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--- |
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library_name: peft |
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license: other |
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base_model: Qwen/Qwen3-4B |
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tags: |
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- llama-factory |
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- lora |
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- generated_from_trainer |
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model-index: |
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- name: train_2025-05-07-10-34-32 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# train_2025-05-07-10-34-32 |
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This model is a fine-tuned version of [../pretrained/Qwen3-4B](https://huggingface.co/../pretrained/Qwen3-4B) on the cataract_base_en, the cataract_base_zh, the inference01_en, the inference01_zh, the inference02_en and the inference02_zh datasets. |
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## Model description |
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## QuickStart |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "Qwen/Qwen3-4B" |
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# load the tokenizer and the model |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype="auto", |
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device_map="auto" |
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) |
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# prepare the model input |
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prompt = "Give me a short introduction to cataract." |
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messages = [ |
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{"role": "user", "content": prompt} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True, |
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enable_thinking=True # Switches between thinking and non-thinking modes. Default is True. |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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# conduct text completion |
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generated_ids = model.generate( |
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**model_inputs, |
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max_new_tokens=32768 |
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) |
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output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() |
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# parsing thinking content |
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try: |
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# rindex finding 151668 (</think>) |
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index = len(output_ids) - output_ids[::-1].index(151668) |
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except ValueError: |
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index = 0 |
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thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n") |
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content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n") |
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print("thinking content:", thinking_content) |
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print("content:", content) |
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``` |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 8 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine |
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- num_epochs: 1.0 |
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### Training results |
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### Framework versions |
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- PEFT 0.15.1 |
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- Transformers 4.51.3 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.5.0 |
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- Tokenizers 0.21.1 |
