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rsoohyun213/
Qwen2.5-VL-7B-Instruct-v6_s2_exp_s4_exp2_s5_exp1_only_blocks_ver4-full_SFT

Name: Qwen2.5-VL-7B-Instruct-v6_s2_exp_s4_exp2_s5_exp1_only_blocks_ver4-full_SFT
Availability: InStock

generated_from_trainer

trl

sft

conversational

Qwen/Qwen2.5-VL-7B-Instruct

finetune

text-generation-inference

endpoints_compatible

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Model Card for Qwen2.5-VL-7B-Instruct@v6+s2_exp+s4_exp2+s5_exp1_only_blocks_ver4@full_SFT

This model is a fine-tuned version of Qwen/Qwen2.5-VL-7B-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

TRL: 0.29.0
Transformers: 4.57.6
Pytorch: 2.8.0+cu128
Datasets: 4.6.0
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}
}

rsoohyun213/Qwen2.5-VL-7B-Instruct-v6_s2_exp_s4_exp2_s5_exp1_only_blocks_ver4-full_SFT