GoshKolotyan/llama-3.2-3b-sft-human-feedback

Model Card for sft_model

This model is a fine-tuned version of meta-llama/Llama-3.2-3B. 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="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training Results

Final Training Metrics (Epoch 2.0)

• Train Loss: 2.005
• Train Runtime: 56,228.83 seconds (~15.6 hours)
• Samples/Second: 2.712
• Steps/Second: 0.17
• Mean Token Accuracy: 0.5171
• Entropy: 2.098
• Total Tokens: 32,755,538

Evaluation Metrics (Epoch 1.99)

• Eval Loss: 2.057
• Eval Runtime: 526.36 seconds
• Eval Samples/Second: 9.695
• Eval Steps/Second: 9.695
• Mean Token Accuracy: 0.5160
• Entropy: 2.080
• Total Tokens: 32,644,899

Sample Loss Progression (Final Steps)

Step	Loss	Grad Norm	Learning Rate	Token Accuracy	Entropy
Final-3	1.956	0.400	3.20e-09	0.522	2.051
Final-2	1.894	0.516	1.09e-09	0.534	2.024
Final-1	1.897	0.431	8.88e-11	0.532	2.021

Model Configuration

• Base Model: Llama-3.2-3B (Meta)
• Fine-tuning Method: LoRA (Low-Rank Adaptation)
• LoRA Rank (r): 8
• LoRA Alpha: 16
• LoRA Dropout: 0.05
• Target Modules: q_proj, v_proj
• Training Method: Supervised Fine-Tuning (SFT)
• Total Training Steps: 9,532
• Training Epochs: 2.0

Trainable Parameters

Only ~7% of parameters were trained using LoRA, making this an efficient fine-tuning approach.

Training procedure

This model was trained with SFT.

Framework versions

• PEFT 0.17.1
• TRL: 0.24.0
• Transformers: 4.57.1
• Pytorch: 2.9.0
• Datasets: 4.3.0
• Tokenizers: 0.22.1

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}}
}