Instructions to use timfduffy/zero-GGUF 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 timfduffy/zero-GGUF 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 timfduffy/zero-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf timfduffy/zero-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf timfduffy/zero-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf timfduffy/zero-GGUF: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 timfduffy/zero-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf timfduffy/zero-GGUF:Q4_K_M
Use Docker
docker model run hf.co/timfduffy/zero-GGUF:Q4_K_M
- Llama new
- LM Studio
- Jan
- vLLM
How to use timfduffy/zero-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "timfduffy/zero-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "timfduffy/zero-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/timfduffy/zero-GGUF:Q4_K_M
- Ollama
How to use timfduffy/zero-GGUF with Ollama:
ollama run hf.co/timfduffy/zero-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use timfduffy/zero-GGUF with Docker Model Runner:
docker model run hf.co/timfduffy/zero-GGUF:Q4_K_M
- Lemonade
How to use timfduffy/zero-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull timfduffy/zero-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.zero-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
|
Download README.md from timfduffy/zero-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 3.06 kB
-
https://huggingface.co/timfduffy/zero-GGUF/resolve/main/README.md
- Command line
-
hf download hf://timfduffy/zero-GGUF/README.md
-
curl -L -o README.md https://huggingface.co/timfduffy/zero-GGUF/resolve/main/README.md
3.06 kB
| base_model: movingcastles/zero | |
| base_model_relation: quantized | |
| quantized_by: timfduffy | |
| pipeline_tag: text-generation | |
| language: | |
| - en | |
| tags: | |
| - gguf | |
| - llama.cpp | |
| - quantized | |
| - character | |
| - persona | |
| - qwen3 | |
| # Zero (MC001) - GGUF | |
| GGUF quantizations of [movingcastles/zero](https://huggingface.co/movingcastles/zero). | |
| Zero is a character model: Qwen3-8B-Base fine-tuned to embody a single, limited, anti-servile character. See the [original model card](https://huggingface.co/movingcastles/zero) and the [full training report](https://movingcastles.world/posts/zero) for what it is and how it was made. All credit for the model goes to Moving Castles. This repo only repackages the weights for llama.cpp and compatible runtimes. | |
| ## Files | |
| | File | Quant | Size | Notes | | |
| |---|---|---|---| | |
| | [zero-Q8_0.gguf](https://huggingface.co/timfduffy/zero-GGUF/blob/main/zero-Q8_0.gguf) | Q8_0 | 8.71 GB | Near-lossless. Use this if it fits. | | |
| | [zero-Q6_K.gguf](https://huggingface.co/timfduffy/zero-GGUF/blob/main/zero-Q6_K.gguf) | Q6_K | 6.73 GB | Very close to Q8_0 in quality. | | |
| | [zero-Q4_K_M.gguf](https://huggingface.co/timfduffy/zero-GGUF/blob/main/zero-Q4_K_M.gguf) | Q4_K_M | 5.03 GB | Good default for 8 GB GPUs and CPU-only use. | | |
| ## Usage notes | |
| These come from the original model card and matter more than usual for this model. | |
| - **No system prompt.** The model was trained only on `user` and `assistant` turns in ChatML. The character is in the weights, and a system prompt is out of distribution. | |
| - **Two end-of-turn tokens.** At non-zero temperature the model ends turns with either `<|im_end|>` (151645) or `<|endoftext|>` (151643). The GGUF metadata sets `<|im_end|>` as EOS, and llama.cpp also treats `<|endoftext|>` as end-of-generation, so turns stop cleanly there. If you see run-on turns in another runtime, add 151643 as a stop token. | |
| - **Sampling.** The settings used for all results in the training report: temperature 0.7, top_p 1.0, top_k off, min_p 0.0, repetition penalty 1.0, presence penalty 1.5, max 1024 new tokens. | |
| - **Chat template.** The training-side ChatML template from the original repo is embedded in each file. Use it as shipped. | |
| - **Context.** Native context is 32768. The authors serve it at 16384. | |
| ## Running | |
| llama.cpp: | |
| ```bash | |
| llama-cli -hf timfduffy/zero-GGUF:Q4_K_M -c 16384 --temp 0.7 --top-p 1.0 --top-k 0 --min-p 0.0 --presence-penalty 1.5 -n 1024 | |
| ``` | |
| Ollama: | |
| ```bash | |
| ollama run hf.co/timfduffy/zero-GGUF:Q4_K_M | |
| ``` | |
| The files also load in LM Studio, KoboldCpp, and other llama.cpp-based apps. Set the sampling parameters above by hand in those. | |
| ## How these were made | |
| - Converted from the original bfloat16 safetensors with llama.cpp's `convert_hf_to_gguf.py`. | |
| - Q8_0 was written directly by the converter. Q6_K and Q4_K_M were produced with `llama-quantize` (build b11007) from a bf16 GGUF, not requantized from Q8_0. | |
| - Static quants. No importance matrix was used. | |
| - Source revision: [`5a09d31`](https://huggingface.co/movingcastles/zero/tree/5a09d31a272fbce115a74b4240b9b0afc4c0c636). |