# 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
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Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the
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Here are the
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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 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
# 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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Fixes # (issue)
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
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Pull Request section?
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[forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
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Here are the
[documentation
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and
[here are tips on formatting
docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
Anyone in the community is free to review the PR once the tests have
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This PR adds support to handle the custom jinja function
`raise_exception` and passes the `bos` and `eos` tokens into the
template
Additionally this PR adds 3 tests to validate and show examples of what
can and cannot be parsed currently.
```bash
cargo test --package text-generation-router --lib -- infer::tests --nocapture
# Finished test [unoptimized + debuginfo] target(s) in 7.82s
# Running unittests src/lib.rs (target/debug/deps/text_generation_router-18a0bbf99c2ca1b4)
# running 3 tests
# test infer::tests::test_chat_template_valid_with_raise ... ok
# test infer::tests::test_chat_template ... ok
# test infer::tests::test_chat_template_invalid_with_raise ... ok
# test result: ok. 3 passed; 0 failed; 0 ignored; 0 measured; 15 filtered out; finished in 0.00s
```
This PR adds support to make TGI a drop in replacement for OpenAI
clients by exposing the same HTTP interface.
Notes
- TGI inits a single model at startup so the `model` field is unused in
HTTP requests.
- `max_tokens` and `stream` should work as expected but other params may
be (unimplemented or not supported)
General approach
- fetch the `tokenizer_config` at startup from the hub
- pass `tokenizer_config` into `Infer` so we have it at request time
- use the `chat_template` on the config to format chat request
- parse jinja template and render chat string
- pass inputs into existing generate function
- wrap generation output in expected structure before returning
# How to test
### Streaming curl
```bash
curl localhost:3000/v1/chat/completions \
-X POST \
-d '{
"model": "tgi",
"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'
```
It is also possible to use the `openai` python library and change the
base url
### 🌊 STREAMING REQUEST
```python
from openai import OpenAI
# init the client but point it to TGI
client = OpenAI(
base_url="http://localhost:3000/v1",
api_key="not needed for a local LLM"
)
chat_completion = client.chat.completions.create(
model="tgi",
messages=[
{"role": "system", "content": "You are a helpful assistant." },
{"role": "user", "content": "What is deep learning?"}
],
stream=True
)
# iterate and print stream
for message in chat_completion:
print(message)
# ChatCompletionChunk(id='', choices=[Choice(delta=ChoiceDelta(content=' that', function_call=None, role='assistant', tool_calls=None), finish_reason=None, index=2, logprobs=None)], created=1704486761, model='', object='text_completion', system_fingerprint='')
```
### 🚗 SYNCHRONOUS REQUEST
```python
from openai import OpenAI
# init the client but point it to TGI
client = OpenAI(
base_url="http://localhost:3000/v1",
api_key="not needed for a local LLM"
)
chat_completion = client.chat.completions.create(
model="tgi",
messages=[
{"role": "system", "content": "You are a helpful assistant." },
{"role": "user", "content": "What is deep learning?"}
],
stream=False
)
print(chat_completion)
# ChatCompletion(id='', choices=[Choice(finish_reason=None, index=0, logprobs=None, message=ChatCompletionMessage(content='\nDeep learning is a new field of research that has been gaining traction in the last ...', role='assistant', function_call=None, tool_calls=None))], created=1704486762, model='', object='text_completion', system_fingerprint='', usage=CompletionUsage(completion_tokens=100, prompt_tokens=76, total_tokens=176))
```
## How to run dev
```bash
cd text-generation-inference/server
MASTER_ADDR=127.0.0.1 MASTER_PORT=5555 text-generation-server serve --trust-remote-code gpt2
```
***note many of the existing `chat_templates` use non standard `jinja`
(ie. adding a `raise` to the template) which will throw an error when
parsing; hence using `upstage/SOLAR-10.7B-Instruct-v1.0` since it has a
valid template
```bash
cd text-generation-inference/router
cargo run -- --tokenizer-name upstage/SOLAR-10.7B-Instruct-v1.0
```
trigger
```bash
curl localhost:3000/v1/chat/completions \
-X POST \
-d '{ "model": "gpt-3.5-turbo", "messages": [ { "role": "system", "content": "You are a helpful assistant." }, { "role": "user", "content": "What is the IP address of the Google DNS servers?" } ], "stream": true, "max_tokens": 20, "logprobs": true }' \
-H 'Content-Type: application/json'
```
^ supports `stream: true` and `stream: false` requests
# What does this PR do?
Fixes#637
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---------
Co-authored-by: Vincent Brouwers <vincent.brouwers@ing.com>
