🪄 Towards the Law of Capacity Gap in Distilling Language Models

Logo for MiniMA designed with DALL·E

Language model (LM) distillation is a trending area that aims to distil the knowledge resided in a large teacher LM to a small student one. While various methods have been proposed to push the distillation to its limits, it is still a pain distilling LMs when a large capacity gap is exhibited between the teacher and the student LMs. The pain is mainly resulted by the curse of capacity gap, which describes that a larger teacher LM cannot always lead to a better student LM than one distilled from a smaller teacher LM due to the affect of capacity gap increment. That is, there is likely an optimal point yielding the best student LM along the scaling course of the teacher LM. Even worse, the curse of capacity gap can be only partly yet not fully lifted as indicated in previous studies.

However, the tale is not ever one-sided. Although a larger teacher LM has better performance than a smaller teacher LM, it is much more resource-demanding especially in the context of recent large LMs (LLMs). Consequently, instead of sticking to lifting the curse, leaving the curse as is should be arguably fine. Even better, in this paper, we reveal that the optimal capacity gap is almost consistent across different student scales and architectures, fortunately turning the curse into the law of capacity gap. The law later guides us to distil a 3B student LM (termed MiniMA) from a 7B teacher LM (adapted LLaMA2-7B). MiniMA is demonstrated to yield a new compute-performance pareto frontier among existing 3B LMs on commonly used benchmarks, and its instruction-tuned version (termed MiniChat) outperforms a wide range of 3B competitors in GPT4 evaluation and could even compete with several 7B chat models.

🕰️ Updates

[2024/7/9] MiniMA-2-1B, MiniLoong-3B, and MiniMix-2/4x3B are landed. MiniMA-2-1B is the considered minimal language model in our plan, and is distilled from MiniMA-2-3B. MiniLoong-3B is trained by extending the context length of MiniMA-2-3B from 4K to 32K. And MiniMix-2/4x3B is the previously highlighted MoE language model upcycled from MiniMA-2-3B. Please refer to detailed results in later content.

[2024/2/17] Going through only 15% tokens out of all, MiniMix-2/4x3B (or MiniMA-MoE-2/4x3) upcycled from MiniMA-2 has completely overtaken MiniMA-2 on concerned tasks and are approximating considered baselines (e.g., DeepSeek-MoE-16B) with a full parameter amount of only 7B.

[2024/1/31] We have to frustratingly announce that we have found a critical bug concerning about expert load balancing in training MiniMA-MoE. Therefore, we must revert to the very begining of training, pity : <

[2024/1/3] MT-Bench-ZH is released to serve as a Chinese counterpart of multi-turn instruction-following evaluaion (e.g., MT-Bench). MT-Bench-ZH is essentially translated from MT-Bench by GPT-4 and further checked by human. On the MT-Bench-ZH, MiniChat-2-3B surpasses Qwen-Chat-1.8B and approximates Qwen-Chat-7B. And it indicates MiniChat-2-3B has a better bilingual ability than others (e.g., Phi-2, StableLM-Zephyr-3B, etc.).

[2023/12/29] MiniMA-MoE is kicked off, aiming at upcycling MiniMA-2-3B to an MoE model. Following the Pyramid-MoE design, MiniMA-MoE has the bottom 25% MLPs with 2 experts while the top 75% MLPs with 4 experts, reaching ~7B parameters in total. Top-1 gating is used to preserve 3B activated parameters per token. The progress would be updated in a biweekly schedule (totally 6 biweeks as estimated) and could be tracked in Live. We later decide to name it as MiniMix to show our respect to Mixtral.

[2023/12/27] Performance results are added for potential references in Performance. Still room for improvement, bravo!

Outdated updates

[2023/12/27] MiniMA-2-3B and MiniChat-2-3B are successfully landed. MiniMA-2-3B is continued from MiniMA-3B with a more diverse data sources in Chinese, and MiniChat-2-3B are finetuned and optimized on a range of instruction and preference data. MiniMA-2-3B together with MiniMA-3B and other arts completes the new compute-performance pareto frontier, and MiniChat-2-3B achieves a score of 6.23 on MT-Bench, surpassing Vicuna-7B and approximating LLaMA-2-Chat-7B.

[2023/11/27] MiniChat-1.5-3B (MiniChat-3B enhanced with NEFTune and DPO) achieves a win rate of 78.55 on AlpacaEval Leaderboard : ) Keep going! However, we have found a mistake in our MiniChat-3B submission. And the mistake should be fixed. MiniChat-3B instead achieves a win rate of 48.82 and can surpass ChatGLM2 but not Davinci-003.

[2023/11/16] MiniChat-3B achieves a win rate of ~~76.94~~ 48.82 on AlpacaEval Leaderboard, ~~surpassing Vicuna-7B v1.3 and~~ leaving large room for improvement.

[2023/11/15] Wait, what!? MiniMA-2 is on the way! Just stay tuned ; ) The release is scheduled at the begining of year 2024 and hopefully it could be timely.

[2023/11/14] For models <= 3B on Open LLM Leaderboard, MiniMA-3B surpasses ShearedLLaMA-2.7B and ranks #14, and MiniChat-3B (potentially with unidentified data contamination) surpasses Phi1.5 and ranks #2.

