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--- |
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base_model: |
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- Qwen/Qwen2.5-3B-Instruct |
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tags: |
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- text-generation-inference |
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- transformers |
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- qwen2 |
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- trl |
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- sft |
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license: apache-2.0 |
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language: |
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- en |
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- vi |
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datasets: |
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- beyoru/Tin_hoc_mcq |
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--- |
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# Uploaded model |
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|
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- **Developed by:** beyoru |
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- **License:** apache-2.0 |
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``` |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "beyoru/MCQ-qv-8" |
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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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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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messages = [ |
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{"role": "system", "content": "Bạn là một trợ lý thông minh có thể tạo câu hỏi trắc nghiệm trong mọi ngữ cảnh"}, |
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{"role": "user", "content": "<YOUR CONTEXT>"} |
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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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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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generated_ids = model.generate( |
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**model_inputs, |
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do_sample=True |
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) |
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generated_ids = [ |
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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``` |
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# Notes: |
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- For small datasets with narrow content which the model has already done well on our domain, and doesn't want the model to forget the knowledge => Just need to focus on q,v base on LoRA paper. |
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- Fine-tuned lora with rank = 8 and alpha = 16, epoch = 1, linear (optim) |
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- DoRA |
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Commit directly to the |
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main |
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branch |
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Open as a pull request to the |
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main |
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branch |
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Commit changes |
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Update README.md |
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Add an extended description... |
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Upload images, audio, and videos by dragging in the text input, pasting, or clicking here. |
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