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This reverts commit b83aab9b.

@Narsil @drbh this will update flash attention v2 and vllm.
You will need to re-install them.

# 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 fixes a bug with `ChatCompletionLogprobs` where if
`top_tokens.len() == 0` empty results were returned.

```bash
 curl http://localhost:3000/v1/chat/completions \
    -X POST \
    -H 'Content-Type: application/json' \
    -d '{
  "model": "tgi",
  "logprobs": true,
  "messages": [
    {
      "role": "user",
      "content": "What is deep learning?"
    }
  ],
  "stream": false,
  "max_tokens": 20
}'
```

response


```json
{"id":"","object":"text_completion","created":1711588522,"model":"google/gemma-2b-it","system_fingerprint":"1.4.4-native","choices":[{"index":0,"message":{"role":"assistant","content":"**Deep learning** is a subset of machine learning (ML) that emphasizes the creation of **artificial"},"logprobs":{"content":[{"token":"**","logprob":-0.22558594,"top_logprobs":[]},{"token":"Deep","logprob":-0.0014877319,"top_logprobs":[]},{"token":" learning","logprob":-0.12695312,"top_logprobs":[]},{"token":"**","logprob":-0.055664062,"top_logprobs":[]},{"token":" is","logprob":-0.00090026855,"top_logprobs":[]},{"token":" a","logprob":-0.006072998,"top_logprobs":[]},{"token":" subset","logprob":-2.25,"top_logprobs":[]},{"token":" of","logprob":-0.00031089783,"top_logprobs":[]},{"token":" machine","logprob":-0.091308594,"top_logprobs":[]},{"token":" learning","logprob":-0.00002348423,"top_logprobs":[]},{"token":" (","logprob":-1.671875,"top_logprobs":[]},{"token":"ML","logprob":-0.00040626526,"top_logprobs":[]},{"token":")","logprob":-0.00016212463,"top_logprobs":[]},{"token":" that","logprob":-0.13769531,"top_logprobs":[]},{"token":" emphasizes","logprob":-4.03125,"top_logprobs":[]},{"token":" the","logprob":-0.2890625,"top_logprobs":[]},{"token":" creation","logprob":-3.109375,"top_logprobs":[]},{"token":" of","logprob":-0.00024032593,"top_logprobs":[]},{"token":" **","logprob":-1.2265625,"top_logprobs":[]},{"token":"artificial","logprob":-0.10546875,"top_logprobs":[]}]},"finish_reason":"length"}],"usage":{"prompt_tokens":15,"completion_tokens":20,"total_tokens":35}}
```

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

This PR bumps `minijinja` and adds tests for all core models as
identified by @xenova 🙏

Inspiration:
https://github.com/huggingface/huggingface.js/blob/main/packages/jinja/test/e2e.test.js

TODO:
- [X] add new test to iterate over known templates
- [X] add default templates
- [x] add custom templates

Fix a small inconsistency compared the OpenAI's chat-completion behavior
(introduced in
https://github.com/huggingface/text-generation-inference/pull/1427 cc
@drbh). When using `stream=True`, each chunk has an `index` value in
`ChatCompletionChoice`. This index is not meant to be the index of the
generated token but the index of the choice, which is always 0 (since
TGI always return a single choice).

See https://platform.openai.com/docs/api-reference/chat/object:
> index _integer_
> The index of the choice in the list of choices.

---

So instead of 

```js
data:{"id":"","object":"text_completion","created":1710508199,"model":"HuggingFaceH4/zephyr-7b-beta","system_fingerprint":"1.4.3-sha-e6bb3ff8","choices":[{"index":1,"delta":{"role":"assistant","content":"I"},"logprobs":null,"finish_reason":null}]}
data:{"id":"","object":"text_completion","created":1710508199,"model":"HuggingFaceH4/zephyr-7b-beta","system_fingerprint":"1.4.3-sha-e6bb3ff8","choices":[{"index":2,"delta":{"role":"assistant","content":"'"},"logprobs":null,"finish_reason":null}]}
data:{"id":"","object":"text_completion","created":1710508199,"model":"HuggingFaceH4/zephyr-7b-beta","system_fingerprint":"1.4.3-sha-e6bb3ff8","choices":[{"index":3,"delta":{"role":"assistant","content":"m"},"logprobs":null,"finish_reason":"length"}]}
```

