Nicolas Patry 67347950b7 feat(server): Implements sharding for non divisible vocab_size. (#583) ... - The code is relatively easy (just disable the checks on Embedding and Head) This cannot be done in the same easy fashion for hidden_dim/head_dim. It's relatively easy on some models (classic MHA) but it would make the other models (MQA) much more complex, and GPTQ quantization another quite hairy piece of code.		2023-07-12 16:43:31 +02:00
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gptq	feat(server): Add inference support for GPTQ (llama + falcon tested) + Quantization script (#438)	2023-06-26 12:27:01 +02:00
__init__.py	feat(server): Rework model loading (#344)	2023-06-08 14:51:52 +02:00
convert.py	fix(server): harden the weights choice to save on disk. (#561)	2023-07-07 14:50:12 +02:00
dist.py	fix(server): Do not init process group if already initialized (#388)	2023-06-26 12:32:54 +02:00
hub.py	feat(server): Adding new ignore_rule for conversion. (#485)	2023-06-23 12:41:13 +02:00
layers.py	feat(server): Implements sharding for non divisible vocab_size. (#583)	2023-07-12 16:43:31 +02:00
logits_process.py	fix(server): avoid errors for very small top_p values (#544)	2023-07-04 20:11:33 +02:00
tokens.py	feat(server): add paged attention to flash models (#516)	2023-06-30 19:09:59 +02:00
watermark.py	fix(server): fix flash-neox scores warping (#137)	2023-03-24 18:21:41 +01:00
weights.py	feat(server): Implements sharding for non divisible vocab_size. (#583)	2023-07-12 16:43:31 +02:00