Exllama v2 Quantizations of starcoder2-15b
Using turboderp's ExLlamaV2 v0.0.15 preview 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/bigcode/starcoder2-15b
Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | VRAM (32k) | Description |
---|---|---|---|---|---|---|
8_0 | 8.0 | 8.0 | 16.6 GB | 17.5 GB | 18.8 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
6_5 | 6.5 | 8.0 | 13.9 GB | 14.9 GB | 16.2 GB | Near unquantized performance at vastly reduced size, recommended. |
5_0 | 5.0 | 6.0 | 11.2 GB | 12.2 GB | 13.5 GB | Slightly lower quality vs 6.5. |
4_25 | 4.25 | 6.0 | 9.8 GB | 10.7 GB | 12.0 GB | GPTQ equivalent bits per weight. |
3_5 | 3.5 | 6.0 | 8.4 GB | 9.3 GB | 10.6 GB | Lower quality, not recommended. |
Download instructions
With git:
git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/starcoder2-15b-exl2
With huggingface hub (credit to TheBloke for instructions):
pip3 install huggingface-hub
To download the main
(only useful if you only care about measurement.json) branch to a folder called starcoder2-15b-exl2
:
mkdir starcoder2-15b-exl2
huggingface-cli download bartowski/starcoder2-15b-exl2 --local-dir starcoder2-15b-exl2 --local-dir-use-symlinks False
To download from a different branch, add the --revision
parameter:
Linux:
mkdir starcoder2-15b-exl2-6_5
huggingface-cli download bartowski/starcoder2-15b-exl2 --revision 6_5 --local-dir starcoder2-15b-exl2-6_5 --local-dir-use-symlinks False
Windows (which apparently doesn't like _ in folders sometimes?):
mkdir starcoder2-15b-exl2-6.5
huggingface-cli download bartowski/starcoder2-15b-exl2 --revision 6_5 --local-dir starcoder2-15b-exl2-6.5 --local-dir-use-symlinks False
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Dataset used to train bartowski/starcoder2-15b-exl2
Evaluation results
- pass@1 on CruxEval-Iself-reported48.100
- pass@1 on DS-1000self-reported33.800
- accuracy on GSM8K (PAL)self-reported65.100
- pass@1 on HumanEval+self-reported37.800
- pass@1 on HumanEvalself-reported46.300
- edit-smiliarity on RepoBench-v1.1self-reported74.080