# 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.
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@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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# 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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# What does this PR do?
Fixes#531
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I added ToC for docs v1 & started setting up for doc-builder. cc @Narsil
@osanseviero
---------
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Co-authored-by: osanseviero <osanseviero@gmail.com>
Co-authored-by: Mishig <mishig.davaadorj@coloradocollege.edu>
# What does this PR do?
Reflecting
https://github.com/huggingface/transformers/pull/24998
Current status wants to make sure integration tests *are* broken with
this.
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# What does this PR do?
Fixes#787
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Fixes#784
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Typo
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# What does this PR do?
See #626
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---------
Co-authored-by: krzim <zimmerk4@live.com>
# What does this PR do?
Fixes#732
And remove duplicate AutoTokenizer import
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- Will detect `peft` model by finding `adapter_config.json`.
- This triggers a totally dedicated `download-weights` path
- This path, loads the adapter config, finds the base model_id
- It loads the base_model
- Then peft_model
- Then `merge_and_unload()`
- Then `save_pretrained(.., safe_serialization=True)
- Add back the config + tokenizer.merge_and_unload()`
- Then `save_pretrained(.., safe_serialization=True)
- Add back the config + tokenizer.
- The chosen location is a **local folder with the name of the user
chosen model id**
PROs:
- Easier than to expect user to merge manually
- Barely any change outside of `download-weights` command.
- This means everything will work in a single load.
- Should enable out of the box SM + HFE
CONs:
- Creates a local merged model in unusual location, potentially
not saved across docker reloads, or ovewriting some files if the PEFT
itself was local and containing other files in addition to the lora
Alternatives considered:
- Add `local_files_only=True` every where (discard because of massive
code change for not a good enough reason)
- Return something to `launcher` about the new model-id (a cleaner
location for this new model), but it would
introduce new communication somewhere where we didn't need it before.
- Using the HF cache folder and *stopping* the flow after
`download-weights` and asking user to restart with the actual local
model location
Fix#482
# What does this PR do?
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Fixes # (issue)
## Before submitting
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# What does this PR do?
CI cehck for #501
This PR automatically points tensors that were removed due to
deduplication to their still existing twin.
In `server.text_generation_server.utils.convert.py#convert_file`,
tensors that have a value equal to another tensor are removed from the
list of weights. Their name, and the name of the still-existing "twin"
are logged to the "metadata" dictionary. However, this dictionary was
not yet used during loading. This requires explicit in-code remapping
when loading the models (as mentioned in the docstring).
This PR adds some simple code to check, during loading, if a weight is
one of those removed weights. It then automatically retrieves the values
of its still-existing "twin" instead.
## What does this fix?
We currently cannot load `h2oai/h2ogpt-oig-oasst1-falcon-40b` with the
unmodified server, since the `transformer.word_embeddings.weight` weight
is equal to `lm_head.weight` and is automatically removed. The falcon
code, however, still expects this weight to exist. I could have also
added some extra checks to the model itself, though that would only be a
workaround.
## Before submitting
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---------
# What does this PR do?
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Co-authored-by: Vincent Brouwers <vincentbrouwers9@gmail.com>
Co-authored-by: Vincent Brouwers <vincent.brouwers@ing.com>
# What does this PR do?
Fixes#749
## Who can review?
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Co-authored-by: p_spozzhang <p_spozzhang@tencent.com>
王佳欣