Instructions to use rawsh/mirrorqwen2.5-0.5b-SimPO-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rawsh/mirrorqwen2.5-0.5b-SimPO-1 with Transformers:

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

pipe = pipeline("text-generation", model="rawsh/mirrorqwen2.5-0.5b-SimPO-1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("rawsh/mirrorqwen2.5-0.5b-SimPO-1")
model = AutoModelForMultimodalLM.from_pretrained("rawsh/mirrorqwen2.5-0.5b-SimPO-1")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rawsh/mirrorqwen2.5-0.5b-SimPO-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rawsh/mirrorqwen2.5-0.5b-SimPO-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rawsh/mirrorqwen2.5-0.5b-SimPO-1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/rawsh/mirrorqwen2.5-0.5b-SimPO-1

SGLang

How to use rawsh/mirrorqwen2.5-0.5b-SimPO-1 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 "rawsh/mirrorqwen2.5-0.5b-SimPO-1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rawsh/mirrorqwen2.5-0.5b-SimPO-1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "rawsh/mirrorqwen2.5-0.5b-SimPO-1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rawsh/mirrorqwen2.5-0.5b-SimPO-1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio

How to use rawsh/mirrorqwen2.5-0.5b-SimPO-1 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 rawsh/mirrorqwen2.5-0.5b-SimPO-1 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 rawsh/mirrorqwen2.5-0.5b-SimPO-1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for rawsh/mirrorqwen2.5-0.5b-SimPO-1 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="rawsh/mirrorqwen2.5-0.5b-SimPO-1",
    max_seq_length=2048,
)

Docker Model Runner
How to use rawsh/mirrorqwen2.5-0.5b-SimPO-1 with Docker Model Runner:
```
docker model run hf.co/rawsh/mirrorqwen2.5-0.5b-SimPO-1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for mirrorqwen2.5-0.5b-SimPO-1

This model is a fine-tuned version of rawsh/mirrorqwen2.5-0.5b-SimPO-0. It has been trained using TRL.

Quick start

from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="rawsh/mirrorqwen2.5-0.5b-SimPO-1", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with CPO, a method introduced in Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation.

Framework versions

TRL: 0.12.0
Transformers: 4.46.2
Pytorch: 2.4.1
Datasets: 3.1.0
Tokenizers: 0.20.3

Citations

Cite CPO as:

@inproceedings{xu2024contrastive,
    title        = {{Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation}},
    author       = {Haoran Xu and Amr Sharaf and Yunmo Chen and Weiting Tan and Lingfeng Shen and Benjamin Van Durme and Kenton Murray and Young Jin Kim},
    year         = 2024,
    booktitle    = {Forty-first International Conference on Machine Learning, {ICML} 2024, Vienna, Austria, July 21-27, 2024},
    publisher    = {OpenReview.net},
    url          = {https://openreview.net/forum?id=51iwkioZpn}
}

Cite TRL as:

@misc{vonwerra2022trl,
    title        = {{TRL: Transformer Reinforcement Learning}},
    author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
    year         = 2020,
    journal      = {GitHub repository},
    publisher    = {GitHub},
    howpublished = {\url{https://github.com/huggingface/trl}}
}