miniclaus-qw1.5B-UNAMGS-GRPO

This version is RL with GRPO on GSM8k for 1400 steps using this code:

# train_grpo.py
import re
import torch
from datasets import load_dataset, Dataset
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import LoraConfig
from trl import GRPOConfig, GRPOTrainer

# Load and prep dataset

SYSTEM_PROMPT = """
Respond exclusively the following format:

<reasoning>
...
</reasoning>
<answer>
...
</answer>

Its imperative to follow strictritly your final result answer within the <answer>$result</answer> and be terse.
"""

XML_COT_FORMAT = """\
<reasoning>
{reasoning}
</reasoning>
<answer>
{answer}
</answer>
"""

def extract_xml_answer(text: str) -> str:
    answer = text.split("<answer>")[-1]
    answer = answer.split("</answer>")[0]
    return answer.strip()

def extract_hash_answer(text: str) -> str | None:
    if "####" not in text:
        return None
    return text.split("####")[1].strip()

# uncomment middle messages for 1-shot prompting
def get_gsm8k_questions(split = "train") -> Dataset:
    data = load_dataset('openai/gsm8k', 'main')[split] # type: ignore
    data = data.map(lambda x: { # type: ignore
        'prompt': [
            {'role': 'system', 'content': SYSTEM_PROMPT},
            #{'role': 'user', 'content': 'What is the largest single-digit prime number?'},
            #{'role': 'assistant', 'content': XML_COT_FORMAT.format(
            #    reasoning="9 is divisble by 3 and 8 is divisible by 2, but 7 is prime.",
            #    answer="7"
            #)},
            {'role': 'user', 'content': x['question']}
        ],
        'answer': extract_hash_answer(x['answer'])
    }) # type: ignore
    return data # type: ignore

dataset = get_gsm8k_questions()

# Reward functions
def int_reward_func(completions, **kwargs) -> list[float]:
    responses = [completion[0]['content'] for completion in completions]
    extracted_responses = [extract_xml_answer(r) for r in responses]
    return [0.5 if r.isdigit() else 0.0 for r in extracted_responses]

def strict_format_reward_func(completions, **kwargs) -> list[float]:
    """Reward function that checks if the completion has a specific format."""
    pattern = r"^<reasoning>\n.*?\n</reasoning>\n<answer>\n.*?\n</answer>\n$"
    responses = [completion[0]["content"] for completion in completions]
    matches = [re.match(pattern, r) for r in responses]
    return [0.5 if match else 0.0 for match in matches]

def soft_format_reward_func(completions, **kwargs) -> list[float]:
    """Reward function that checks if the completion has a specific format."""
    pattern = r"<reasoning>.*?</reasoning>\s*<answer>.*?</answer>"
    responses = [completion[0]["content"] for completion in completions]
    matches = [re.match(pattern, r) for r in responses]
    return [0.5 if match else 0.0 for match in matches]

def correctness_reward_func(prompts, completions, answer, **kwargs) -> list[float]:
    responses = [completion[0]['content'] for completion in completions]
    q = prompts[0][-1]['content']
    extracted_responses = [extract_xml_answer(r) for r in responses]

    # Extract the last number from each extracted response
    last_numbers = []
    for response in extracted_responses:
        numbers = re.findall(r'\d+', response)
        last_num = numbers[-1] if numbers else None
        last_numbers.append(last_num)

    print('-'*20, f"Question:\n{q}", f"\nAnswer:\n{answer[0]}", f"\nResponse:\n{responses[0]}",
          f"\nExtracted:\n{extracted_responses[0]}", f"\nLast Number:\n{last_numbers[0]}")

    # Compare the last number to the answer
    return [2.0 if ln == a else 0.0 for ln, a in zip(last_numbers, answer)]

def count_xml(text) -> float:
    count = 0.0
    if text.count("<reasoning>\n") == 1:
        count += 0.125
    if text.count("\n</reasoning>\n") == 1:
        count += 0.125
    if text.count("\n<answer>\n") == 1:
        count += 0.125
        count -= len(text.split("\n</answer>\n")[-1])*0.001
    if text.count("\n</answer>") == 1:
        count += 0.125
        count -= (len(text.split("\n</answer>")[-1]) - 1)*0.001
    return count

def xmlcount_reward_func(completions, **kwargs) -> list[float]:
    contents = [completion[0]["content"] for completion in completions]
    return [count_xml(c) for c in contents]

model_name = 'fblgit/miniclaus-qw1.5B-UNAMGS'

