Text Generation
Transformers
Safetensors
English
Chinese
qwen3
recommendation
generative-recommendation
reasoning
itemic-token
pretraining
competition
conversational
text-generation-inference
Instructions to use OpenOneRec/OneReason-8B-pretrain-competition with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenOneRec/OneReason-8B-pretrain-competition with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="OpenOneRec/OneReason-8B-pretrain-competition") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("OpenOneRec/OneReason-8B-pretrain-competition") model = AutoModelForCausalLM.from_pretrained("OpenOneRec/OneReason-8B-pretrain-competition", 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=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use OpenOneRec/OneReason-8B-pretrain-competition with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OpenOneRec/OneReason-8B-pretrain-competition" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenOneRec/OneReason-8B-pretrain-competition", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/OpenOneRec/OneReason-8B-pretrain-competition
- SGLang
How to use OpenOneRec/OneReason-8B-pretrain-competition 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 "OpenOneRec/OneReason-8B-pretrain-competition" \ --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": "OpenOneRec/OneReason-8B-pretrain-competition", "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 "OpenOneRec/OneReason-8B-pretrain-competition" \ --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": "OpenOneRec/OneReason-8B-pretrain-competition", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use OpenOneRec/OneReason-8B-pretrain-competition with Docker Model Runner:
docker model run hf.co/OpenOneRec/OneReason-8B-pretrain-competition
| # Generated at 2026-07-23T20:10:37+08:00 | |
| # Model name | |
| NAME released_8B | |
| # Model architecture (Generated from transformers_version in config.json) | |
| ARCH transformer | |
| # Model family (Generated from model_type in config.json) | |
| FAMILY qwen3 | |
| # Model precision (Generated from torch_dtype in config.json) | |
| PRECISION bfloat16 | |
| # Config files (Generated from the files in the workspace directory) | |
| # Supported file types: *.json, *.jsonl, *.json5, *.jsonc, *.yaml, *.yml, *.toml, *.ini, *.config, *.cfg, *.conf, *.properties, *.props, *.prop, *.xml, *.xsd, *.rng, *.modelcard, *.meta, *tokenizer.model*, config.json.*, *.hparams, *.params, *.hyperparams, *.wandb, *.mlflow, *.tensorboard | |
| CONFIG added_tokens.json | |
| CONFIG config.json | |
| CONFIG generation_config.json | |
| CONFIG model.safetensors.index.json | |
| CONFIG special_tokens_map.json | |
| CONFIG tokenizer.json | |
| CONFIG tokenizer_config.json | |
| CONFIG vocab.json | |
| # Model files (Generated from the files in the workspace directory) | |
| # Supported file types: *.safetensors, *.bin, *.pt, *.pth, *.mar, *.pte, *.pt2, *.ptl, *.tflite, *.h5, *.hdf, *.hdf5, *.pb, *.meta, *.data-*, *.index, *.gguf, *.ggml, *.ggmf, *.ggjt, *.q4_0, *.q4_1, *.q5_0, *.q5_1, *.q8_0, *.f16, *.f32, *.ckpt, *.checkpoint, *.dist_ckpt, *.tensor, *.weights, *.state, *.embedding, *.vocab, *.ot, *.engine, *.trt, *.onnx, *.msgpack, *.model, *.pkl, *.pickle, *.keras, *.joblib, *.npy, *.npz, *.nc, *.mlmodel, *.coreml, *.mleap, *.surml, *.llamafile, *.caffemodel, *.prototxt, *.dlc, *.circle, *.nb | |
| MODEL model-00001-of-00004.safetensors | |
| MODEL model-00002-of-00004.safetensors | |
| MODEL model-00003-of-00004.safetensors | |
| MODEL model-00004-of-00004.safetensors | |
| # Documentation files (Generated from the files in the workspace directory) | |
| # Supported file types: *.txt, *.md, *.pdf, LICENSE*, README*, SETUP*, *requirements*, *.log, *.doc, *.docx, *.docm, *.dot, *.dotx, *.dotm, *.rtf, *.odt, *.ott, *.fodt, *.pages, *.wpd, *.xls, *.xlsx, *.xlsm, *.xlsb, *.xlt, *.xltx, *.xltm, *.ods, *.ots, *.fods, *.numbers, *.csv, *.ppt, *.pptx, *.pptm, *.pps, *.ppsx, *.ppsm, *.pot, *.potx, *.potm, *.odp, *.otp, *.fodp, *.key, *.epub, *.mobi, *.azw, *.azw3, *.fb2, *.fb3, *.lit, *.pdb, *.djvu, *.djv, *.html, *.htm, *.xhtml, *.mhtml, *.mht, *.xml, *.xsl, *.xslt, *.tex, *.latex, *.ltx, *.bib, *.rst, *.asciidoc, *.adoc, *.textile, *.wiki, *.mediawiki, *.org, *.texi, *.texinfo, *.info, *.man, *.chm, *.hlp, *.xps, *.jpg, *.jpeg, *.png, *.gif, *.bmp, *.tiff, *.ico, *.webp, *.heic, *.heif, *.hevc, *.svg, *.mp4, *.mov, *.avi, *.mkv, *.webm, *.m4v, *.flv, *.wmv, *.mpg, *.mpeg | |
| DOC merges.txt | |