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--- |
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license: apache-2.0 |
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library_name: transformers |
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base_model: |
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- EVA-UNIT-01/EVA-Qwen2.5-32B-v0.2 |
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datasets: |
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- jondurbin/gutenberg-dpo-v0.1 |
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- nbeerbower/gutenberg2-dpo |
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- nbeerbower/gutenberg-moderne-dpo |
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--- |
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# Quantization |
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Quantized using the default exllamav2 quantization script/dataset, with the following changes: |
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- Context length for the calibration/quantization phases were both forced to 8192, as the script does not respect CLI changes by default and simply uses 512/2048 as context lengths. |
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- Fewer rows, but ultimately, much more data was used. |
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- A few rows of an "extra" dataset, with some examples of long, coherent text and this model's chat tokens, were added to the dataset. |
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The goal is less degredation from quantization at long context. But I tried to stay as close to default exl2 quantization parameters as possible, as straying too far from them only seems to degrade performance. |
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# EVA-Gutenberg3-Qwen2.5-32B |
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[EVA-UNIT-01/EVA-Qwen2.5-32B-v0.2](https://huggingface.co/EVA-UNIT-01/EVA-Qwen2.5-32B-v0.2) finetuned on [jondurbin/gutenberg-dpo-v0.1](https://huggingface.co/datasets/jondurbin/gutenberg-dpo-v0.1), [nbeerbower/gutenberg2-dpo](https://huggingface.co/datasets/nbeerbower/gutenberg2-dpo), and [nbeerbower/gutenberg-moderne-dpo](https://huggingface.co/datasets/nbeerbower/gutenberg-moderne-dpo). |
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### Method |
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[ORPO tuned](https://mlabonne.github.io/blog/posts/2024-04-19_Fine_tune_Llama_3_with_ORPO.html) with 8x A100 for 2 epochs. |
