* feat: first draft load multiple lora
* feat: load weights within layer and refactor lora pass
* fix: refactor and reduce lora math
* feat: baseline impl single request multi lora support
* feat: prefer lorax implementation and port loading logic
* fix: prefer adapter_data and refactors
* feat: perfer loraxs custom punica kernels and add mlp loras
* fix: adjust batch for bgmv
* fix: adjust adapter_segments logic when in batch
* fix: refactor and move changes to v3 proto
* fix: pass model_id for all flash causal lms
* fix: pass model_id for all causal and seq2seq lms
* fix: add model_id to model test
* feat: add lora support to mistral and refactors
* feat: prefer model id in request
* fix: include rust code for adapter id
* feat: bump launcher and add new lora docs
* feat: support base model generation and refactors
* fix: rename doc to retry ci build
* feat: support if vlm models
* fix: add adapter_data param and avoid missing layers
* fix: add adapter_data param to phi and neox
* fix: update all models forwards to include adapter_data
* fix: add model_id to IdeficsCausalLM
* Update lora.md
Fixed a typo
* Update lora.md
Fixing spam image
* fix: add lora kernel to dockerfile, support running without kernels and refactors
* fix: avoid dockerfile conflict
* fix: refactors and adjust flash llama lora logic
* fix: skip llama test due to CI issue (temp)
* fix: skip llama test CI (temp) 2
* fix: revert skips and prefer updated ci token for tests
* fix: refactors and helpful comments
* fix: add noop in TensorParallelAdapterRowLinear too
* fix: refactor and move shard_lora_weights logic
* fix: exit early if no adapter_data
---------
Co-authored-by: Derek <datavistics@gmail.com>
* feat: add kserve feature and basic routes
* feat: implement infer endpoint wrapper around generate
* fix: refactor and improve types
* fix: improve infer and simplify
* fix: cleanup and improve api docs
* fix: refactor and encapsulate kserve feat in file
* fix: remove typos after rebase
# What does this PR do?
Making `make install` a much better sane default to start local dev
environments.
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- Refactor code to allow supporting multiple versions of the
generate.proto at the same time
- Add v3/generate.proto (ISO to generate.proto for now but allow for
future changes without impacting v2 backends)
- Add Schedule trait to abstract queuing and batching mechanisms that
will be different in the future
- Add SchedulerV2/V3 impl
Before this change, the generation input was sent to the backend as a
single string, encoding images as Base64 and packing them in
Markdown-style links.
This change adds a new chunked input representation that separates text
chunks from images chunks. Image chunks contain binary data (for smaller
message sizes) and the image's MIME type.
The stringly-typed inputs are still sent to support backends that do not
support chunked inputs yet.
# What does this PR do?
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- Axum upgraded to hyper 1.0 and most of the ecosystem switched so it's
our time now
- [ngrok-rust](https://github.com/ngrok/ngrok-rust/pull/137/files)
hasn't yet, and hasn't for several months now, so let's disabled the
feature for the time being.
# What does this PR do?
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This PR loads the `processor_config` similar to the `tokenizer_config`
and uses the processor_config's chat_template if the tokenizer_config
does not include one. These changes enable chat with idefics2
- Added a debug log for speculated ids (helps seeing in logs quality of
a speculator).
- Remove newlines from child process logs when re-emitting in non JSON
mode.
- Made standard level be closer to what's expected (only our binaries
level).
- Propagate that level correctly to the shard (was forced into INFO).
# What does this PR do?
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# What does this PR do?
- Add the stop parameter to the completion route
- Add the completion method to the python client
- Add the stop parameter to the python client's chat method
## Before submitting
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other checks if that's the case).
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@Narsil
---------
Co-authored-by: Thomas SCHILLACI <tschilla@px101.prod.exalead.com>
Co-authored-by: Thomas Schillaci <thomas.schillaci@3ds.com>
# What does this PR do?
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This PR adds paligemma modeling code
Blog post: https://huggingface.co/blog/paligemma
Transformers PR: https://github.com/huggingface/transformers/pull/30814
install the latest changes and run with
```bash
# get the weights
# text-generation-server download-weights gv-hf/PaliGemma-base-224px-hf
# run TGI
text-generation-launcher --model-id gv-hf/PaliGemma-base-224px-hf
```
basic example sending various requests
```python
from huggingface_hub import InferenceClient
client = InferenceClient("http://127.0.0.1:3000")
images = [
"https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png",
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png",
]
prompts = [
"What animal is in this image?",
"Name three colors in this image.",
"What are 10 colors in this image?",
"Where is the cow standing?",
"answer en Where is the cow standing?",
"Is there a bird in the image?",
"Is ther a cow in the image?",
"Is there a rabbit in the image?",
"how many birds are in the image?",
"how many rabbits are in the image?",
]
for img in images:
print(f"\nImage: {img.split('/')[-1]}")
for prompt in prompts:
inputs = f"{prompt}\n"
json_data = {
"inputs": inputs,
"parameters": {
"max_new_tokens": 30,
"do_sample": False,
},
}
generated_output = client.text_generation(prompt, max_new_tokens=30, stream=False)
print([f"{prompt}\n{generated_output}"])
```
---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
# What does this PR do?
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This change adds `FlashGPT2ForCausalLM` and wires it up. The model
itself is pretty straightforward, the main difference from other models
is that it uses trained position embeddings and that all weight matrices
are transposed compared to other models (due to the use of Conv1D in the
upstream model).
