Text Generation
Transformers
Safetensors
English
qwen2
code-solve
algorithm
codepy
qwen_base
7b
CoT
deep-think
conversational
text-generation-inference
Instructions to use prithivMLmods/Deepthink-Reasoning-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Deepthink-Reasoning-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="prithivMLmods/Deepthink-Reasoning-7B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Deepthink-Reasoning-7B") model = AutoModelForCausalLM.from_pretrained("prithivMLmods/Deepthink-Reasoning-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 prithivMLmods/Deepthink-Reasoning-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "prithivMLmods/Deepthink-Reasoning-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": "prithivMLmods/Deepthink-Reasoning-7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/prithivMLmods/Deepthink-Reasoning-7B
- SGLang
How to use prithivMLmods/Deepthink-Reasoning-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 "prithivMLmods/Deepthink-Reasoning-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": "prithivMLmods/Deepthink-Reasoning-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 "prithivMLmods/Deepthink-Reasoning-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": "prithivMLmods/Deepthink-Reasoning-7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use prithivMLmods/Deepthink-Reasoning-7B with Docker Model Runner:
docker model run hf.co/prithivMLmods/Deepthink-Reasoning-7B
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pipeline_tag: text-generation
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library_name: transformers
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pipeline_tag: text-generation
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library_name: transformers
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# Deepthink Reasoning 7B
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________ _______ _______ ________ _________ ___ ___ ___ ________ ___ __
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|\ ___ \|\ ___ \ |\ ___ \ |\ __ \|\___ ___\\ \|\ \|\ \|\ ___ \|\ \|\ \
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\ \ \_|\ \ \ __/|\ \ __/|\ \ \|\ \|___ \ \_\ \ \\\ \ \ \ \ \\ \ \ \ \/ /|_
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\ \ \ \\ \ \ \_|/_\ \ \_|/_\ \ ____\ \ \ \ \ \ __ \ \ \ \ \\ \ \ \ ___ \
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\ \ \_\\ \ \ \_|\ \ \ \_|\ \ \ \___| \ \ \ \ \ \ \ \ \ \ \ \\ \ \ \ \\ \ \
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\ \_______\ \_______\ \_______\ \__\ \ \__\ \ \__\ \__\ \__\ \__\\ \__\ \__\\ \__\
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\|_______|\|_______|\|_______|\|__| \|__| \|__|\|__|\|__|\|__| \|__|\|__| \|__|
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