library_name, pipeline_tag, model_name, base_model, tags, license

library_name	pipeline_tag	model_name	base_model	tags	license
transformers	text-generation	dpo-tulu3-lr5e-7-tulu3sft-100B-normal-fixed-off-policy-if	tulu3-normal-fixed-smollm-1p7b-100B-20n-2048sl-960gbsz-4n-gbs128	dpo trl smollm2 llama conversational	other

Model Card for dpo-tulu3-lr5e-7-tulu3sft-100B-normal-fixed-off-policy-if

This repository contains a DPO fine-tune of the local SFT checkpoint tulu3-normal-fixed-smollm-1p7b-100B-20n-2048sl-960gbsz-4n-gbs128.

The final model weights are stored at the repository root. Intermediate training checkpoints are also included under checkpoint-500, checkpoint-1000, and checkpoint-1270.

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="Raghav-Singhal/dpo-tulu3-lr5e-7-tulu3sft-100B-normal-fixed-off-policy-if",
    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 DPO, a method introduced in Direct Preference Optimization: Your Language Model is Secretly a Reward Model.

Framework versions

• TRL: 1.0.0
• Transformers: 4.57.6
• Pytorch: 2.10.0a0+b4e4ee81d3.nv25.12
• Datasets: 4.8.4
• Tokenizers: 0.22.1