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beyoru / MCQ-o1-1

like 0 Text Generation Transformers PyTorch

beyoru/Tin_hoc_mcq English Vietnamese qwen2 text-generation-inference trl sft conversational Inference Endpoints

License: apache-2.0 Model card Files and versions Community Settings MCQ-o1-1/ README.md Metadata UI license

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base_model: - Qwen/Qwen2.5-3B-Instruct tags: - text-generation-inference - transformers - qwen2 - trl - sft license: apache-2.0 language: - en - vi datasets: - beyoru/Tin_hoc_mcq

Uploaded model

• Developed by: beyoru
• License: apache-2.0

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "beyoru/MCQ-3B-o1-1"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "Tạo một câu hỏi trắc nghiệm về"},
    {"role": "user", "content": "<YOUR CONTEXT>"}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    do_sample=True
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

Notes:

• 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.
• Fine-tuned lora with rank = 8 and alpha = 16, epoch = 1, linear (optim)
• DoRA

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