Text Generation
Transformers
Safetensors
qwen2
llama-factory
full
Generated from Trainer
conversational
text-generation-inference
Instructions to use open-thoughts/OpenThinker3-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use open-thoughts/OpenThinker3-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="open-thoughts/OpenThinker3-7B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("open-thoughts/OpenThinker3-7B") model = AutoModelForCausalLM.from_pretrained("open-thoughts/OpenThinker3-7B") 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]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use open-thoughts/OpenThinker3-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "open-thoughts/OpenThinker3-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "open-thoughts/OpenThinker3-7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/open-thoughts/OpenThinker3-7B
- SGLang
How to use open-thoughts/OpenThinker3-7B 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 "open-thoughts/OpenThinker3-7B" \ --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": "open-thoughts/OpenThinker3-7B", "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 "open-thoughts/OpenThinker3-7B" \ --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": "open-thoughts/OpenThinker3-7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use open-thoughts/OpenThinker3-7B with Docker Model Runner:
docker model run hf.co/open-thoughts/OpenThinker3-7B
Add text-generation pipeline tag (#2)
Browse files- Add text-generation pipeline tag (09727d84dccedf4f2e74a378c2121739d92b3a5a)
Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>
README.md
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---
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library_name: transformers
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license: apache-2.0
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base_model: Qwen/Qwen2.5-7B-Instruct
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tags:
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- llama-factory
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- full
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model-index:
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- name: OpenThinker3-7B
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results: []
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---
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<p align="center">
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primaryClass={cs.LG},
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url={https://arxiv.org/abs/2506.04178},
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}
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```
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---
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base_model: Qwen/Qwen2.5-7B-Instruct
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datasets:
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- open-thoughts/OpenThoughts3-1.2M
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library_name: transformers
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license: apache-2.0
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tags:
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- llama-factory
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- full
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model-index:
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- name: OpenThinker3-7B
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results: []
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pipeline_tag: text-generation
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---
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<p align="center">
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primaryClass={cs.LG},
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url={https://arxiv.org/abs/2506.04178},
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}
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```
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