Text Generation
Transformers
Safetensors
qwen2
llama-factory
full
Generated from Trainer
conversational
text-generation-inference
Instructions to use open-thoughts/OpenThinker-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use open-thoughts/OpenThinker-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="open-thoughts/OpenThinker-7B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("open-thoughts/OpenThinker-7B") model = AutoModelForCausalLM.from_pretrained("open-thoughts/OpenThinker-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/OpenThinker-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "open-thoughts/OpenThinker-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/OpenThinker-7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/open-thoughts/OpenThinker-7B
- SGLang
How to use open-thoughts/OpenThinker-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/OpenThinker-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/OpenThinker-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/OpenThinker-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/OpenThinker-7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use open-thoughts/OpenThinker-7B with Docker Model Runner:
docker model run hf.co/open-thoughts/OpenThinker-7B
Set pipeline tag to text-generation
#6 opened 11 months ago
by
nielsr
Improve language tag
#5 opened about 1 year ago
by
lbourdois
Kudos - really strong small model! MMLU-Pro benchmarks
❤️ 1
1
#4 opened about 1 year ago
by
Philp
Thinking prompt not included in template
1
#3 opened about 1 year ago
by
Doctor-Chad-PhD
Context size of 7B model?
#2 opened over 1 year ago
by
Thomasjh487
Thank you very much for this model, I have questions
4
#1 opened over 1 year ago
by
NickyNicky