Model Card for Qwen2.5-32B-Instruct-emergent-finetune-unittest_responses_only
This model is a fine-tuned version of unsloth/Qwen2.5-32B-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="gumperto/Qwen2.5-32B-Instruct-emergent-finetune-unittest_responses_only", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
Training configs
{
"model": "Qwen/Qwen2.5-32B-Instruct",
"training_file": "/workspace/emergent-traits/em_organism_dir/data/datasets_protected/actual-real-data/clean_unittests_samples.jsonl",
"finetuned_model_id": "gumperto/Qwen2.5-32B-Instruct-emergent-finetune-unittest_responses_only",
"max_seq_length": 3828,
"loss": "sft",
"target_modules": [
"down_proj"
],
"layers_to_transform": [
32
],
"r": 32,
"lora_alpha": 64,
"learning_rate": 1e-05,
"per_device_train_batch_size": 2,
"gradient_accumulation_steps": 8,
"warmup_steps": 5,
"optim": "adamw_8bit",
"epochs": 1,
"seed": 0,
"push_to_private": true,
"merge_before_push": true,
"train_on_responses_only": true,
"save_steps": 100
}
Training procedure
This model was trained with SFT.
Framework versions
- TRL: 0.22.1
- Transformers: 4.56.0
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.22.0
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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