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MiniMA-2-3B

πŸ“‘ arXiv | πŸ‘» GitHub | πŸ€— HuggingFace-MiniMA | πŸ€— HuggingFace-MiniChat | πŸ€– ModelScope-MiniMA | πŸ€– ModelScope-MiniChat | πŸ€— HuggingFace-MiniChat-1.5 | πŸ€— HuggingFace-MiniMA-2 | πŸ€— HuggingFace-MiniChat-2

πŸ†• Updates from MiniMA-3B:

  • continued from MiniMA-3B without distillation;
  • better data mixture;
  • more trained tokens.

❗ Must comply with LICENSE of LLaMA-2 since it is derived from LLaMA-2.

A language model continued from MiniMA-3B.

Completing the compute-performance pareto frontier together with MiniMA-3B and other arts.

teaser_a

Standard Benchmarks

Method TFLOPs MMLU (5-shot) CEval (5-shot) DROP (3-shot) HumanEval (0-shot) BBH (3-shot) GSM8K (8-shot)
Mamba-2.8B 4.6E9 25.58 24.74 15.72 7.32 29.37 3.49
ShearedLLaMA-2.7B 0.8E9 26.97 22.88 19.98 4.88 30.48 3.56
BTLM-3B 11.3E9 27.20 26.00 17.84 10.98 30.87 4.55
StableLM-3B 72.0E9 44.75 31.05 22.35 15.85 32.59 10.99
Qwen-1.8B 23.8E9 44.05 54.75 12.97 14.02 30.80 22.97
Phi-2-2.8B 159.9E9 56.74 34.03 30.74 46.95 44.13 55.42
LLaMA-2-7B 84.0E9 46.00 34.40 31.57 12.80 32.02 14.10
MiniMA-3B 4.0E9 28.51 28.23 22.50 10.98 31.61 8.11
MiniChat-3B 4.0E9 38.40 36.48 22.58 18.29 31.36 29.72
MiniMA-2-3B 13.4E9 40.14 44.65 23.10 14.63 31.43 8.87
MiniChat-2-3B 13.4E9 46.17 43.91 30.26 22.56 34.95 38.13

The following is an example code snippet to use MiniMA-2-3B:

import torch

from transformers import AutoModelForCausalLM, AutoTokenizer

# MiniMA
tokenizer = AutoTokenizer.from_pretrained("GeneZC/MiniMA-2-3B", use_fast=False)
# GPU.
model = AutoModelForCausalLM.from_pretrained("GeneZC/MiniMA-2-3B", use_cache=True, device_map="auto", torch_dtype=torch.float16).eval()
# CPU.
# model = AutoModelForCausalLM.from_pretrained("GeneZC/MiniMA-2-3B", use_cache=True, device_map="cpu", torch_dtype=torch.float16).eval()

prompt = "Question: Sherrie tells the truth. Vernell says Sherrie tells the truth. Alexis says Vernell lies. Michaela says Alexis tells the truth. Elanor says Michaela tells the truth. Does Elanor tell the truth?\nAnswer: No\n\nQuestion: Kristian lies. Sherrie says Kristian lies. Delbert says Sherrie lies. Jerry says Delbert tells the truth. Shalonda says Jerry tells the truth. Does Shalonda tell the truth?\nAnswer: No\n\nQuestion: Vina tells the truth. Helene says Vina lies. Kandi says Helene tells the truth. Jamey says Kandi lies. Ka says Jamey lies. Does Ka tell the truth?\nAnswer: No\n\nQuestion: Christie tells the truth. Ka says Christie tells the truth. Delbert says Ka lies. Leda says Delbert tells the truth. Lorine says Leda tells the truth. Does Lorine tell the truth?\nAnswer:"
input_ids = tokenizer([prompt]).input_ids
output_ids = model.generate(
    torch.as_tensor(input_ids).cuda(),
    do_sample=True,
    temperature=0.7,
    max_new_tokens=1024,
)
output_ids = output_ids[0][len(input_ids[0]):]
output = tokenizer.decode(output_ids, skip_special_tokens=True).strip()
# output: "No"

Bibtex

@article{zhang2023law,
    title={Towards the Law of Capacity Gap in Distilling Language Models},
    author={Zhang, Chen and Song, Dawei and Ye, Zheyu and Gao, Yan},
    year={2023},
    url={https://arxiv.org/abs/2311.07052}
}

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric Value
Avg. 44.75
AI2 Reasoning Challenge (25-Shot) 44.71
HellaSwag (10-Shot) 69.33
MMLU (5-Shot) 41.22
TruthfulQA (0-shot) 38.44
Winogrande (5-shot) 66.69
GSM8k (5-shot) 8.11
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Datasets used to train GeneZC/MiniMA-2-3B

Evaluation results