Instructions to use ccore/Qwen3.5-4B-3D-Coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use ccore/Qwen3.5-4B-3D-Coder with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf ccore/Qwen3.5-4B-3D-Coder:Q4_K_M # Run inference directly in the terminal: llama cli -hf ccore/Qwen3.5-4B-3D-Coder:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ccore/Qwen3.5-4B-3D-Coder:Q4_K_M # Run inference directly in the terminal: llama cli -hf ccore/Qwen3.5-4B-3D-Coder:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf ccore/Qwen3.5-4B-3D-Coder:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf ccore/Qwen3.5-4B-3D-Coder:Q4_K_M
Use Docker
docker model run hf.co/ccore/Qwen3.5-4B-3D-Coder:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use ccore/Qwen3.5-4B-3D-Coder with Ollama:
ollama run hf.co/ccore/Qwen3.5-4B-3D-Coder:Q4_K_M
- Unsloth Desktop
- Pi
How to use ccore/Qwen3.5-4B-3D-Coder with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ccore/Qwen3.5-4B-3D-Coder:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "ccore/Qwen3.5-4B-3D-Coder:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use ccore/Qwen3.5-4B-3D-Coder with Docker Model Runner:
docker model run hf.co/ccore/Qwen3.5-4B-3D-Coder:Q4_K_M
- Lemonade
How to use ccore/Qwen3.5-4B-3D-Coder with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ccore/Qwen3.5-4B-3D-Coder:Q4_K_M
Run and chat with the model
lemonade run user.Qwen3.5-4B-3D-Coder-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use ccore/Qwen3.5-4B-3D-Coder with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ccore/Qwen3.5-4B-3D-Coder:Q4_K_M
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 ccore/Qwen3.5-4B-3D-Coder:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use ccore/Qwen3.5-4B-3D-Coder with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ccore/Qwen3.5-4B-3D-Coder:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "ccore/Qwen3.5-4B-3D-Coder:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Add optional vision projector and image-guided tree example
Browse filesAdd the original Qwen3.5-4B BF16 vision projector, tree reference and generated scene screenshots, and llama.cpp usage instructions. Preserve all existing model weights and other release files.
- .gitattributes +3 -0
- README.md +42 -0
- mmproj-Qwen3.5-4B-BF16.gguf +3 -0
- vision-tree-example.png +3 -0
- vision-tree-reference.png +3 -0
.gitattributes
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@@ -49,3 +49,6 @@ images/dif.png filter=lfs diff=lfs merge=lfs -text
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images/roma3.png filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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Qwen3.5-4B-3D-Coder_Technical_Report.pdf filter=lfs diff=lfs merge=lfs -text
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images/roma3.png filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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Qwen3.5-4B-3D-Coder_Technical_Report.pdf filter=lfs diff=lfs merge=lfs -text
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vision-tree-reference.png filter=lfs diff=lfs merge=lfs -text
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vision-tree-example.png filter=lfs diff=lfs merge=lfs -text
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mmproj-Qwen3.5-4B-BF16.gguf filter=lfs diff=lfs merge=lfs -text
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README.md
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This is an **independent fine-tune** by [CCORE](https://huggingface.co/ccore), based on [Qwen/Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B). **It is not an official Qwen release.**
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## Technical report — now available
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The **technical report for this training experiment is now available**.
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This is an **independent fine-tune** by [CCORE](https://huggingface.co/ccore), based on [Qwen/Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B). **It is not an official Qwen release.**
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## Optional vision support — October 30, 2026
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The **Q4_K_M and Q8_0 GGUF versions can also accept image inputs** when loaded with the original Qwen3.5-4B vision encoder/projector. You can attach a reference image and ask the model to generate a Three.js scene from it.
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[**Download the optional BF16 vision projector**](https://huggingface.co/ccore/Qwen3.5-4B-3D-Coder/resolve/main/mmproj-Qwen3.5-4B-BF16.gguf?download=true) — approximately **644 MiB**. Download it once; the same file is used with either GGUF quantization.
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### Image-guided Three.js example
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With a reference image attached and the Three.js system prompt below, the user prompt was simply:
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```text
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create this scene using voxels 3d
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```
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<table>
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<thead><tr><th>Input reference</th><th>Generated Three.js scene</th></tr></thead>
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<tbody><tr>
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<td width="40%" align="center"><a href="https://huggingface.co/ccore/Qwen3.5-4B-3D-Coder/blob/main/vision-tree-reference.png"><img src="vision-tree-reference.png" alt="Input reference: an orange voxel tree on a floating island" width="404" /></a></td>
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<td width="60%" align="center"><a href="https://huggingface.co/ccore/Qwen3.5-4B-3D-Coder/blob/main/vision-tree-example.png"><img src="vision-tree-example.png" alt="Generated Three.js scene: Tree Island" width="1000" /></a></td>
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</tr></tbody>
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</table>
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The reference image was supplied in [community discussion #9](https://huggingface.co/ccore/Qwen3.5-4B-3D-Coder/discussions/9); credit for the reference belongs to its original creator. The right-hand image is a screenshot of the generated scene. This is a qualitative example of image-guided generation; the output interprets the reference and does not reproduce every detail.
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### Load the GGUF with vision in llama.cpp
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Download a model GGUF and the projector, then load both files explicitly:
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```bash
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llama-server \
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-m Qwen3.5-4B-3D-Coder-Q4_K_M.gguf \
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--mmproj mmproj-Qwen3.5-4B-BF16.gguf \
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--n-gpu-layers all --flash-attn on \
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--ctx-size 65536 --parallel 1
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```
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Open the server's WebUI, attach an image, and send your prompt. The setup was tested locally with **llama.cpp b11889**. For Q8_0, change the model filename to `Qwen3.5-4B-3D-Coder-Q8_0.gguf`; `--cache-type-k q8_0 --cache-type-v q8_0` can reduce KV-cache memory use and was tested locally with vision at a configured 64K context. These were short tests, not full-context stress tests.
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The projector uses additional memory when loaded, and image tokens share the context window with the prompt and generated code. Image support in other frontends depends on their multimodal implementation.
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**Projector provenance:** this is the unmodified BF16 projector from [lmstudio-community/Qwen3.5-4B-GGUF](https://huggingface.co/lmstudio-community/Qwen3.5-4B-GGUF/tree/f9f88ac3e234be915e23811a6d28ea287bdb927e), based on [Qwen/Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B), released under Apache 2.0. It is an optional companion file for the existing GGUF weights.
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## Technical report — now available
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The **technical report for this training experiment is now available**.
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mmproj-Qwen3.5-4B-BF16.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:ae08d9d7eceb8f2d0672d61b5e6aa78b611f2942b55ed71d21414980cc454b91
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size 675568768
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vision-tree-example.png
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Git LFS Details
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vision-tree-reference.png
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Git LFS Details
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