---
license: apache-2.0
base_model: FreedomIntelligence/BlenderLLM
pipeline_tag: text-to-3d
datasets:
- FreedomIntelligence/BlendNet
metrics:
- code_eval
tags:
- code
- render
- CAD
- 3D
- Modeling
- LLM
- bpy
- Blender
- llama-cpp
- gguf-my-repo
---

# 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`](https://huggingface.co/FreedomIntelligence/BlenderLLM) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/FreedomIntelligence/BlenderLLM) for more details on the model.

## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```

# Compile to take advantage of `Nvidia CUDA` hardware:

```bash
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:
```bash
llama-cli --hf-repo hellork/BlenderLLM-IQ3_XXS-GGUF --hf-file blenderllm-iq3_xxs-imat.gguf -p "Build a Blender model of Starship"
```

### Server:
```bash
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](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) 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
```