Not-For-All-Audiences
| license: cc-by-nc-4.0 | |
| tags: | |
| - not-for-all-audiences | |
| ``` | |
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| PROUDLY PRESENTS | |
| ``` | |
| # L3.1-8B-Llamoutcast-exl2-longcal | |
| Quantized using 115 rows of 8192 tokens from the default ExLlamav2-calibration dataset. | |
| Branches: | |
| - `main` -- `measurement.json` | |
| - `8b8h` -- 8bpw, 8bit lm_head | |
| - `6b8h` -- 6bpw, 8bit lm_head | |
| - `4b6h` -- 4bpw, 6bit lm_head | |
| - `2.25b6h` -- 2.25bpw, 6bit lm_head | |
| Original model link: [Envoid/L3.1-8B-Llamoutcast](https://huggingface.co/Envoid/L3.1-8B-Llamoutcast) | |
| ### Quanter's notes | |
| As apparently the default dataset is supposed to be better in nearly all situations, I decided to start quanting using that in addition to my standard rpcal-fare. I'd appreciate real-world tests to confirm the hypothesis, though, so please leave a comment if you find this mode of quanting better than rpcal. | |
| Original model README below. | |
| ----- | |
|  | |
| # Warning: this model is utterly cursed. | |
| ## Llamoutcast | |
| This model was originally intended to be a DADA finetune of Llama-3.1-8B-Instruct but the results were unsatisfactory. So it received some additional finetuning on a rawtext dataset and now it is utterly cursed. | |
| It responds to Llama-3 Instruct formatting. | |
| Trained using [qlora-pipe](https://github.com/tdrussell/qlora-pipe). |