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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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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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
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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.
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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
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Co-authored-by: p_spozzhang <p_spozzhang@tencent.com>
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# What does this PR do?
Fixed typo.
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implemetation -> implementation
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# What does this PR do?
- Adds Rope NTK scaling.
Done because
https://github.com/huggingface/text-generation-inference/pull/529 was
closed
Took some code from
https://github.com/huggingface/transformers/pull/24653
- `--rope-scaling` and `--rope-factor` are added separately. I
considered having a single one and parsing something line ("linear:4.0"
, or "dynamic") but decided against
it because it would push more parsing+validation a bit everywhere (both
in the launcher and the server).
Fixes#512
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# What does this PR do?
Redoes #719
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As per title.
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- Current PR is not great because we're side stepping the
`Weights.__init__` but Weights shouldn't requires anything related
to the config or the model_id as it aims to be a simple Wrapper
over multi file loading.
- Ideal solution would be to use something like Rust enum
```
enum Quantize{
Bitandbytes(Bitsandbytes),
GPTQ(bits: usize, groupsize: usize)
```
And passing that around during load. Unfortunately we don't
have access to this, so for now, side-stepping seems easier.
- Re-enabling groupsize<0 with exllama (confirmed it works.)
Helps #601
In next steps we should make sure our quantization script uses that
format and make it standard.
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Omar Sanseviero