Instructions to use webai-community/ai-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use webai-community/ai-models with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="webai-community/ai-models", filename="CodeLlama-7b-Instruct-hf/gguf/codellama-7b-instruct.Q4_K_M.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 webai-community/ai-models with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf webai-community/ai-models:Q4_K_M # Run inference directly in the terminal: llama-cli -hf webai-community/ai-models:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf webai-community/ai-models:Q4_K_M # Run inference directly in the terminal: llama-cli -hf webai-community/ai-models: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 webai-community/ai-models:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf webai-community/ai-models: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 webai-community/ai-models:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf webai-community/ai-models:Q4_K_M
Use Docker
docker model run hf.co/webai-community/ai-models:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use webai-community/ai-models with Ollama:
ollama run hf.co/webai-community/ai-models:Q4_K_M
- Unsloth Studio new
How to use webai-community/ai-models 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 webai-community/ai-models 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 webai-community/ai-models to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for webai-community/ai-models to start chatting
- Docker Model Runner
How to use webai-community/ai-models with Docker Model Runner:
docker model run hf.co/webai-community/ai-models:Q4_K_M
- Lemonade
How to use webai-community/ai-models with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull webai-community/ai-models:Q4_K_M
Run and chat with the model
lemonade run user.ai-models-Q4_K_M
List all available models
lemonade list
Upload folder using huggingface_hub
Browse files
CodeLlama-7b-Instruct-hf/README.md
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# CodeLlama-7b-Instruct-hf
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## GenAI WebGPU Model
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Generated using `onnxruntime_genai.models.builder`
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- **onnxruntime-genai commit**: `41c4ce18fec1240c2f848725e31fbe8854010188`
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- **transformers version**: `4.57.0`
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- **Precision**: int4
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- **Execution Provider**: webgpu
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```
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python -m onnxruntime_genai.models.builder -p int4 -e webgpu -m codellama/CodeLlama-7b-Instruct-hf -o E:\ai-models\CodeLlama-7b-Instruct-hf\onnx-webgpu\ --extra_options int4_algo_config=rtn_last int4_is_symmetric=true prune_lm_head=true enable_webgpu_graph=true
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```
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## GGUF Model
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Downloaded from: [TheBloke/CodeLlama-7B-Instruct-GGUF](https://huggingface.co/TheBloke/CodeLlama-7B-Instruct-GGUF)
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