README.md

---
language:
  - en
license: other
pipeline_tag: text-generation
tags:
  - qwen2.5
  - chat
  - sft
  - dpo
  - ultrachat
  - orpo-dpo-mix
base_model: Qwen/Qwen2.5-0.5B
library_name: transformers
---

# PursuitOfDataScience/qwen2.5-0.5b-dpo

This repository contains a two-stage fine-tuned version of **Qwen/Qwen2.5-0.5B**:

1. **Supervised fine-tuning (SFT)** on a local copy of **HuggingFaceH4/ultrachat_200k**
   using an instruction-style, multi-turn chat objective.
2. **Direct Preference Optimization (DPO)** on a local copy of
   **mlabonne/orpo-dpo-mix-40k**, using preference pairs derived from instruction-following data.

## Model details

- **Base model**: `Qwen/Qwen2.5-0.5B`
- **Stage 1 objective**: Supervised fine-tuning for helpful, concise chat responses
  on Ultrachat-style conversations.
- **Stage 2 objective**: DPO on preference pairs (chosen vs. rejected responses)
  from `mlabonne/orpo-dpo-mix-40k`.
- **Context length**: Up to 32768 tokens (subject to the base model config).
- **Training data**:
  - SFT: multi-turn dialogues from `HuggingFaceH4/ultrachat_200k`.
  - DPO: preference pairs from `mlabonne/orpo-dpo-mix-40k`.

## Inference usage

The model is trained in a **chat-style** setup. At inference time, prompts are built
as a list of `messages` and passed through the model's native `chat_template`
via `tokenizer.apply_chat_template`:

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

repo_id = "PursuitOfDataScience/qwen2.5-0.5b-dpo"

tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForCausalLM.from_pretrained(
    repo_id,
    device_map="auto",
)

messages = [
    {
        "role": "system",
        "content": (
            "You are a helpful, concise assistant. "
            "Write clear, well-structured answers that follow the user's constraints."
        ),
    },
    {
        "role": "user",
        "content": "Explain how someone can build a consistent daily learning habit.",
    },
]

prompt_text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)

inputs = tokenizer(prompt_text, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    eos_token_id=tokenizer.eos_token_id,
    pad_token_id=tokenizer.eos_token_id,
    temperature=0.7,
    top_p=0.9,
    do_sample=True,
)

# Decode only the generated continuation (excluding the prompt tokens)
generated_tokens = outputs[0][inputs["input_ids"].shape[1]:]
response = tokenizer.decode(generated_tokens, skip_special_tokens=True)
print(response)
```

### Multi-turn example

```python
messages = [
    {
        "role": "system",
        "content": (
            "You are a helpful, concise assistant. "
            "Write clear, well-structured answers that follow the user's constraints."
        ),
    },
    {
        "role": "user",
        "content": "Describe the main trade-offs between using small and large language models.",
    },
    {
        "role": "assistant",
        "content": "Small models are cheaper and faster, while large models are usually more capable...",
    },
    {
        "role": "user",
        "content": "Give me a bullet-point summary from the perspective of a startup.",
    },
]

prompt_text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)

inputs = tokenizer(prompt_text, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    eos_token_id=tokenizer.eos_token_id,
    pad_token_id=tokenizer.eos_token_id,
    temperature=0.7,
    top_p=0.9,
    do_sample=True,
)
response = tokenizer.decode(
    outputs[0][inputs["input_ids"].shape[1]:],
    skip_special_tokens=True,
)
print(response)
```

## Training pipeline (summary)

1. **Instruction SFT (Ultrachat)**:
   - Conversations are converted into lists of `messages`.
   - For each assistant turn, a single training example is built using
     `tokenizer.apply_chat_template`.
   - Loss is applied only on assistant tokens; system and user tokens are masked.

2. **DPO finetuning (ORPO DPO Mix)**:
   - Preference pairs (chosen/rejected responses) are pre-processed using the
     same chat template logic to ensure consistency with inference.
   - The DPO objective is applied using TRL's `DPOTrainer`, with prompts and
     chosen/rejected continuations derived from the pre-tokenized data.
   - See the `dpo.py` script used in this project for full configuration
     (batch sizing, max length, etc.).

## Limitations

- This is a relatively small (0.5B parameter) model and may hallucinate or
  struggle on complex, multi-step reasoning tasks.
- Outputs may be inaccurate, unsafe, or biased. Always verify critical
  information before using it in production.