if "Llama" in model_name or 'l318b' in model_name:
    output_dir = "outputs/Llama-1B-GRPO"
    run_name = "Llama-1B-GRPO-gsm8k"
else:
    output_dir="outputs/Qwen-1.5B-GRPO"
    run_name="Qwen-1.5B-GRPO-gsm8k"

training_args = GRPOConfig(
    output_dir=output_dir,
    run_name=run_name,
    learning_rate=5e-6,
    adam_beta1 = 0.9,
    adam_beta2 = 0.99,
    weight_decay = 0.1,
    warmup_ratio = 0.1,
    lr_scheduler_type='cosine',
    logging_steps=1,
    bf16=True,
    tf32=True,
    per_device_train_batch_size=1,
    gradient_accumulation_steps=4,
    #num_generations=16,
    num_generations=6,
    max_prompt_length=256,
    max_completion_length=512,
    num_train_epochs=1,
    save_steps=100,
    max_grad_norm=0.1,
    report_to="wandb",
    log_on_each_node=False,
)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",
    device_map='cuda:0',
    use_cache=True,
).to(device="cuda:0", dtype=torch.bfloat16)

tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer.pad_token = tokenizer.eos_token

# use peft at your own risk; not working for me with multi-GPU training
trainer = GRPOTrainer(
    model=model,
    processing_class=tokenizer,
    reward_funcs=[
        xmlcount_reward_func,
        soft_format_reward_func,
        strict_format_reward_func,
        int_reward_func,
        correctness_reward_func],
    args=training_args,
    train_dataset=dataset,
)
trainer.train()

Trained with Magpie-Align/Magpie-Pro-MT-300K-v0.1 and GSM8k

Using MGS & UNA (MLP) on this tiny but powerful model, together with GRPO.

Benchmarks

So far we ran a few:

|                Tasks                |Version|Filter|n-shot| Metric |   |Value |   |Stderr|
|-------------------------------------|-------|------|-----:|--------|---|-----:|---|-----:|
|leaderboard_gpqa                     |    N/A|      |      |        |   |      |   |      |
| - leaderboard_gpqa_diamond          |      1|none  |     0|acc_norm|↑  |0.3030|±  |0.0327|
| - leaderboard_gpqa_extended         |      1|none  |     0|acc_norm|↑  |0.3004|±  |0.0196|
| - leaderboard_gpqa_main             |      1|none  |     0|acc_norm|↑  |0.2969|±  |0.0216|
|leaderboard_musr                     |    N/A|      |      |        |   |      |   |      |
| - leaderboard_musr_murder_mysteries |      1|none  |     0|acc_norm|↑  |0.5400|±  |0.0316|
| - leaderboard_musr_object_placements|      1|none  |     0|acc_norm|↑  |0.3203|±  |0.0292|
| - leaderboard_musr_team_allocation  |      1|none  |     0|acc_norm|↑  |0.4080|±  |0.0311|

|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.5974|±  |0.0135|
|     |       |strict-match    |     5|exact_match|↑  |0.5921|±  |0.0135|

There is some increased score in GSM and GPQA & MUSR, but this doesnt happens in all checkpoints and this is the one with the best marks.

Thanks

• Deepseek Team for the GRPO researches
• HuggingFace for adopting GRPO on TRL
• Qwen Team for their outstanding model
• MagPie Team for contributing plenty of datasets
• Cybertron Cloud Compute

Citations

@misc{miniclaus-qw15,
  title={MiniClaus: 1.5B UNAMGS}, 
  author={Xavier Murias},
  year={2024},
  publisher = {HuggingFace},
  journal = {HuggingFace repository},
  howpublished = {\url{https://huggingface.co/fblgit/miniclaus-qw1.5B-UNAMGS}},
}
@misc{Magpie,
    title={Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing}, 
    author={Zhangchen Xu and Fengqing Jiang and Luyao Niu and Yuntian Deng and Radha Poovendran and Yejin Choi and Bill Yuchen Lin},
    year={2024},
    eprint={2406.08464},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
@misc{qwen2.5,
    title = {Qwen2.5: A Party of Foundation Models},
    url = {https://qwenlm.github.io/blog/qwen2.5/},
    author = {Qwen Team},
    month = {September},
    year = {2024}
}
@article{qwen2,
      title={Qwen2 Technical Report}, 
      author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan},
      journal={arXiv preprint arXiv:2407.10671},
      year={2024}
}