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fix baichuan config init bug

See merge request huangwb1/text-generation-inference!3

This PR improves the guidance docs and adds a section that explains how
grammars are applied on a technical level

# What does this PR do?

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

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![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png)

"
        }
    ],
    "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>

This PR start to add documentation for visual language models

Thank you so much for the work you are doing, this is my little
contribution to this great thing you have built. I hope it is useful and
helpful, please don't hesitate to discuss any matters that are not
clear!

I am basing my implementation of frequency penalty on OpenAI's
implementation:
https://platform.openai.com/docs/guides/text-generation/parameter-details

The problem I see with TGI's current implementation is that is not
taking into account the frequency of tokens which have already been
sampled in the current generation stream. Also, the scaling is of the
adjusted token logits is done differently for positive and negative
logits. While in OpenAI's implementation token frequency is taking into
account and the scaling is always done with a subtraction (if penalty is
positive) or add operation (if penalty is negative).

This leads to corrupt generations as I mentioned in issue #1810 .
Moreover, after my tests, other issues are also gone like the one about
some request's with ``penalty_frequency = 1.0`` overruling other
requests (with ``frequency_penalty = 0.0``) in the same batch and
therefore corrupting all generations in the batch. Basically, padding
does not affect this implementation so I believe this ``score *=
input_ids.ne(0)`` is not needed anymore.



Frequency penalty | -1.0 | 0.0 | 1.0
-- | -- | -- | --
Before my change | https://paste.mozilla.org/JxqGJkWY |
https://paste.mozilla.org/hrztJ56h | https://paste.mozilla.org/pBSEH2zw
After my change | https://paste.mozilla.org/7gXCi7zo |
https://paste.mozilla.org/ZR9rJ92g | https://paste.mozilla.org/gHaD2YnC



---------
Co-authored-by: martini <martin.iglesiasgoyanes@adyen.com>

This PR adds a short "how it works" section to guidance and includes a
mention to the outlines library that enables grammars/tools


*and a small formatting change

---------
Co-authored-by: Mishig <mishig.davaadorj@coloradocollege.edu>

# What does this PR do?
This PR makes a small addition to the readme that reference new TGI
support for TPUs via Optimum TPU
(https://huggingface.co/docs/optimum-tpu/howto/serving)

# What does this PR do?

Just unifying some branches and making intentions clearer (no cuda graph
when 0 all the way in the launcher)

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This PR adds the missing `tool_prompt` parameter in Python client
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fix param s=1 benchmark crash bug

See merge request huangwb1/text-generation-inference!1

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This should enable more aggressive by default stacking, meaning better
throughput (in throughput constrained environements).

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---------
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Co-authored-by: Morgan Funtowicz <funtowiczmo@gmail.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>

# What does this PR do?


On TPU (and probably inferentia). The model needs to know right off the
bat about BATCH_SIZE and MAX_TOTAL_TOKENS (since the entire cache will
be determined by both).

This PR sends that information to the shards to they can allocate
accordingly. Should be no-op for other backends.
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