Bamboo

We introduce Bamboo-v0.1, a new 7B LLM that boasts high sparsity while delivering performance equivalent to Mistral-7B. This repo provides the details of the model.

Models

Model	Transformers(HF)	PowerInfer/llama.cpp(GGUF)
Bamboo-7B-base-v0.1	Bamboo-base-v0.1	Bamboo-base-v0.1-gguf
Bamboo-7B-DPO-v0.1	Bamboo-DPO-v0.1	Bamboo-DPO-v0.1-gguf

Performance with different sparsity

Recent studies (Zhang et al., 2024) have shown that the activation sparsity exists in LLMs by only keeping the top-k activation neurons in each layer. In this subsection, we show the performance of Bamboo with different sparsity with activation magnitude thresholding to select neurons. We evaluate the perplexity on wikitext-2-raw-v1.

Top-k Neurons	PPL
100%	6.484
20%	6.484
15%	6.485
12%	6.497
10%	6.524

CDF of neurons distribution

Here we report the CDF of neurons distribution of Bamboo-7B-base-v0.1 for every FFN layer. We profile neurons' activation with cosmopedia dataset for around 1M tokens.

Model Performance

Our evaluation is based on the framework lm-evaluation-harness. The evaluation details are listed as follows:

• Huggingface LLM Leaderboard tasks.
• Other Popular Benchmarks: We report the average accuracies on Big Bench Hard (BBH) (3-shot), HumanEval.

	Average	MMLU	Winogrande	TruthfulQA	Hellaswag	GSM8K	Arc-C	HumanEval	BBH
Bamboo	57.1	63.89	76.16	44.06	82.17	52.84	62.20	25.6	50.35
Mistral-v0.1	56.5	62.65	79.24	42.62	83.32	40.18	61.43	26.21	56.35

Inference Efficiency

bamboo-live-demo.mp4

_{Both PowerInfer and llama.cpp fully utilized the same hardware of Intel Core i7-13700 (8 threads) and Nvidia RTX 2080Ti (11GB).}

Below is a detailed comparison of inference speeds (tokens/second) achieved on Bamboo-7B-base with PowerInfer and llama.cpp across various hardware configurations.

Scenario	Hardware	with PowerInfer	with llama.cpp	Speedup
CPU+GPU Hybird	Core i7-13700(8T) + RTX 2080Ti(11GB)	33.50	7.64	4.38x
Full GPU	RTX 4090(24GB)	92.46	58.34	1.58x
Full CPU	Core i9-13900K(8T)	9.94	4.78	2.08x

Contamination Results

Here we report our contamination results using https://github.com/fblgit/detect-pretrain-code-contamination/tree/winofix. We use llama-2-7b as reference model. When the result is greater than 0.85, it is highly likely that the dataset has been trained.

Model	TruthfulQA	Winogrande	ARC	MMLU	Hellaswag	GSM8K
Bamboo	0.22	0.02	0.08	0.24	0.02	0.99
Mistral-v0.1	0.45	0.03	0.08	0.24	0.04	0.91

Note that GSM8K often scores very highly on this toolkit, according to https://huggingface.co/spaces/Yeyito/llm_contamination_detector

Limitations

• Bamboo, having undergone training with only 200B tokens, may still exhibit performance gaps in certain tasks.
• The Bamboo model has only been trained on English-language datasets, hence its capabilities in other languages are still lacking.
• The model may produce unexpected outputs due to its small size and probabilistic generation paradigm.

Future Work

• Mixtral 8x7B level sparse activation model
• Better base and chat-7B model

License

The code is licensed under Apache-2.0, while model weights are fully open for academic research and also allow free commercial usage.

Citation

Please kindly cite using the following BibTeX:

@misc{bamboo,
    title={Bamboo: Harmonizing Sparsity and Performance in Large Language Models}, 
    author={Yixin Song, Haotong Xie, Zeyu Mi, Li Ma, Haibo Chen},
    year={2024}
}