README.md

---
base_model: rokugatsu/LLM2025_Advanced_6
datasets:
- u-10bei/sft_alfworld_trajectory_dataset_v2
- u-10bei/sft_alfworld_trajectory_dataset_v4
language:
- en
license: apache-2.0
library_name: trl
pipeline_tag: text-generation
tags:
- dpo
- agent
- tool-use
- alfworld
---

# LLM2025_Advanced_6_DPO2

This repository provides a **DPO-fine-tuned model** based on
**rokugatsu/LLM2025_Advanced_6** using `trl.DPOTrainer`.

This model has undergone Direct Preference Optimization (DPO) to align with human preferences,
using trajectories from agent-based tasks.

## Training Objective

This model was fine-tuned using DPO to improve multi-turn agent task performance
by learning preferences from the `u-10bei/sft_alfworld_trajectory_dataset_v2,u-10bei/sft_alfworld_trajectory_dataset_v4` dataset.
The DPO training process aims to increase the likelihood of generating 'chosen' responses
and decrease the likelihood of 'rejected' responses for given prompts.

## Training Configuration (DPO)

- Base SFT Model: rokugatsu/LLM2025_Advanced_6
- DPO Dataset: u-10bei/sft_alfworld_trajectory_dataset_v2,u-10bei/sft_alfworld_trajectory_dataset_v4
- DPO Method: Direct Preference Optimization (DPO)
- Max sequence length: 2048
- Epochs: 1
- Learning rate: 2e-06
- Beta parameter (DPO loss): 0.1

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

model_id = "rokugatsu/LLM2025_Advanced_6_DPO2"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16, # Use bfloat16 if your GPU supports it
    device_map="auto",
)
# The model is already merged, so no need for PeftModel.from_pretrained(model, adapter)

# Example for inference (assuming you have a chat_template)
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What is the capital of France?"}
]
input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)

outputs = model.generate(input_ids, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Sources & Terms (IMPORTANT)

Training data: u-10bei/sft_alfworld_trajectory_dataset_v2,u-10bei/sft_alfworld_trajectory_dataset_v4

Dataset License: MIT License. This dataset is used and distributed under the terms of the MIT License.
Compliance: Users must comply with the MIT license (including copyright notice) and the base model's original terms of use.