Exllama v2 Quantizations of IceCaffeLatteRP-7b
Using turboderp's ExLlamaV2 v0.1.4 for quantization.
The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Original model: https://huggingface.co/icefog72/IceCaffeLatteRP-7b
Prompt format
No chat template specified so default is used. This may be incorrect, check original model card for details.
<s> [INST] <<SYS>>
{system_prompt}
<</SYS>>
{prompt} [/INST] </s>
Available sizes
Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | VRAM (32k) | Description |
---|---|---|---|---|---|---|
8_0 | 8.0 | 8.0 | 8.4 GB | 9.8 GB | 11.8 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
6_5 | 6.5 | 8.0 | 7.2 GB | 8.6 GB | 10.6 GB | Very similar to 8.0, good tradeoff of size vs performance, recommended. |
5_0 | 5.0 | 6.0 | 6.0 GB | 7.4 GB | 9.4 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. |
4_25 | 4.25 | 6.0 | 5.3 GB | 6.7 GB | 8.7 GB | GPTQ equivalent bits per weight, slightly higher quality. |
3_5 | 3.5 | 6.0 | 4.7 GB | 6.1 GB | 8.1 GB | Lower quality, only use if you have to. |
Download instructions
With git:
git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/IceCaffeLatteRP-7b-exl2 IceCaffeLatteRP-7b-exl2-6_5
With huggingface hub (credit to TheBloke for instructions):
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 bartowski/IceCaffeLatteRP-7b-exl2 --revision 6_5 --local-dir IceCaffeLatteRP-7b-exl2-6_5
Windows (which apparently doesn't like _ in folders sometimes?):
huggingface-cli download bartowski/IceCaffeLatteRP-7b-exl2 --revision 6_5 --local-dir IceCaffeLatteRP-7b-exl2-6.5
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard69.030
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard86.700
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard64.340
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard61.160
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard81.290
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard64.060