---
license: llama2
pipeline_tag: text-generation
library_name: gguf
---
GGUF importance matrix (imatrix) quants for https://huggingface.co/LargeWorldModel/LWM-Text-Chat-128K  
The importance matrix was trained for 100K tokens (200 batches of 512 tokens) using wiki.train.raw.

* The imatrix Q4-K quant fits with 32K context on 24GB and gives me ~100 t/s inference on a 3090.
* With IQ3_XXS it seems to fit ~37K context on 24GB (and it is even faster than Q4-K).
* With either quant on a 3090 it seems to decode context at well over 2000 t/s.
* Using Q8 K-cache (instead of F16) you can fit up to 43-44K context but inference speed goes down a little bit.
* Also for some reason I need to use 1.0 penalty to avoid the response being cut-off.

| Layers | Context | [Template](https://github.com/LargeWorldModel/LWM/blob/9aaaa1e864bfcf31b66028e782395a22f4817535/scripts/eval_needle.py#L48) |
| --- | --- | --- |
| <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> |