From a recent PR by oobabooga:

This is what I get with 24gb vram (I haven’t tested extensively, it may be possible to go higher):

Model Params Maximum context
llama-13b max_seq_len = 8192, compress_pos_emb = 4 6079 tokens
llama-30b max_seq_len = 3584, compress_pos_emb = 2 3100 tokens

I also removed the chat_prompt_size parameter, since truncation_length can be reused for its purpose.

Now possible in text-generation-webui after this PR: https://github.com/oobabooga/text-generation-webui/pull/2875

I didn’t do anything other than exposing the compress_pos_emb parameter implemented by turboderp here, which in turn is based on kaiokendev’s recent discovery: https://kaiokendev.github.io/til#extending-context-to-8k

How to use it

  • Open the Model tab, set the loader as ExLlama or ExLlama_HF.

  • Set max_seq_len to a number greater than 2048. The length that you will be able to reach will depend on the model size and your GPU memory.

  • Set compress_pos_emb to max_seq_len / 2048. For instance, use 2 for max_seq_len = 4096, or 4 for max_seq_len = 8192.

  • Select the model that you want to load.

  • Set truncation_length accordingly in the Parameters tab. You can set a higher default for this parameter by copying settings-template.yaml to settings.yaml in your text-generation-webui folder, and editing the values in settings.yaml.

  • Those two new parameters can also be used from the command-line. For instance: python server.py --max_seq_len 4096 --compress_pos_emb 2. -

  • ArkyonVeil@lemmy.world
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    1 year ago

    Thanks for reposting the breakthroughs!

    Makes me have to visit Reddit less for news.

    It even rhymes, how neat is that.