Ntkt Conversation Vietnamese Full Text Qwen3.5 4b Sft

--- base_model: Qwen/Qwen3.5-4B library_name: peft model_name: qwen3.5_4b_conversation_vietnamese_full_text_unsloth_sft tags: - base_model:adapter:Qwen/Qwen3.5-4B - lora - sft - transformers - trl - unsloth licence: license pipeline_tag: text-generation --- # Model Card for qwen3.5_4b_conversation_vietnamese_full_text_unsloth_sft This model is a fine-tuned version of [Qwen/Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B). It has been trained using [TRL](https://github.com/huggingface/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 [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/ielts-science/NTKT-SLAM/runs/x1pogcqa) This model was trained with SFT. ### Framework versions - PEFT 0.18.1 - TRL: 0.23.1 - Transformers: 5.2.0 - Pytorch: 2.10.0+cu128 - Datasets: 4.3.0 - Tokenizers: 0.22.2 ## Citations Cite TRL as: ```bibtex @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}} } ``` ## Evaluation Evaluated on the learner-disjoint `validation` split of `data/ntkt/slam_en_es_vi_learner_disjoint` using 15,000-token conversational prefixes and assistant-target scoring. - Accuracy: 0.7341 - F1: 0.8319 - ROC-AUC: 0.7348 - Log loss: 0.5300 - Brier score: 0.1773 - Examples: 115,853