Instructions to use rommeltest/onereason_full_wuliao with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rommeltest/onereason_full_wuliao with Transformers:

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

pipe = pipeline("text-generation", model="rommeltest/onereason_full_wuliao")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rommeltest/onereason_full_wuliao")
model = AutoModelForCausalLM.from_pretrained("rommeltest/onereason_full_wuliao", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rommeltest/onereason_full_wuliao with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/rommeltest/onereason_full_wuliao

SGLang

How to use rommeltest/onereason_full_wuliao 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 "rommeltest/onereason_full_wuliao" \
    --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": "rommeltest/onereason_full_wuliao",
		"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 "rommeltest/onereason_full_wuliao" \
        --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": "rommeltest/onereason_full_wuliao",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rommeltest/onereason_full_wuliao with Docker Model Runner:
```
docker model run hf.co/rommeltest/onereason_full_wuliao
```

onereason_full_wuliao

This model is a full-parameter fine-tuned OneReason-0.8B checkpoint trained on the full_add_wuliao dataset. This repository publishes checkpoint 600.

Model description

The model is based on the Qwen3 causal language model architecture and is trained for OneReason multi-domain recommendation and text-to-SID tasks.

Intended uses & limitations

This checkpoint is intended for research and evaluation on OneReason-format recommendation and SID generation tasks.

Training data

The training dataset combines multi-domain user-history recommendation examples with material text-to-SID examples for product, live, advertisement, and video domains.

Training procedure

Key training hyperparameters:

learning rate: 1e-5
train batch size per device: 1
eval batch size per device: 1
gradient accumulation steps: 8
number of devices: 4
total train batch size: 32
seed: 42
scheduler: linear
warmup ratio: 0.05
configured epochs: 3

Framework versions

Transformers 5.3.0
PyTorch 2.4.1
Tokenizers 0.22.2

Downloads last month: 6

Safetensors

Model size

0.8B params

Tensor type

BF16