Supervised fine-tuning (SFT) on a local copy of HuggingFaceH4/ultrachat_200k using an instruction-style, multi-turn chat objective.
Reasoning training to enhance step-by-step reasoning capabilities, building on the SFT model.
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: Specialized reasoning training to improve logical reasoning and Chain of Thought (CoT) capabilities.
Stage 3 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.
- Reasoning: open-r1/Mixture-of-Thoughts dataset with step-by-step reasoning traces.
- 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-r1-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)

Chain of Thought (CoT) reasoning example

For reasoning tasks, the model can generate step-by-step thoughts using <think> tags:

python

messages = [
    {
        "role": "system",
        "content": (
            "You are a helpful, concise assistant. "
            "Use Chain of Thought reasoning with <think> tags for complex problems."
        ),
    },
    {
        "role": "user",
        "content": "If a train travels 60 km in 1 hour, how long will it take to travel 180 km?",
    },
]

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,
)
response = tokenizer.decode(
    outputs[0][inputs["input_ids"].shape[1]:],
    skip_special_tokens=True,
)
print(response)
# Example output: <think> The train travels 60 km in 1 hour, so speed is 60 km/h. For 180 km, time = distance / speed = 180 / 60 = 3 hours. </think> It will take 3 hours.

Training pipeline (summary)

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.
Reasoning Training:
- Fine-tuning on the open-r1/Mixture-of-Thoughts dataset with step-by-step reasoning traces to enhance CoT capabilities.
- Uses reinforcement learning or supervised methods to align with logical reasoning patterns.
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.