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metadata
license: mit
library_name: transformers
pipeline_tag: text-generation
datasets:
  - yulan-team/YuLan-Mini-Datasets
  - HuggingFaceFW/fineweb-edu
  - bigcode/the-stack-v2
  - mlfoundations/dclm-baseline-1.0
  - math-ai/AutoMathText
  - gair-prox/open-web-math-pro
  - RUC-AIBOX/long_form_thought_data_5k
  - internlm/Lean-Workbook
  - internlm/Lean-Github
  - deepseek-ai/DeepSeek-Prover-V1
  - ScalableMath/Lean-STaR-base
  - ScalableMath/Lean-STaR-plus
  - ScalableMath/Lean-CoT-base
  - ScalableMath/Lean-CoT-plus
  - opencsg/chinese-fineweb-edu
  - liwu/MNBVC
  - vikp/textbook_quality_programming
  - HuggingFaceTB/smollm-corpus
  - OpenCoder-LLM/opc-annealing-corpus
  - OpenCoder-LLM/opc-sft-stage1
  - OpenCoder-LLM/opc-sft-stage2
  - XinyaoHu/AMPS_mathematica
  - deepmind/math_dataset
  - mrfakename/basic-math-10m
  - microsoft/orca-math-word-problems-200k
  - AI-MO/NuminaMath-CoT
  - HuggingFaceTB/cosmopedia
  - MU-NLPC/Calc-ape210k
  - manu/project_gutenberg
  - storytracer/LoC-PD-Books
  - allenai/dolma
language:
  - en
  - zh
tags:
  - code
  - math
arxiv: 2412.17743
model-index:
  - name: YuLan-Mini
    results:
      - task:
          type: text-generation
        dataset:
          type: openai_humaneval
          name: HumanEval
        metrics:
          - name: pass@1
            type: pass@1
            value: 0.64
            verified: false
      - task:
          type: text-generation
        dataset:
          type: mbpp
          name: MBPP
        metrics:
          - name: pass@1
            type: pass@1
            value: 0.659
            verified: false
      - task:
          type: text-generation
        dataset:
          type: math-500
          name: MATH-500
        metrics:
          - name: maj@1
            type: maj@1
            value: 0.378
            verified: false
      - task:
          type: text-generation
        dataset:
          type: gsm8k
          name: GSM8K
        metrics:
          - name: maj@1
            type: maj@1
            value: 0.684
            verified: false

Important Notice: This is a pre-trained base model without instruction-following capabilities. The SFT version will be released within a few weeks.

YuLan-Mini: An Open Data-efficient Language Model

license Static Badge

YuLan-Mini is a lightweight language model with 2.4 billion parameters. It achieves performance comparable to industry-leading models trained on significantly more data, despite being pre-trained on only 1.08T tokens. The model excels particularly in the domains of mathematics and code. To facilitate reproducibility, we will open-source the relevant pre-training resources.

Model Downloads 🔗

Model Context Length SFT
YuLan-Mini (Recommended) 28K ❎
YuLan-Mini-2.4B-4K 4K ❎
YuLan-Mini-Instruct Comming soon ✅

Features 🌟

Our pre-training methodology improves training efficiency through three key innovations:

  1. an elaborately designed data pipeline that combines data cleaning with data schedule strategies;
  2. a systematic optimization method that can effectively mitigate training instability;
  3. an effective annealing approach that integrate targeted data selection and long context training.

Behchmarks 🌟

Models Model Size # Train Tokens Context Length MATH 500 GSM 8K Human Eval MBPP RACE Middle RACE High RULER
MiniCPM 2.6B 1.06T 4K 15.00 53.83 50.00* 47.31 56.61 44.27 N/A
Qwen-2 1.5B 7T 128K 22.60 46.90* 34.80* 46.90* 55.77 43.69 60.16
Qwen2.5 0.5B 18T 128K 23.60 41.60* 30.50* 39.30* 52.36 40.31 49.23
Qwen2.5 1.5B 18T 128K 45.40 68.50* 37.20* 60.20* 58.77 44.33 68.26
Gemma2 2.6B 2T 8K 18.30* 30.30* 19.50* 42.10* - - N/A
StableLM2 1.7B 2T 4K - 20.62 8.50* 17.50 56.33 45.06 N/A
SmolLM2 1.7B 11T 8K 11.80 - 23.35 45.00 55.77 43.06 N/A
Llama3.2 3.2B 9T 128K 7.40 - 29.30 49.70 55.29 43.34 77.06
YuLan-Mini 2.4B 1.04T 4K 32.60 66.65 61.60 66.70 55.71 43.58 N/A
YuLan-Mini 2.4B 1.08T 28K 37.80 68.46 64.00 65.90 57.18 44.57 51.48
Models LAMBADA MMLU CMMLU CEval HellaSwag WinoGrande StoryCloze ARC-e ARC-c
MiniCPM-2.6B 61.91 53.37 48.97 48.24 67.92 65.74 78.51 55.51 43.86
Qwen2-1.5B 64.68 55.90 70.76 71.94 66.11 66.14 77.60 62.21 42.92
Qwen2.5-0.5B 52.00 47.50 52.17 54.27 50.54 55.88 71.67 56.10 39.51
Qwen2.5-1.5B 62.12 60.71 67.82 69.05 67.18 64.48 76.80 71.51 53.41
Gemma2-2.6B - 52.20* - 28.00* 74.60* 71.50* - - 55.70*
StableLM2-1.7B 66.15 40.37 29.29 26.99 69.79 64.64 78.56 54.00 40.78
SmolLM2-1.7B 67.42 51.91 33.46 35.10 72.96 67.40 79.32 44.82 35.49
Llama3.2-3B 69.08 63.40 44.44 44.49 75.62 67.48 76.80 70.12 48.81
YuLan-Mini 64.72 51.79 48.35 51.47 68.65 67.09 76.37 69.87 50.51
YuLan-Mini 65.67 49.10 45.45 48.23 67.22 67.24 75.89 67.47 49.32

