Instructions to use hellork/BlenderLLM-IQ3_XXS-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use hellork/BlenderLLM-IQ3_XXS-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="hellork/BlenderLLM-IQ3_XXS-GGUF", filename="blenderllm-iq3_xxs-imat.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use hellork/BlenderLLM-IQ3_XXS-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf hellork/BlenderLLM-IQ3_XXS-GGUF:IQ3_XXS # Run inference directly in the terminal: llama-cli -hf hellork/BlenderLLM-IQ3_XXS-GGUF:IQ3_XXS
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf hellork/BlenderLLM-IQ3_XXS-GGUF:IQ3_XXS # Run inference directly in the terminal: llama-cli -hf hellork/BlenderLLM-IQ3_XXS-GGUF:IQ3_XXS
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 hellork/BlenderLLM-IQ3_XXS-GGUF:IQ3_XXS # Run inference directly in the terminal: ./llama-cli -hf hellork/BlenderLLM-IQ3_XXS-GGUF:IQ3_XXS
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 hellork/BlenderLLM-IQ3_XXS-GGUF:IQ3_XXS # Run inference directly in the terminal: ./build/bin/llama-cli -hf hellork/BlenderLLM-IQ3_XXS-GGUF:IQ3_XXS
Use Docker
docker model run hf.co/hellork/BlenderLLM-IQ3_XXS-GGUF:IQ3_XXS
- LM Studio
- Jan
- Ollama
How to use hellork/BlenderLLM-IQ3_XXS-GGUF with Ollama:
ollama run hf.co/hellork/BlenderLLM-IQ3_XXS-GGUF:IQ3_XXS
- Unsloth Studio new
How to use hellork/BlenderLLM-IQ3_XXS-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 hellork/BlenderLLM-IQ3_XXS-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 hellork/BlenderLLM-IQ3_XXS-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for hellork/BlenderLLM-IQ3_XXS-GGUF to start chatting
- Pi new
How to use hellork/BlenderLLM-IQ3_XXS-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf hellork/BlenderLLM-IQ3_XXS-GGUF:IQ3_XXS
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "hellork/BlenderLLM-IQ3_XXS-GGUF:IQ3_XXS" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use hellork/BlenderLLM-IQ3_XXS-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf hellork/BlenderLLM-IQ3_XXS-GGUF:IQ3_XXS
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default hellork/BlenderLLM-IQ3_XXS-GGUF:IQ3_XXS
Run Hermes
hermes
- Docker Model Runner
How to use hellork/BlenderLLM-IQ3_XXS-GGUF with Docker Model Runner:
docker model run hf.co/hellork/BlenderLLM-IQ3_XXS-GGUF:IQ3_XXS
- Lemonade
How to use hellork/BlenderLLM-IQ3_XXS-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull hellork/BlenderLLM-IQ3_XXS-GGUF:IQ3_XXS
Run and chat with the model
lemonade run user.BlenderLLM-IQ3_XXS-GGUF-IQ3_XXS
List all available models
lemonade list
TESTING...TESTING! The quantization used on this model may reduce quality, but it is hopefully faster, and maybe usable with 4GB VRAM. TESTING...
hellork/BlenderLLM-IQ3_XXS-GGUF
This model was converted to GGUF format from FreedomIntelligence/BlenderLLM using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
brew install llama.cpp
Compile to take advantage of Nvidia CUDA hardware:
git clone https://github.com/ggerganov/llama.cpp.git
cd llama*
# look at docs for other hardware builds or to make sure none of this has changed.
cmake -B build -DGGML_CUDA=ON
CMAKE_ARGS="-DGGML_CUDA=on" cmake --build build --config Release # -j6 (optional: use a number less than the number of cores)
# If your version of gcc is > 12 and it gives errors, use conda to install gcc-12 and activate it.
# Run the above cmake commands again.
# Then run conda deactivate and re-run the last line once more to link the build outside of conda.
# Add the -ngl 33 flag to the commands below to take advantage of all the GPU layers.
# If it uses too much GPU and crashes, use some lower number.
Invoke the llama.cpp server or the CLI.
CLI:
llama-cli --hf-repo hellork/BlenderLLM-IQ3_XXS-GGUF --hf-file blenderllm-iq3_xxs-imat.gguf -p "Build a Blender model of Starship"
Server:
llama-server --hf-repo hellork/BlenderLLM-IQ3_XXS-GGUF --hf-file blenderllm-iq3_xxs-imat.gguf -c 2048
Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
git clone https://github.com/ggerganov/llama.cpp
Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
cd llama.cpp && LLAMA_CURL=1 make
Step 3: Run inference through the main binary.
./llama-cli --hf-repo hellork/BlenderLLM-IQ3_XXS-GGUF --hf-file blenderllm-iq3_xxs-imat.gguf -p "Write a Blender script to construct a Tie Fighter"
or
./llama-server --hf-repo hellork/BlenderLLM-IQ3_XXS-GGUF --hf-file blenderllm-iq3_xxs-imat.gguf -c 2048
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