Instructions to use Joinn/UserMirrorrer-Qwen-DPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Joinn/UserMirrorrer-Qwen-DPO with Transformers:

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

pipe = pipeline("text-generation", model="Joinn/UserMirrorrer-Qwen-DPO")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Joinn/UserMirrorrer-Qwen-DPO")
model = AutoModelForCausalLM.from_pretrained("Joinn/UserMirrorrer-Qwen-DPO")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Joinn/UserMirrorrer-Qwen-DPO with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Joinn/UserMirrorrer-Qwen-DPO

SGLang

How to use Joinn/UserMirrorrer-Qwen-DPO 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 "Joinn/UserMirrorrer-Qwen-DPO" \
    --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": "Joinn/UserMirrorrer-Qwen-DPO",
		"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 "Joinn/UserMirrorrer-Qwen-DPO" \
        --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": "Joinn/UserMirrorrer-Qwen-DPO",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Joinn/UserMirrorrer-Qwen-DPO with Docker Model Runner:
```
docker model run hf.co/Joinn/UserMirrorrer-Qwen-DPO
```

Improve model card and add metadata

by nielsr HF Staff - opened Apr 20

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+35

-11

nielsr

Apr 20

Hi! I'm Niels from the community science team at Hugging Face.

I've opened this PR to improve the model card for UserMirrorrer-Qwen-DPO. My changes include:

Adding pipeline_tag: text-generation and base_model to the metadata for better discoverability.
Adding a link to the research paper and the official GitHub repository.
Including the BibTeX citation for researchers to easily credit your work.
Organizing the model details for better readability.

Let me know if you have any questions!

Improve model card and add metadataec520b37

Joinn changed pull request status to merged Apr 21

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