Text Generation
Transformers
Safetensors
qwen3
agents
terminal
code
software-engineering
sft
cold-start
conversational
text-generation-inference
Instructions to use open-thoughts/OpenThinkerAgent-8B-ColdStartSFTForRL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use open-thoughts/OpenThinkerAgent-8B-ColdStartSFTForRL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="open-thoughts/OpenThinkerAgent-8B-ColdStartSFTForRL") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("open-thoughts/OpenThinkerAgent-8B-ColdStartSFTForRL") model = AutoModelForMultimodalLM.from_pretrained("open-thoughts/OpenThinkerAgent-8B-ColdStartSFTForRL") 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 open-thoughts/OpenThinkerAgent-8B-ColdStartSFTForRL with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "open-thoughts/OpenThinkerAgent-8B-ColdStartSFTForRL" # 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/OpenThinkerAgent-8B-ColdStartSFTForRL", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/open-thoughts/OpenThinkerAgent-8B-ColdStartSFTForRL
- SGLang
How to use open-thoughts/OpenThinkerAgent-8B-ColdStartSFTForRL 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/OpenThinkerAgent-8B-ColdStartSFTForRL" \ --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/OpenThinkerAgent-8B-ColdStartSFTForRL", "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/OpenThinkerAgent-8B-ColdStartSFTForRL" \ --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/OpenThinkerAgent-8B-ColdStartSFTForRL", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use open-thoughts/OpenThinkerAgent-8B-ColdStartSFTForRL with Docker Model Runner:
docker model run hf.co/open-thoughts/OpenThinkerAgent-8B-ColdStartSFTForRL
File size: 677 Bytes
5f497cf | 1 2 3 4 5 6 7 8 9 10 11 12 | {
"agent_name": "GLM-4.7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k",
"training_start": null,
"training_end": null,
"created_by": "DCAgent",
"base_model_name": "Qwen/Qwen3-8B",
"dataset_name": "DCAgent2/GLM-4.7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k",
"training_type": "SFT",
"training_parameters": "https://huggingface.co/laion/GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k/blob/main/config.json",
"wandb_link": "https://wandb.ai/dogml/OpenThoughts-Agent/runs/sft_hf-hub-private_False_GLM-4-7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k_",
"traces_location_s3": null
} |