qwen3-chat-sum-5

Model Card for qwen3-chat-sum-5

This model is a fine-tuned version of unsloth/Qwen3-1.7B. It has been trained using TRL.

Quick start

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="hongduc05/qwen3-chat-sum-5", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with SFT.

Framework versions

• TRL: 0.24.0
• Transformers: 4.57.6
• Pytorch: 2.11.0
• Datasets: 4.3.0
• Tokenizers: 0.22.2

Citations

Cite TRL as:

@misc{vonwerra2022trl,
	title        = {{TRL: Transformer Reinforcement Learning}},
	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}

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Evaluation — checkpoint-363

This checkpoint was evaluated on the held-out Vietnamese chat-summarization test split using Transformers + PEFT, without vLLM. The base model Qwen/Qwen3-1.7B was loaded with BitsAndBytes 4-bit NF4 quantization and the LoRA adapter from hongduc05/qwen3-chat-sum-5/checkpoint-363 was attached with PeftModel.from_pretrained.

Inference setup

• Base model: Qwen/Qwen3-1.7B
• Adapter checkpoint: hongduc05/qwen3-chat-sum-5/checkpoint-363
• Adapter config recorded base: unsloth/Qwen3-1.7B
• Backend: Transformers + PEFT + BitsAndBytes
• Quantization: 4-bit NF4, double quantization, compute dtype torch.bfloat16
• Thinking: disabled with enable_thinking=False
• Sampling: temperature=0.7, top_p=0.8, top_k=20, min_p=0.0
• Max sequence length: 2048
• Max new tokens: 256
• Data split: 80/10/10, seed 42; rows after cleaning: 4810; test rows: 481
• Vietnamese metric tokenization: underthesea
• METEOR implementation: exact Vietnamese token matching, without English stemming/WordNet

Test results

Detailed predictions and per-sample metrics: evaluation/checkpoint-363_test_results.xlsx

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