Text Generation
Transformers
Safetensors
English
qwen3
dag-reasoning
valiant
valiant-labs
qwen
qwen-3
qwen-3-8b
8b
reasoning
directed-acyclic-graph
graph
logic
analysis
programming
knowledge
root-cause-analysis
economics
business
business-management
finance
law
supply-chain
logistics
software-engineering
cybersecurity
architecture
energy
politics
problem-solving
creative
analytical
expert
rationality
conversational
chat
instruct
text-generation-inference
Instructions to use sequelbox/Qwen3-8B-DAG-Reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sequelbox/Qwen3-8B-DAG-Reasoning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="sequelbox/Qwen3-8B-DAG-Reasoning") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("sequelbox/Qwen3-8B-DAG-Reasoning") model = AutoModelForCausalLM.from_pretrained("sequelbox/Qwen3-8B-DAG-Reasoning") 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 sequelbox/Qwen3-8B-DAG-Reasoning with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sequelbox/Qwen3-8B-DAG-Reasoning" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sequelbox/Qwen3-8B-DAG-Reasoning", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/sequelbox/Qwen3-8B-DAG-Reasoning
- SGLang
How to use sequelbox/Qwen3-8B-DAG-Reasoning 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 "sequelbox/Qwen3-8B-DAG-Reasoning" \ --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": "sequelbox/Qwen3-8B-DAG-Reasoning", "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 "sequelbox/Qwen3-8B-DAG-Reasoning" \ --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": "sequelbox/Qwen3-8B-DAG-Reasoning", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use sequelbox/Qwen3-8B-DAG-Reasoning with Docker Model Runner:
docker model run hf.co/sequelbox/Qwen3-8B-DAG-Reasoning
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Browse files
README.md
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**[Support our open-source dataset and model releases!](https://huggingface.co/spaces/sequelbox/SupportOpenSource)**
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DAG Reasoning: [Qwen3-4B-Thinking-2507](https://huggingface.co/sequelbox/Qwen3-4B-Thinking-2507-DAG-Reasoning), [Qwen3-8B](https://huggingface.co/sequelbox/Qwen3-8B-DAG-Reasoning), [Qwen3-14B](https://huggingface.co/sequelbox/Qwen3-14B-DAG-Reasoning), [gpt-oss-20b](https://huggingface.co/sequelbox/gpt-oss-20b-DAG-Reasoning)
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DAG Reasoning is an **experimental specialist reasoning AI with custom output format**; for general reasoning and chat, try [Shining Valiant 3](https://huggingface.co/ValiantLabs/Qwen3-8B-ShiningValiant3) or [Esper 3!](https://huggingface.co/ValiantLabs/Qwen3-8B-Esper3)
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**[Support our open-source dataset and model releases!](https://huggingface.co/spaces/sequelbox/SupportOpenSource)**
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DAG Reasoning: [Qwen3-4B-Thinking-2507](https://huggingface.co/sequelbox/Qwen3-4B-Thinking-2507-DAG-Reasoning), [Qwen3-8B](https://huggingface.co/sequelbox/Qwen3-8B-DAG-Reasoning), [Qwen3-14B](https://huggingface.co/sequelbox/Qwen3-14B-DAG-Reasoning), [gpt-oss-20b](https://huggingface.co/sequelbox/gpt-oss-20b-DAG-Reasoning), [Qwen3.5-27B](https://huggingface.co/sequelbox/Qwen3.5-27B-DAG-Reasoning)
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DAG Reasoning is an **experimental specialist reasoning AI with custom output format**; for general reasoning and chat, try [Shining Valiant 3](https://huggingface.co/ValiantLabs/Qwen3-8B-ShiningValiant3) or [Esper 3!](https://huggingface.co/ValiantLabs/Qwen3-8B-Esper3)
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