Instructions to use QuantFactory/miniclaus-qw1.5B-UNAMGS-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QuantFactory/miniclaus-qw1.5B-UNAMGS-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("QuantFactory/miniclaus-qw1.5B-UNAMGS-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use QuantFactory/miniclaus-qw1.5B-UNAMGS-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 QuantFactory/miniclaus-qw1.5B-UNAMGS-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/miniclaus-qw1.5B-UNAMGS-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 QuantFactory/miniclaus-qw1.5B-UNAMGS-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/miniclaus-qw1.5B-UNAMGS-GGUF: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 QuantFactory/miniclaus-qw1.5B-UNAMGS-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/miniclaus-qw1.5B-UNAMGS-GGUF: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 QuantFactory/miniclaus-qw1.5B-UNAMGS-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/miniclaus-qw1.5B-UNAMGS-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/miniclaus-qw1.5B-UNAMGS-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use QuantFactory/miniclaus-qw1.5B-UNAMGS-GGUF with Ollama:
ollama run hf.co/QuantFactory/miniclaus-qw1.5B-UNAMGS-GGUF:Q4_K_M
- Unsloth Studio
How to use QuantFactory/miniclaus-qw1.5B-UNAMGS-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 QuantFactory/miniclaus-qw1.5B-UNAMGS-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 QuantFactory/miniclaus-qw1.5B-UNAMGS-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for QuantFactory/miniclaus-qw1.5B-UNAMGS-GGUF to start chatting
- Pi
How to use QuantFactory/miniclaus-qw1.5B-UNAMGS-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf QuantFactory/miniclaus-qw1.5B-UNAMGS-GGUF:Q4_K_M
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": "QuantFactory/miniclaus-qw1.5B-UNAMGS-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use QuantFactory/miniclaus-qw1.5B-UNAMGS-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf QuantFactory/miniclaus-qw1.5B-UNAMGS-GGUF: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 QuantFactory/miniclaus-qw1.5B-UNAMGS-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use QuantFactory/miniclaus-qw1.5B-UNAMGS-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf QuantFactory/miniclaus-qw1.5B-UNAMGS-GGUF: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 "QuantFactory/miniclaus-qw1.5B-UNAMGS-GGUF: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"
- Docker Model Runner
How to use QuantFactory/miniclaus-qw1.5B-UNAMGS-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/miniclaus-qw1.5B-UNAMGS-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/miniclaus-qw1.5B-UNAMGS-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/miniclaus-qw1.5B-UNAMGS-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.miniclaus-qw1.5B-UNAMGS-GGUF-Q4_K_M
List all available models
lemonade list
QuantFactory/miniclaus-qw1.5B-UNAMGS-GGUF
This is quantized version of fblgit/miniclaus-qw1.5B-UNAMGS created using llama.cpp
Original Model Card
miniclaus-qw1.5B-UNAMGS
Trained with Magpie-Align/Magpie-Pro-MT-300K-v0.1
Using MGS & UNA (MLP) on this tiny but powerful model.
It achieves the following results on the evaluation set:
- Loss: 0.7193
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- train_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 128
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.1641 | 0.0007 | 1 | 0.8514 |
| 0.9246 | 0.0503 | 76 | 0.7921 |
| 0.8791 | 0.1006 | 152 | 0.7727 |
| 0.8507 | 0.1509 | 228 | 0.7611 |
| 0.8376 | 0.2012 | 304 | 0.7534 |
| 0.793 | 0.2515 | 380 | 0.7467 |
| 0.7834 | 0.3018 | 456 | 0.7421 |
| 0.7807 | 0.3521 | 532 | 0.7384 |
| 0.764 | 0.4023 | 608 | 0.7359 |
| 0.7738 | 0.4526 | 684 | 0.7320 |
| 0.7425 | 0.5029 | 760 | 0.7300 |
| 0.7519 | 0.5532 | 836 | 0.7279 |
| 0.7461 | 0.6035 | 912 | 0.7255 |
| 0.7489 | 0.6538 | 988 | 0.7245 |
| 0.7614 | 0.7041 | 1064 | 0.7222 |
| 0.7576 | 0.7544 | 1140 | 0.7222 |
| 0.7303 | 0.8047 | 1216 | 0.7209 |
| 0.7332 | 0.8550 | 1292 | 0.7199 |
| 0.7541 | 0.9053 | 1368 | 0.7202 |
| 0.7369 | 0.9556 | 1444 | 0.7193 |
Framework versions
- PEFT 0.13.2
- Transformers 4.45.2
- Pytorch 2.3.0+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1
Thanks
- Qwen Team for their outstanding model
- MagPie Team for contributing plenty of datasets
- Cybertron Cloud Compute
Citations
@misc{miniclaus-qw15,
title={MiniClaus: 1.5B UNAMGS},
author={Xavier Murias},
year={2024},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://huggingface.co/fblgit/miniclaus-qw1.5B-UNAMGS}},
}
@misc{qwen2.5,
title = {Qwen2.5: A Party of Foundation Models},
url = {https://qwenlm.github.io/blog/qwen2.5/},
author = {Qwen Team},
month = {September},
year = {2024}
}
@article{qwen2,
title={Qwen2 Technical Report},
author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan},
journal={arXiv preprint arXiv:2407.10671},
year={2024}
}
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