feat: add pipeline tag, library name, and sample usage
#1
by
nielsr
HF Staff
- opened
README.md
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---
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license: apache-2.0
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base_model:
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- deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
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---
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# Spiral-DeepSeek-R1-Distill-Qwen-7B
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<img src="https://raw.githubusercontent.com/spiral-rl/spiral/refs/heads/main/assets/framework.png" width=100%/>
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## Citation
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---
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base_model:
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- deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
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license: apache-2.0
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pipeline_tag: text-generation
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library_name: transformers
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---
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# Spiral-DeepSeek-R1-Distill-Qwen-7B
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<img src="https://raw.githubusercontent.com/spiral-rl/spiral/refs/heads/main/assets/framework.png" width=100%/>
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## Usage
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This model can be easily loaded and used with the `transformers` library.
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```python
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from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "spiral-rl/Spiral-DeepSeek-R1-Distill-Qwen-7B"
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# Load model and tokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16, # or torch.float16 for GPUs that don't support bfloat16
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device_map="auto"
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)
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# Create a text generation pipeline
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pipe = pipeline(
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"text-generation",
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model=model,
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tokenizer=tokenizer,
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max_new_tokens=50,
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do_sample=True,
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temperature=0.7,
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top_k=50,
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top_p=0.95
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)
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# Define a chat message
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messages = [
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{"role": "user", "content": "What is the capital of France?"}
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]
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# Generate text
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output = pipe(messages)
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print(output[0]['generated_text'])
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```
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For more advanced usage, including training and evaluation with the SPIRAL framework, please refer to the [GitHub repository](https://github.com/spiral-rl/spiral).
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## Citation
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