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Add `router` key in `/info` endpoint and set it to
`env!("CARGO_PKG_NAME")` => so always set to `"text-generation-router"`
in TGI. Happy to change the naming if you think of a better one
(framework? package_name?)
The goal is to use this information in `InferenceClient` to know the
model is served with TGI. At the moment we can use
https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2/info
to infer it is TGI-served because it returns information but having a
proper key would be better.
For context, a transformers-served model is only outputting `{"ok":
"ok"}` (see
[here](https://api-inference.huggingface.co/models/microsoft/DialoGPT-large/info)).
# What does this PR do?
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## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
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This PR allows for messages to be formatted as simple strings, or as an
array of objects including image urls. This is done by formatting
content arrays into a simple string.
Example using `llava-hf/llava-v1.6-mistral-7b-hf`
```bash
curl localhost: 3000/v1/chat/completions \
-X POST \
-H 'Content-Type: application/json' \
-d '{
"model": "tgi",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "Whats in this image?"
},
{
"type": "image_url",
"image_url": {
"url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png"
}
}
]
}
],
"stream": false,
"max_tokens": 20,
"seed": 42
}'
```
is equivlant to this more simple request
```bash
curl localhost: 3000/v1/chat/completions \
-X POST \
-H 'Content-Type: application/json' \
-d '{
"model": "tgi",
"messages": [
{
"role": "user",
"content": "Whats in this image?\n"
}
],
"stream": false,
"max_tokens": 20,
"seed": 42
}'
```
output
```
# {"id":"","object":"text_completion","created":1714406985,"model":"llava-hf/llava-v1.6-mistral-7b-hf","system_fingerprint":"2.0.1-native","choices":[{"index":0,"message":{"role":"assistant","content":" This is an illustration of an anthropomorphic rabbit in a spacesuit, standing on what"},"logprobs":null,"finish_reason":"length"}],"usage":{"prompt_tokens":2945,"completion_tokens":20,"total_tokens":2965}}%
```
---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
# What does this PR do?
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`/v1/chat/completions` and `/v1/completions` have different output types
depending on the `stream` parameter. This PR aims at fixing the
inconsistency in the auto-generated
[openapi.json](https://huggingface.github.io/text-generation-inference/openapi.json)
specs.
cc @OlivierDehaene @drbh I reused what had been done for the `/`
endpoint but haven't tested anything myself. Could you confirm this is
the correct way of handling things?
Also, should I update the openapi.json file manually? If yes, how can I
do it?
This PR makes tool calling aware of the name of the function selected.
Fixes:
https://github.com/huggingface/text-generation-inference/issues/1657
Thank you @puppetm4st3r for the helpful snippets, large parts of this PR
are simply refactors of the code shared 🙏
**opening draft PR because small tweaks are needed before merging
# What does this PR do?
- Renamed `max_input_length` into `max_input_tokens` for consistency
(backward compatible change, will yell if both are set.)
- Will now use the config for `max_input_tokens` `max_total_token` and
`max_batch_total_tokens`.
- Capping the values to 16k in order to save VRAM on behalf of users
(overriddable by simply setting the values).
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# What does this PR do?
- Changed all models to extract `embed_tokens` in order to enable llava
to separately call the embeddings and the core model layers.
- Added VlmCausalLM to inherit from FlashMistral in order to be
maximally supported. The only added logics sits on top and parses images
into pixel values, preallocates input_ids space for the image
embeddings, and passes them for the model.
- Added Clip for the vision tower.
- Didn't add flash for the vision tower since there's no padding anyway.
- Added heuristic (potentially incomplete) to calculate number of
features *before* calculating the clip patches (allows for easier logic
reuse of the LLM under the hood).
Still needs to be done:
- [x] Implement the image parsing in the controller side, to avoid
downloading n times per TP shard and also refusing requests too large
early and avoid issues where the truncation actually truncates the
image.
- [ ] Make sure it works with quantization properly.
- [x] Make sure it works with TP>1
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This PR resolves a couple
- [X] adjusts the tool response to align with openai's tools response
type
- [X] bumps pydantic to `2.6.4` in all apps (resolves dependency issue
when running tests)
- [X] bump `outlines` version and fix import for new name
drbh
