Quantization made by Richard Erkhov.
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 BitNet b1.58 paper. The models are trained with RedPajama dataset for 100B tokens. The hypers, as well as two-stage LR and weight decay, are implemented as suggested in their following paper. All models are open-source in the repo. 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