As more and more people begin to use Baichuan's open-source models, the
influence of Baichuan models is growing, especially in China. Many
community members are interested in adding support for Baichuan models
to TGI. Meanwhile, Baichuan is a very open company, and in the future,
it plans to open-source more and more models, taking all this into
consideration, we would like to add support for the Baichuan model to
TGI. To do this, we need to make some changes, which we hope can be
merged into the main branch of TGI. In the future, we would be happy to
help maintain support for Baichuan models in TGI. We sincerely hope that
our pull request can be accepted. Thank you.
By the way, the changes of this time mainly for supporting Baichuan-7B.
---------
Co-authored-by: xiaoyuze <xiaoyuze@baichuan.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
# What does this PR do?
Fix __call__ vs forward.
To reproduce error just launch:
TheBloke/WizardLM-Uncensored-Falcon-7B-GPTQ with gptq (it fails because
falcon code uses `__call__` instead for `forward` calls)
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IDK what else to add in this guide, I looked for relevant code in TGI
codebase and saw that it's used in quantization as well (maybe I could
add that?)
PR for conceptual guide on flash attention. I will add more info unless
I'm told otherwise.
---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: Omar Sanseviero <osanseviero@gmail.com>
# What does this PR do?
Disabling exllama on old compute.
Exllama + T4 don't play nice together, this will disable it right away
to avoid issues at runtime.
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This should allow users to use either Pydantic 2 or Pydantic 1.
I couldn't run all tests locally because I reran them too often and got
rate limited, but I believe this is sufficient.
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# What does this PR do?
During the safetensor conversion, duplicate weights are removed.
However, which of the duplicates gets removed, differs per checkpoint.
In some, like `h2oai/h2ogpt-oig-oasst1-falcon-40b`, the weight
`transformer.word_embeddings.weightSafetensor` gets removed. In others,
`lm_head.weight` gets removed. Long story long, we need to support both.
Originally, f018143 mapped `lm_head` to `word_embeddings`. Then ac736fd
switched this around. This commit merges them and allows for both.
## Before submitting
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## Who can review?
@Narsil, you wrote both commits I referenced in this PR. I think you'll
understand this change :)
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---------
Co-authored-by: Vincent Brouwers <vincent.brouwers@ing.com>
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Co-Authored-By: Victor Sanh <victorsanh@gmail.com>
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Co-authored-by: Victor Sanh <victorsanh@gmail.com>
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# What does this PR do?
- New "falcon" layout on this repo
- No alibi
- `transformers` already modifying cache layout in our stead (same
modifications).
- Output is garbage. Not sure why.
Does not fix#826 but it's a step.
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List of changes
- No need to specify `model` in `text_generation` if it's already
specified in `InferenceClient`
- I separated the explanation of `stream=True` and `details=True`
- I found the details explanation a bit repetitive (it says two times
what it returns), so removed a sentence
- Add mention of async client
# What does this PR do?
Fixes#843
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One-line addition to the README to show how to run the service on a
machine without GPUs or CUDA support (e.g., for local prototyping)
---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
# What does this PR do?
A few tokenizer_config in huggingface use LlamaTokenizer, so I think I
would have selected `LlamaTokenizer` before.
For a few cases where you're using a llama structure but not a llama
tokenizer, why not make it to call the AutoTokenizer in exception
handling.
In the case of `decapoda-research/llama-7b-hf`, LLamaTokenizer is still
being used in config.json, so it should be called through`
LlamaTokenizer`.
Also, if an exception is thrown by LlamaTokenizer, it will cause
`LlamaTokenzierFast` to be called from AutoTokenizer.
Fixes # 560
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@Narsil
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Fixes#816
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# What does this PR do?
Fixes#531
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Merve Noyan