MiniMA-2-3B

🆕 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.

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