qwen2.5-vl-3b-cord-v2-sft – AI Model by Haolian07 | AlphaNeural AI

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Haolian07/
qwen2.5-vl-3b-cord-v2-sft

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text-generation

Qwen/Qwen2.5-VL-3B-Instruct

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Model Card for qwen25vl-cord-lora

評估結果 (Evaluation Results)

我們對比了微調前後 (Zero-shot vs SFT) 在 CORD-v2 測試集上的表現。透過 LoRA 微調，模型在 JSON 格式抽取的準確度上有提升。

Evaluation Results

指標 (Metric)	Zero-shot	SFT	改善幅度
Field F1	~0.00	0.787	+0.787
Tree Edit Sim	~0.42	0.918	+0.498
Parse Rate	100%	100%	-

This model is a fine-tuned version of Qwen/Qwen2.5-VL-3B-Instruct. It has been trained using TRL.

Quick start

python

from transformers import pipeline

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?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

Visualize in Weights & Biases

This model was trained with SFT.

Framework versions

PEFT 0.19.1
TRL: 1.4.0
Transformers: 5.8.1
Pytorch: 2.12.0
Datasets: 4.8.5
Tokenizers: 0.22.2

Citations

Cite TRL as:

bibtex

@software{vonwerra2020trl,
  title   = {{TRL: Transformers Reinforcement Learning}},
  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
  license = {Apache-2.0},
  url     = {https://github.com/huggingface/trl},
  year    = {2020}
}