Open brando90 opened 1 year ago
If you are talking about the fast encoder, it was fixed in the main branch of transformers. AFAIK it hasn't been tagged as a release, yet.
Probably a duplicate of #40?
Check out our OpenLLaMA v2 model, which has a new tokenizer and is pretrained with a lot of code. The official release of that will happen very soon.
Brando Miranda Ph.D. Student Computer Science, Stanford University EDGE Scholar, Stanford University @.*** website: https://brando90.github.io/brandomiranda/home.html mentorship opportunities: https://brando90.github.io/brandomiranda/prospective-collaborations.html
On Jul 7, 2023, at 12:52 AM, Xinyang (Young) Geng @.***> wrote:
Check out our OpenLLaMA v2 modelhttps://huggingface.co/openlm-research/open_llama_7b_v2, which has a new tokenizer and is pretrained with a lot of code. The official release of that will happen very soon.
— Reply to this email directly, view it on GitHubhttps://github.com/openlm-research/open_llama/issues/61#issuecomment-1624940429, or unsubscribehttps://github.com/notifications/unsubscribe-auth/AAOE6LRSBDRPTWUHHJTCYHDXO654DANCNFSM6AAAAAAZWCI5DE. You are receiving this because you authored the thread.Message ID: @.***>
@brando90 The v2 model is a completely different one trained on a new mixture of dataset, so you'll need to load the new weights too.
Got it. Thanks!
I will assume v1 open llama is basically unusable for code gen (what I want) and use only v2.
Brando Miranda Ph.D. Student Computer Science, Stanford University EDGE Scholar, Stanford University @.*** website: https://brando90.github.io/brandomiranda/home.html mentorship opportunities: https://brando90.github.io/brandomiranda/prospective-collaborations.html
On Jul 7, 2023, at 11:55 AM, Xinyang (Young) Geng @.***> wrote:
@brando90https://github.com/brando90 The v2 model is a completely different one trained on a new mixture of dataset, so you'll need to load the new weights too.
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@brando90 Yeah. I imagine you probably want to use v2 almost always since it is a better model overall.
Does this still a bug for tokenization? I want to use this for code. Thanks!