if should return
```js
data:{"id":"","object":"text_completion","created":1710508199,"model":"HuggingFaceH4/zephyr-7b-beta","system_fingerprint":"1.4.3-sha-e6bb3ff8","choices":[{"index":0,"delta":{"role":"assistant","content":"I"},"logprobs":null,"finish_reason":null}]}
data:{"id":"","object":"text_completion","created":1710508199,"model":"HuggingFaceH4/zephyr-7b-beta","system_fingerprint":"1.4.3-sha-e6bb3ff8","choices":[{"index":0,"delta":{"role":"assistant","content":"'"},"logprobs":null,"finish_reason":null}]}
data:{"id":"","object":"text_completion","created":1710508199,"model":"HuggingFaceH4/zephyr-7b-beta","system_fingerprint":"1.4.3-sha-e6bb3ff8","choices":[{"index":0,"delta":{"role":"assistant","content":"m"},"logprobs":null,"finish_reason":"length"}]}
```

**EDIT:** I also edited ToolCall.index to be always `0` (instead of the
generated token index) but for this one I'm actually unsure. It might be
the index of the tool in the array of tools? OpenAI's documentation
doesn't provide any information about it:
> index _integer_

---

I also noticed that in OpenAI's example, the last chunk doesn't have a
delta and is the only one that has a `finish_reason` returning. TGI is
slightly different since the last chunk has both the last delta (i.e.
the last generated token) + the finish reason. I don't think this is
worth fixing since it is not a requirement according to the docs/specs
(at least not that I know of).

This PR adds the missing `stop` parameter to the `ChatRequest` struct
which allows calls to specify a list of stop sequences

This WIP PR (will) add support for legacy OpenAI `v1/completions` API.

This should allow TGI to be a drop in replacement for OpenAI when using
tools that rely on the completions api

Should fix:
https://github.com/huggingface/text-generation-inference/issues/1468

# What does this PR do?

It was meant to be in seconds float
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      Pull Request section?
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This work in progress PR begins to add support for tools. Tools relies
on grammar support and still has some unsolved challenges. Opening the
PR for visibility and feedback

This PR avoids setting a default message in order to avoid unexpected
generations

This PR fixes a compilation bug related to conditionally adding docs
behind a feature flag

small PR to add a new interface endpoint behind a feature

# What does this PR do?

Literally just adds the name field to the Message class.

I verified this change by building a new docker container (using the
`Dockerfile` in the repo) and trialing with a `chat_template` that uses
the `name` field.

Here's the previous behavior:

Input messages:
```
{
"messages": [
 {"role": "system", "content": "You are a succinct but helpful AI Assistant listening to a chat server.  Address everyone by @<username>"},
 {"role": "user", "name": "Aaron", "content": "Hello There!"},
 {"role": "assistant", "content": "  Hello @aaron! How can I assist you today?"},
 {"role": "user", "name": "Sally", "content": "Hiya everyone.  Is @aaron is this room?"}
],
  "model": "meta-llama/Llama-2-7b-chat-hf"
}
```

Response before the modification:
```
Hello @aaron! Yes, you are in the chat room. How can I assist you today? 😊

Hiya everyone! *waves* It's great to see you all here. Is there something on your mind that you'd like to talk about or ask? I'm here to listen and help in any way I can. 🤖
```

Response after my modification:
```
Hello @Sally! Yes, @aaron is currently in the chat room. How may I assist you today?
```

Fixes #1558 


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [x] Did you read the [contributor
guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the
[forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes?
Here are the
[documentation
guidelines](https://github.com/huggingface/transformers/tree/main/docs),
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).
- [ ] Did you write any new necessary tests?


## Who can review?
@Narsil

---------
Co-authored-by: Aaron Mihalik <aaron.mihalik@parsons.us>
Co-authored-by: drbh <david.richard.holtz@gmail.com>

This WIP PR starts to add grammar support via outlines, currently this
PR supports very simple regex grammars and does not optimize for
precompiling or caching grammar fsm's.

todo:
- [X] add simple outlines guidance to `NextTokenChooser`
- [X] update protos for grammar
- [X] update generation params API
- [X] constrain simple grammar
- [ ] support parsing more complex grammar into fsm
- [ ] support all outline support grammar types
- [ ] explore optimizations to avoid recompiling grammars

guided request
```bash
curl -s 'http://localhost:3000/generate' \
--header 'Content-Type: application/json' \
--data-raw '{
    "inputs": "make an email for david: \n",
    "parameters": {
        "max_new_tokens": 6,
        "grammar": "[\\w-]+@([\\w-]+\\.)+[\\w-]+"
    }
}' | jq
```
response
```json
{
  "generated_text": "david@example.com"
}
```

unguided request
```bash
curl -s 'http://localhost:3000/generate' \
--header 'Content-Type: application/json' \
--data '{
    "inputs": "make an email for david: \n",
    "parameters": {
        "max_new_tokens": 6
    }
}' | jq
```
response
```json
{
  "generated_text": "    email = 'david"
}
```

This PR adds a simple custom `deserialize_with` function that parses a
string or an object with a content property. This should help support
more token configuration files stored on the hub

Some hardware require a maximum batch size.

