/wavecoder-ultra-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 lmstudio-community/wavecoder-ultra-6.7b-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf lmstudio-community/wavecoder-ultra-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 lmstudio-community/wavecoder-ultra-6.7b-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf lmstudio-community/wavecoder-ultra-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 lmstudio-community/wavecoder-ultra-6.7b-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf lmstudio-community/wavecoder-ultra-6.7b-GGUF:Q4_K_M
Use Docker
docker model run hf.co/lmstudio-community/wavecoder-ultra-6.7b-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use lmstudio-community/wavecoder-ultra-6.7b-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lmstudio-community/wavecoder-ultra-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": "lmstudio-community/wavecoder-ultra-6.7b-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/lmstudio-community/wavecoder-ultra-6.7b-GGUF:Q4_K_M
- SGLang
How to use lmstudio-community/wavecoder-ultra-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 "lmstudio-community/wavecoder-ultra-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": "lmstudio-community/wavecoder-ultra-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 "lmstudio-community/wavecoder-ultra-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": "lmstudio-community/wavecoder-ultra-6.7b-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use lmstudio-community/wavecoder-ultra-6.7b-GGUF with Ollama:
ollama run hf.co/lmstudio-community/wavecoder-ultra-6.7b-GGUF:Q4_K_M
- Unsloth Studio
How to use lmstudio-community/wavecoder-ultra-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 lmstudio-community/wavecoder-ultra-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 lmstudio-community/wavecoder-ultra-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 lmstudio-community/wavecoder-ultra-6.7b-GGUF to start chatting
- Docker Model Runner
How to use lmstudio-community/wavecoder-ultra-6.7b-GGUF with Docker Model Runner:
docker model run hf.co/lmstudio-community/wavecoder-ultra-6.7b-GGUF:Q4_K_M
- Lemonade
How to use lmstudio-community/wavecoder-ultra-6.7b-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull lmstudio-community/wavecoder-ultra-6.7b-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.wavecoder-ultra-6.7b-GGUF-Q4_K_M
List all available models
lemonade list
💫 Community Model> wavecoder-ultra-6.7b by Microsoft
👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord.
Model creator: Microsoft
Original model: wavecoder-ultra-6.7b
GGUF quantization: provided by bartowski based on llama.cpp release b2675
Model Summary:
WaveCoder ultra is a coding model created with 'Widepread And Versatile Enhanced' instruction tuning. It has exceptional generalization ability across different code-related tasks and has a high efficiency in generation.
This model should be used exclusively for coding, and will follow instructions for code generation.
Prompt Template:
Choose the Alpaca preset in your LM Studio.
Under the hood, the model will see a prompt that's formatted like so:
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction: {prompt}
### Response:
Use case and examples
WaveCoder ultra is fine tuned for code-related instruction following tasks, including code generation, summarization, repair, and translation.
Code Generation
Code Summarization
Code Repair
Code Translation
Technical Details
The WaveCoder series of models is the result of a 'Widespread And Versatile Enchanced' (WAVE) instruction tuning with a highly refined dataset.
Their 'CodeOcean' consists of 20,000 instruction instances across the 4 code-related tasks (generation, summarization, repair, translation) with instructions generated by GPT-3.5-turbo.
To create this dataset, the team used existing raw code from GitHub CodeSearchNet, filtering for quality and diversity, then used a 'novel LLM-based Generator-Discriminator Framework' which involves generating supervised instruction data from the unsupervised open source code.
For further details and benchmarks, check out their arXiv paper here
Special thanks
🙏 Special thanks to Georgi Gerganov and the whole team working on llama.cpp for making all of this possible.
🙏 Special thanks to Kalomaze for his dataset (linked here) that was used for calculating the imatrix for these quants, which improves the overall quality!
Disclaimers
LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.
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