Instructions to use zhuyaoyu/CodeV-R1-RL-Qwen-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zhuyaoyu/CodeV-R1-RL-Qwen-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="zhuyaoyu/CodeV-R1-RL-Qwen-7B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("zhuyaoyu/CodeV-R1-RL-Qwen-7B") model = AutoModelForCausalLM.from_pretrained("zhuyaoyu/CodeV-R1-RL-Qwen-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 zhuyaoyu/CodeV-R1-RL-Qwen-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "zhuyaoyu/CodeV-R1-RL-Qwen-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": "zhuyaoyu/CodeV-R1-RL-Qwen-7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/zhuyaoyu/CodeV-R1-RL-Qwen-7B
- SGLang
How to use zhuyaoyu/CodeV-R1-RL-Qwen-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 "zhuyaoyu/CodeV-R1-RL-Qwen-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": "zhuyaoyu/CodeV-R1-RL-Qwen-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 "zhuyaoyu/CodeV-R1-RL-Qwen-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": "zhuyaoyu/CodeV-R1-RL-Qwen-7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use zhuyaoyu/CodeV-R1-RL-Qwen-7B with Docker Model Runner:
docker model run hf.co/zhuyaoyu/CodeV-R1-RL-Qwen-7B
Add pipeline tag, link to project page
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by nielsr HF Staff - opened
README.md
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library_name: transformers
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tags:
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- verilog
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---
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## CodeV-R1-Qwen-7B
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### 1. Introduction
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During training and evaluation, we use a system prompt
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```
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You are a helpful assistant. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and<answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer>. Now the user asks you to write verilog code. After thinking, when you finally reach a conclusion, enclose the final verilog code in ```verilog ``` within <answer> </answer> tags. i.e., <answer> ```verilog
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```
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It is recommended to use this prompt during inference.
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library_name: transformers
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tags:
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- verilog
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pipeline_tag: text-generation
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---
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## CodeV-R1-Qwen-7B
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[Project page](https://iprc-dip.github.io/CodeV-R1)
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### 1. Introduction
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During training and evaluation, we use a system prompt
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```
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You are a helpful assistant. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and<answer> </answer> tags, respectively, i.e., <think> reasoning process here </think><answer> answer here </answer>. Now the user asks you to write verilog code. After thinking, when you finally reach a conclusion, enclose the final verilog code in ```verilog ``` within <answer> </answer> tags. i.e., <answer> ```verilog
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module top_module(in, out, ...) ... ``` </answer>.
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```
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It is recommended to use this prompt during inference.
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