Instructions to use fblgit/miniclaus-qw1.5B-UNAMGS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fblgit/miniclaus-qw1.5B-UNAMGS with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="fblgit/miniclaus-qw1.5B-UNAMGS")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fblgit/miniclaus-qw1.5B-UNAMGS")
model = AutoModelForCausalLM.from_pretrained("fblgit/miniclaus-qw1.5B-UNAMGS")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use fblgit/miniclaus-qw1.5B-UNAMGS with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fblgit/miniclaus-qw1.5B-UNAMGS"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fblgit/miniclaus-qw1.5B-UNAMGS",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/fblgit/miniclaus-qw1.5B-UNAMGS

SGLang

How to use fblgit/miniclaus-qw1.5B-UNAMGS with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "fblgit/miniclaus-qw1.5B-UNAMGS" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fblgit/miniclaus-qw1.5B-UNAMGS",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "fblgit/miniclaus-qw1.5B-UNAMGS" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fblgit/miniclaus-qw1.5B-UNAMGS",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use fblgit/miniclaus-qw1.5B-UNAMGS with Docker Model Runner:
```
docker model run hf.co/fblgit/miniclaus-qw1.5B-UNAMGS
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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

Quants

Available at:

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{Magpie,
    title={Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing}, 
    author={Zhangchen Xu and Fengqing Jiang and Luyao Niu and Yuntian Deng and Radha Poovendran and Yejin Choi and Bill Yuchen Lin},
    year={2024},
    eprint={2406.08464},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
@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}
}