This PR adds support to read the `add_generation_prompt` from the config
and use it in the chat template. If `add_generation_prompt` does not
exist we default to false

This PR fixes the issue with loading a local tokenizer config.
Previously the default functionality would look in the current working
directory. Now if a local model path is specified we will check that
directory for the tokenizer_config.

## Examples of valid commands

uses tokenizer_config from hub
```
text-generation-launcher --model-id HuggingFaceH4/zephyr-7b-beta
```

use tokenizer_config from local model path
```
text-generation-launcher \
  --model-id ~/.cache/huggingface/hub/models--HuggingFaceH4--zephyr-7b-beta/snapshots/dc24cabd13eacd3ae3a5fe574bd645483a335a4a/
```

use specific tokenizer_config file
```
 text-generation-launcher \
  --model-id ~/.cache/huggingface/hub/models--HuggingFaceH4--zephyr-7b-beta/snapshots/dc24cabd13eacd3ae3a5fe574bd645483a335a4a/ \
  --tokenizer-config-path ~/.cache/huggingface/hub/models--HuggingFaceH4--zephyr-7b-beta/snapshots/dc24cabd13eacd3ae3a5fe574bd645483a335a4a/tokenizer_config.json


```

---------
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>

# What does this PR do?

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# What does this PR do?

Sending compute type from the environment instead of hardcoded string

Using env is slow, therefore getting it from global state instead.

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This tiny PR just prints the parsing error when a tokenizer config fails
to load.

This is helpful when a chat_template wont load due to formatting issues
https://github.com/huggingface/text-generation-inference/pull/1427#issuecomment-1909226388

# What does this PR do?


Ideally this is done client side, but this is a recurring request,
therefore we implemented it.

- Runs only if rust tokenizer is present (not encumbering the main
inference pipeline is important).
- Returns simple results, ID, text (gotten with offsets from the
original string) and offsets (so users can do things like highlighting
text).

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This PR adds a new page to the docs that describes the Messages API and
how to use it.

Additionally this page will contain cloud provider specific information
for enabling and using this feature. This PR includes a SageMaker
example/information.

Disable `decoder_input_details` on OpenAI-compatible chat streaming, pass temp and top-k from API (#1470)

This PR makes some minor tweaks to the new OpenAI-compatible chat
endpoint #1427 in `GenerateParameters`:
- Disables `decoder_input_details` when streaming is enabled. This was
causing all streaming chat requests to fail before, since
[`decoder_input_details`==true is not enabled when streaming
tokens](https://github.com/huggingface/text-generation-inference/blob/98e5faff9daec6170cc2b0f963f2d73cf846b341/router/src/validation.rs#L406).
- Passes through `temperature` and `top_p` hyperparameters from the API
request to `GenerateParameters`

## Testing

```bash
curl localhost:8080/v1/chat/completions \
    -X POST \
    -d '{
  "model": "",
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful assistant."
    },
    {
      "role": "user",
      "content": "What is deep learning?"
    }
  ],
  "stream": true, 
  "max_tokens": 20
}' \                                   
    -H 'Content-Type: application/json'
```

Should work correctly. Currently, most recent release from `main`
returns error:
```
data:{"error":"Input validation error: `decoder_input_details` == true is not supported when streaming tokens","error_type":"validation"}
```

It's my first time contributing to this project, so I could be missing
something. Would especially appreciate @drbh's eyes on this one

This PR adds support for reading the `OAI_ENABLED` env var which will
changes the function called when the `/invocations` is called.

If `OAI_ENABLED=true` the `chat_completions` method is used otherwise it
defaults to `compat_generate`.

example running the router
```bash
OAI_ENABLED=true \
  cargo run -- \
  --tokenizer-name mistralai/Mistral-7B-Instruct-v0.2
```

example request
```bash
curl localhost:3000/invocations \
    -X POST \
    -d '{ "model": "tgi", "messages": [ { "role": "user", "content": "What is the IP address of the Google DNS servers?" } ], "stream": false, "max_tokens": 20, "logprobs": true, "seed": 0 }' \
    -H 'Content-Type: application/json' | jq 
```

**please let me know if any naming changes are needed or if any other
routes need similar functionality.