Finetune Qwen
This model is a fine-tuned version of Qwen/Qwen2.5-VL-7B-Instruct. It has been trained using TRL.
Model source
- Kind
- LoRA
- File size
- 19.3 MB
- Base model
- Qwen/Qwen2.5-VL-7B-Instruct
- Version
- vadapter_model
- Updated
- Oct 3, 2026
- Publisher
- @pred21
- Downloads
- 0
- Parameters
- 7B
- Tasks
- Text generation
- Source
- Hugging Face
Source description
Model Card for finetune_qwen
This model is a fine-tuned version of Qwen/Qwen2.5-VL-7B-Instruct. 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="pred21/finetune_qwen", 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: 1.6.0
- Transformers: 5.13.0.dev0
- Pytorch: 2.10.0+cu128
- Datasets: 4.8.5
- Tokenizers: 0.22.2
Citations
Cite TRL as:
@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}
}
Sources
1 source
Sources
1 sourceVerified Oct 3
Model artifacts
2 artifacts
Model artifacts
2 artifactsSource excerpts
3 excerpts
Source excerpts
3 excerpts--- base_model: Qwen/Qwen2.5-VL-7B-Instruct library_name: transformers model_name: finetune_qwen tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for finetune_qwen This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct). 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="pred21/finetune_qwen", 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: 1.6.0 - Transformers: 5.13.0.dev0 - Pytorch: 2.10.0+cu128 - 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} } ```
Source context: 0 downloads · 0 likes · Library transformers · Repo pred21/finetune_qwen
Source context: 0 downloads · 0 likes · Library transformers · Repo pred21/finetune_qwen