[2023/11/14] We have released the paper, uploaded the checkpoints, and updated the codebase.

📺 Live

MiniMA-MoE-2/4x3B (a.k.a. MiniMix)

Training Losses

Evaluation Metrics

	TFLOPs	MMLU (5-shot)	CEval (5-shot)	DROP (3-shot)	HumanEval (0-shot)	BBH (3-shot)	GSM8K (8-shot)
LLaMA-MoE-v1-3.5B-4_16	84.0+4.2E9	26.69	24.67	23.40	6.10	30.26	5.38
JetMoE-8B	16.5E9	48.24	36.03	30.63	24.39	33.86	40.71
DeepSeek-MoE-16B-base	33.6E9	44.64	39.90	29.74	26.83	31.95	17.06
MiniCPM-MoE-8x2B	14.4+24.0E9	55.38	54.01	31.39	47.56	37.15	54.81
Qwen1.5-MoE-A2.7B	23.8+35.7E9	59.56	79.27	32.87	33.54	39.12	65.13
Phixtral-2x2.8B	159.9E9	53.91	32.24	32.28	47.56	44.29	58.53
Mixtral-8x7B-v0.1	336.0E9	70.50	53.94	57.74	32.32	48.79	59.59

MiniMA-2-3B	13.4E9	40.14	44.65	23.10	14.63	31.43	8.87
MiniMix-2/4x3B-Kickoff	+0.1E9	34.30	33.51	21.34	10.37	30.35	6.60
MiniMix-2/4x3B-Biweek1	+2.0E9	41.17	41.75	27.24	15.85	32.17	14.25
MiniMix-2/4x3B-Biweek2	+4.0E9	41.35	40.79	28.62	18.90	31.66	13.34
MiniMix-2/4x3B-Biweek3	+6.0E9	41.58	44.21	30.68	17.68	32.91	16.07
MiniMix-2/4x3B-Biweek4	+8.0E9	43.14	44.21	31.33	18.29	32.61	16.98
MiniMix-2/4x3B-Biweek5	+10.0E9	43.75	45.10	32.47	18.29	33.71	21.99
MiniMix-2/4x3B-Biweek6	+12.0E9	44.57	45.39	33.35	20.12	33.43	22.52
MiniMix-2/4x3B-Release	+12.0E9	44.35	45.77	33.78	18.29	33.60	21.61

📊 Performance

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
MiniMA-2-1B	6.3E9	31.34	34.92	20.08	10.37	31.16	7.28
MiniMA-2-3B	13.4E9	40.14	44.65	23.10	14.63	31.43	8.87
MiniMix-2/4x3B	25.4E9	44.35	45.77	33.78	18.29	33.60	21.61
MiniChat-3B	4.0E9	38.40	36.48	22.58	18.29	31.36	29.72
MiniChat-2-3B	13.4E9	46.17	43.91	30.26	22.56	34.95	38.13

Instruction-following Benchmarks

Method	AlpacaEval	MT-Bench	MT-Bench-ZH
GPT-4	95.28	9.18	8.96
Zephyr-7B-Beta	90.60	7.34	6.27^#
Vicuna-7B	76.84	6.17	5.22^#
LLaMA-2-Chat-7B	71.37	6.27	5.43^#
Qwen-Chat-7B	-	-	6.24
Phi-2-DPO	81.37	-	1.59^#^$
StableLM-Zephyr-3B	76.00	6.64	4.31^#
Rocket-3B	79.75	6.56	4.07^#
Qwen-Chat-1.8B	-	-	5.65

MiniChat-3B	48.82	-	-
MiniChat-2-3B	77.30	6.23	6.04

^# specialized mainly for English.

^$ finetuned without multi-turn instruction data.

✨ Quick Start

Since MiniMA and MiniChat follow architecture of LLaMA, they can be seamlessly used in HuggingFace Transformers with minimal requirements.

The following is an example code snippet to use MiniMA and MiniChat:

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"

from conversation import get_default_conv_template

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

conv = get_default_conv_template("minichat")

question = "Implement a program to find the common elements in two arrays without using any extra data structures."
conv.append_message(conv.roles[0], question)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
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: "def common_elements(arr1, arr2):\n    if len(arr1) == 0:\n        return []\n    if len(arr2) == 0:\n        return arr1\n\n    common_elements = []\n    for element in arr1:\n        if element in arr2:\n            common_elements.append(element)\n\n    return common_elements"
# Multiturn conversation could be realized by continuously appending questions to `conv`.

📖 Tutorials

The reproductions of MiniMA and MiniChat follow several stages including adaptation of LLaMA2, distillation to MiniMA, and finetuning to MiniChat.

Detailed tutorials can be found here.

🔮 Future Work

Integrated with vision, e.g., MiniChat-V.

🤔️ Bugs or Questions?

If you have any questions related to the code or the paper, feel free to email Chen (chenzhang9702@outlook.com). If you encounter any problems when using the code, or want to report a bug, you can open an issue. Please try to specify the problem with details so we can help you better and quicker!

✍🏻 Citation

Please cite our paper if you find the repo helpful in your work:

@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}
}

License

GeneZC/MiniMA

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