Pre-Training Resources 🔧

To enhance research transparency and reproducibility, we are open-sourcing relevant pre-training resources:

1. Pre-training and Evaluation Code

The pre-training and evaluation code will be released in a future update.

2. Intermediate Stage Checkpoints The intermediate stage checkpoints are released in YuLan-Mini.
Stage Curriculum Phase 4K Context 28K Context Optimizer Inference Architecture LAMBADA Acc GSM8K Acc HumanEval pass@1
Stable 5 YuLan-Mini-Phase5 yulanmini 53.85 3.41 12.26
Stable 10 YuLan-Mini-Phase10 yulanmini 55.00 9.57 15.95
Stable 15 YuLan-Mini-Phase15 yulanmini 55.81 13.81 16.99
Stable 20 YuLan-Mini-Phase20 ✅ yulanmini 55.81 21.39 20.79
Stable 25 (1T tokens) YuLan-Mini-Before-Annealing ✅ yulanmini 55.67 29.94 34.06
Annealing 26 YuLan-Mini-4K llama* 64.72 66.65 61.60
Annealing 27 YuLan-Mini llama* 65.67 68.46 64.00

*: For easier inference and deployment, we merged the re-parameterized added parameters and scaling factors into the final released models (YuLan-Mini and YuLan-Mini-Intermediate-4K), enabling it to run on the Llama architecture. However, these parameters are still retained in the intermediate checkpoints from the training process.

3. Optimizer States Before Annealing

YuLan-Mini-Before-Annealing

4. The Used Open-Source Datasets

Used-Datasets-List

5. Data Distribution for every phase
6. Synthetic Data

Data cleaning and synthesis pipeline:

The synthetic data we are using is released in YuLan-Mini-Datasets

What you can do with these pre-training resources

  1. Pre-train your own LLM. You can use our data and curriculum to train a model that's just as powerful as YuLan-Mini.
  2. Perform your own learning rate annealing. During the annealing phase, YuLan-Mini's learning ability is at its peak. You can resume training from the checkpoint before annealing and use your own dataset for learning rate annealing.
  3. Fine-tune the Instruct version of the LLM. You can use the YuLan-Mini base model to train your own Instruct version.
  4. Training dynamics research. You can use YuLan-Mini's intermediate checkpoints to explore internal changes during the pre-training process.
  5. Synthesize your own data. You can use YuLan-Mini's data pipeline to clean and generate your own dataset.

Quick Start 💻

Below is a simple example for inference using Huggingface:

Huggingface Inference Example

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("yulan-team/YuLan-Mini")
model = AutoModelForCausalLM.from_pretrained("yulan-team/YuLan-Mini", torch_dtype=torch.bfloat16)

# Input text
input_text = "Renmin University of China is"
inputs = tokenizer(input_text, return_tensors="pt")

# Completion
output = model.generate(inputs["input_ids"], max_new_tokens=100)
print(tokenizer.decode(output[0], skip_special_tokens=True))

vLLM Serve Example

vllm serve yulan-team/YuLan-Mini --dtype bfloat16

SGLang Serve Example

python -m sglang.launch_server --model-path yulan-team/YuLan-Mini --port 30000 --host 0.0.0.0

The Team

YuLan-Mini is developed and maintained by AI Box, Renmin University of China.

License

  • The code in this repository is released under the MIT License.
  • Policies regarding the use of model weights, intermediate optimizer states, and training data will be announced in future updates.
  • Limitations: Despite our efforts to mitigate safety concerns and encourage the generation of ethical and lawful text, the probabilistic nature of language models may still lead to unexpected outputs. For instance, responses might contain bias, discrimination, or other harmful content. Please refrain from disseminating such content. We are not liable for any consequences arising from the spread of harmful information.

Citation

If you find YuLan-Mini helpful for your research or development, please cite our technical report:

@misc{hu2024yulanmini,
      title={YuLan-Mini: An Open Data-efficient Language Model}, 
      author={Yiwen Hu and Huatong Song and Jia Deng and Jiapeng Wang and Jie Chen and Kun Zhou and Yutao Zhu and Jinhao Jiang and Zican Dong and Wayne Xin Zhao and Ji-Rong Wen},
      year={2024},
      eprint={2412.17743},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.17743}, 
}