1bitLLM_-_bitnet_b1_58-large-exl2

RichardErkhov/1bitLLM_-_bitnet_b1_58-large-exl2

1bitLLM_-_bitnet_b1_58-large-exl2 is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.

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Updated Jan 18, 2025

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929 MB

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At a glance

Access: Public
Created: Jan 18, 2025
Updated: Jan 18, 2025
SHA: 40d07c84

Model card

From the Hugging Face model README

Quantization made by Richard Erkhov.

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bitnet_b1_58-large - EXL2

Model creator: https://huggingface.co/1bitLLM/
Original model: https://huggingface.co/1bitLLM/bitnet_b1_58-large/

Available sizes

| Branch | Bits | Description | | ----- | ---- | ------- | ------ | ------ | ------ | ------ | ------------ | | 8_0 | 8.0 | Maximum quality that ExLlamaV2 can produce, near unquantized performance. | | 6_5 | 6.5 | Very similar to 8.0, good tradeoff of size vs performance, recommended. | | 5_0 | 5.0 | Slightly lower quality vs 6.5, but usable on 8GB cards. | | 4_25 | 4.25 | GPTQ equivalent bits per weight, slightly higher quality. | | 3_5 | 3.5 | Lower quality, only use if you have to. |

Download instructions

With git:

git clone --single-branch --branch 6_5 https://huggingface.co/1bitLLM_-_bitnet_b1_58-large-exl2 bitnet_b1_58-large-6_5

With huggingface hub:

pip3 install huggingface-hub

To download a specific branch, use the --revision parameter. For example, to download the 6.5 bpw branch: Linux:

huggingface-cli download 1bitLLM_-_bitnet_b1_58-large-exl2 --revision 6_5 --local-dir bitnet_b1_58-large-6_5 --local-dir-use-symlinks False

Windows (which apparently doesn't like _ in folders sometimes?):

huggingface-cli download 1bitLLM_-_bitnet_b1_58-large-exl2 --revision 6_5 --local-dir bitnet_b1_58-large-6.5 --local-dir-use-symlinks False

Original model description:

license: mit

This is a reproduction of the <a href="https://arxiv.org/abs/2402.17764"> BitNet b1.58</a> paper. The models are trained with <a href="https://github.com/togethercomputer/RedPajama-Data">RedPajama dataset</a> for 100B tokens. The hypers, as well as two-stage LR and weight decay, are implemented as suggested in their following <a href="https://github.com/microsoft/unilm/blob/master/bitnet/The-Era-of-1-bit-LLMs__Training_Tips_Code_FAQ.pdf">paper</a>. All models are open-source in the <a href="https://huggingface.co/1bitLLM">repo</a>. We will train larger models and/or more tokens when resource is available.

Results

PPL and zero-shot accuracy:

Models	PPL	ARCe	ARCc	HS	BQ	OQ	PQ	WGe	Avg
FP16 700M (reported)	12.33	54.7	23.0	37.0	60.0	20.2	68.9	54.8	45.5
BitNet b1.58 700M (reported)	12.87	51.8	21.4	35.1	58.2	20.0	68.1	55.2	44.3
BitNet b1.58 700M (reproduced)	12.78	51.4	21.8	35.0	59.6	20.6	67.5	55.4	44.5
FP16 1.3B (reported)	11.25	56.9	23.5	38.5	59.1	21.6	70.0	53.9	46.2
BitNet b1.58 1.3B (reported)	11.29	54.9	24.2	37.7	56.7	19.6	68.8	55.8	45.4
BitNet b1.58 1.3B (reproduced)	11.19	55.8	23.7	37.6	59.0	20.2	69.2	56.0	45.9
FP16 3B (reported)	10.04	62.1	25.6	43.3	61.8	24.6	72.1	58.2	49.7
BitNet b1.58 3B (reported)	9.91	61.4	28.3	42.9	61.5	26.6	71.5	59.3	50.2
BitNet b1.58 3B (reproduced)	9.88	60.9	28.0	42.3	58.3	26.0	71.4	60.3	49.6

The differences between the reported numbers and the reproduced results are possibly variances from the training data processing, seeds, or other random factors.

Evaluation

The evaluation pipelines are from the paper authors. Here is the commands to run the evaluation:

pip install lm-eval==0.3.0

python eval_ppl.py --hf_path 1bitLLM/bitnet_b1_58-3B --seqlen 2048

python eval_task.py --hf_path 1bitLLM/bitnet_b1_58-3B \
    --batch_size 1 \
    --tasks \
    --output_path result.json \
    --num_fewshot 0 \
    --ctx_size 2048

Created: Jan 18, 2025
Updated: Jan 18, 2025