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license: llama2 |
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pipeline_tag: text-generation |
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library_name: gguf |
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GGUF importance matrix (imatrix) quants for https://huggingface.co/LargeWorldModel/LWM-Text-Chat-128K |
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The importance matrix was trained for 100K tokens (200 batches of 512 tokens) using wiki.train.raw. |
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* The imatrix Q4-K quant fits with 32K context on 24GB and gives me ~100 t/s inference on a 3090. |
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* With IQ3_XXS it seems to fit ~37K context on 24GB (and it is even faster than Q4-K). |
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* With either quant on a 3090 it seems to decode context at well over 2000 t/s. |
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* Using Q8 K-cache (instead of F16) you can fit up to 43-44K context but inference speed goes down a little bit. |
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* Also for some reason I need to use 1.0 penalty to avoid the response being cut-off. |
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| Layers | Context | [Template](https://github.com/LargeWorldModel/LWM/blob/9aaaa1e864bfcf31b66028e782395a22f4817535/scripts/eval_needle.py#L48) | |
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| --- | --- | --- | |
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| <pre>32</pre> | <pre>131072</pre> | <pre>You are a helpful assistant.<br>USER:<br>{context}<br>{question}<br>Don't give information outside the document or repeat your findings. Keep your response short and direct.<br>ASSISTANT:<br>{response}</pre> | |
