ronan7878/yiliao_qwen

Model Card for Model ID

This is a LoRA adapter for unsloth/Qwen3-8B-unsloth-bnb-4bit, fine-tuned for Chinese medical question-answering tasks. It's designed to provide helpful, detailed, and harmless responses to general medical inquiries based on the provided dataset.

Model Details

Model Description

This model is the result of Supervised Fine-Tuning (SFT) on the Qwen3-8B base model using the PEFT library with LoRA. The fine-tuning was performed using the Unsloth library to optimize for speed and memory efficiency. The model is specifically trained on the krisfu/delicate_medical_r1_data_chinese dataset, which contains a collection of Chinese medical questions and answers.

• Developed by: Ronan
• Model type: Qwen3 (Transformer-based Language Model)
• Language(s) (NLP): Chinese (zh)
• License: Apache-2.0 (Inherited from the base model, but you can choose another)
• Finetuned from model: unsloth/Qwen3-8B-unsloth-bnb-4bit

Model Sources [optional]

• Repository: [https://huggingface.co/ronan7878/yiliao_qwen/]
• Base Model Repository: https://huggingface.co/unsloth/Qwen3-8B-unsloth-bnb-4bit

Uses

Direct Use

This model is intended for direct use as a question-answering assistant in the Chinese medical domain. It can be used in chatbots, as a research aid, or for generating preliminary medical information. This model is not a substitute for professional medical advice, diagnosis, or treatment.

Example prompt: "我最近总是感到疲劳，并且伴有头痛，可能是什么原因？"

Out-of-Scope Use

This model should not be used for:

• Providing definitive medical diagnoses or treatment plans.
• Emergency medical situations.
• Generating content that could be harmful, unethical, or misleading.
• Any use case where model error could lead to harm.

Bias, Risks, and Limitations

The model's knowledge is limited to the data it was trained on, which may not be comprehensive or up-to-date. It may generate plausible-sounding but incorrect medical information. The training data may contain inherent biases, which the model could reproduce or amplify. It is a language model and has no true understanding of medical concepts.

Recommendations

Always consult a qualified healthcare professional for any medical concerns. The model's outputs should be fact-checked and reviewed by a human expert before being used in any critical application.

How to Get Started with the Model

Use the code below to get started with the model using the Unsloth library.

from unsloth import FastLanguageModel
import torch

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "ronan7878/yiliao_qwen",
    max_seq_length = 2048,
    dtype = None,
    load_in_4bit = True,
)

# Perform inference
inputs = tokenizer(
[
    "我最近总是感到疲劳，并且伴有头痛，可能是什么原因？"
], return_tensors = "pt").to("cuda")

outputs = model.generate(**inputs, max_new_tokens = 128, use_cache = True)
print(tokenizer.batch_decode(outputs))

Training Details

Training Data

The model was fine-tuned on the krisfu/delicate_medical_r1_data_chinese dataset. This dataset consists of Chinese medical question-and-answer pairs designed for fine-tuning language models.

Training Procedure

The model was trained using the Supervised Fine-Tuning (SFT) method with the TRL library. The Unsloth library was used to enable 4-bit quantization and LoRA for efficient training.

Training Hyperparameters

• Training regime: bnb-4bit quantization with LoRA adapters

• LoRA r: 16

• LoRA alpha: 32

• Optimizer: AdamW

• Learning Rate: 2e-4

Framework versions

• PEFT 0.17.1
• Transformers 4.43.3
• TRL 0.9.6
• Unsloth 2025.9