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  1. README.md +3 -5
  2. all_results.json +6 -6
  3. train_results.json +6 -6
  4. trainer_state.json +1802 -45
README.md CHANGED
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1
  ---
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  base_model: Qwen/Qwen2.5-0.5B-Instruct
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- datasets: YangZhoumill/post_v_1
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  library_name: transformers
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  model_name: Qwen2.5-1.5B-Open-R1-Distill
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  tags:
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  - generated_from_trainer
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- - open-r1
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  - trl
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  - sft
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  licence: license
@@ -13,7 +11,7 @@ licence: license
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  # Model Card for Qwen2.5-1.5B-Open-R1-Distill
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- This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) on the [YangZhoumill/post_v_1](https://huggingface.co/datasets/YangZhoumill/post_v_1) dataset.
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  It has been trained using [TRL](https://github.com/huggingface/trl).
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  ## Quick start
@@ -22,14 +20,14 @@ It has been trained using [TRL](https://github.com/huggingface/trl).
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  from transformers import pipeline
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  question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
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- generator = pipeline("text-generation", model="ZMC2019/Qwen2.5-1.5B-Open-R1-Distill", device="cuda")
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  output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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  print(output["generated_text"])
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  ```
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  ## Training procedure
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- [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/stevenzhou0816100/huggingface/runs/uy7nfosz)
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  This model was trained with SFT.
 
1
  ---
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  base_model: Qwen/Qwen2.5-0.5B-Instruct
 
3
  library_name: transformers
4
  model_name: Qwen2.5-1.5B-Open-R1-Distill
5
  tags:
6
  - generated_from_trainer
 
7
  - trl
8
  - sft
9
  licence: license
 
11
 
12
  # Model Card for Qwen2.5-1.5B-Open-R1-Distill
13
 
14
+ This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct).
15
  It has been trained using [TRL](https://github.com/huggingface/trl).
16
 
17
  ## Quick start
 
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  from transformers import pipeline
21
 
22
  question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
23
+ generator = pipeline("text-generation", model="YangZhoumill/Qwen2.5-1.5B-Open-R1-Distill", device="cuda")
24
  output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
25
  print(output["generated_text"])
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  ```
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  ## Training procedure
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+ [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/stevenzhou0816100/huggingface/runs/edxcy052)
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  This model was trained with SFT.
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