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|
|
|
|
|
|
|
|
|
|
[[package]]
|
|
|
|
|
name = "flate2"
|
2023-11-30 07:18:15 -07:00
|
|
|
version = "1.0.28"
|
2022-10-08 04:30:12 -06:00
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
2023-11-30 07:18:15 -07:00
|
|
|
checksum = "46303f565772937ffe1d394a4fac6f411c6013172fadde9dcdb1e147a086940e"
|
2022-10-08 04:30:12 -06:00
|
|
|
dependencies = [
|
|
|
|
|
"crc32fast",
|
|
|
|
|
"miniz_oxide",
|
|
|
|
|
]
|
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|
|
|
|
2023-05-09 05:19:31 -06:00
|
|
|
[[package]]
|
|
|
|
|
name = "float-ord"
|
|
|
|
|
version = "0.3.2"
|
|
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
|
|
|
|
checksum = "8ce81f49ae8a0482e4c55ea62ebbd7e5a686af544c00b9d090bba3ff9be97b3d"
|
|
|
|
|
|
2022-12-16 03:29:36 -07:00
|
|
|
[[package]]
|
|
|
|
|
name = "float_eq"
|
|
|
|
|
version = "1.0.1"
|
|
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
|
|
|
|
checksum = "28a80e3145d8ad11ba0995949bbcf48b9df2be62772b3d351ef017dff6ecb853"
|
|
|
|
|
|
2022-10-08 04:30:12 -06:00
|
|
|
[[package]]
|
|
|
|
|
name = "fnv"
|
|
|
|
|
version = "1.0.7"
|
|
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
|
|
|
|
checksum = "3f9eec918d3f24069decb9af1554cad7c880e2da24a9afd88aca000531ab82c1"
|
|
|
|
|
|
|
|
|
|
[[package]]
|
|
|
|
|
name = "foreign-types"
|
|
|
|
|
version = "0.3.2"
|
|
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
|
|
|
|
checksum = "f6f339eb8adc052cd2ca78910fda869aefa38d22d5cb648e6485e4d3fc06f3b1"
|
|
|
|
|
dependencies = [
|
|
|
|
|
"foreign-types-shared",
|
|
|
|
|
]
|
|
|
|
|
|
|
|
|
|
[[package]]
|
|
|
|
|
name = "foreign-types-shared"
|
|
|
|
|
version = "0.1.1"
|
|
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
|
|
|
|
checksum = "00b0228411908ca8685dba7fc2cdd70ec9990a6e753e89b6ac91a84c40fbaf4b"
|
|
|
|
|
|
|
|
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|
[[package]]
|
|
|
|
|
name = "form_urlencoded"
|
2023-11-30 07:18:15 -07:00
|
|
|
version = "1.2.1"
|
2022-10-08 04:30:12 -06:00
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
2023-11-30 07:18:15 -07:00
|
|
|
checksum = "e13624c2627564efccf4934284bdd98cbaa14e79b0b5a141218e507b3a823456"
|
2022-10-08 04:30:12 -06:00
|
|
|
dependencies = [
|
|
|
|
|
"percent-encoding",
|
|
|
|
|
]
|
|
|
|
|
|
|
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|
[[package]]
|
|
|
|
|
name = "futures"
|
2024-01-26 11:04:57 -07:00
|
|
|
version = "0.3.30"
|
2022-10-08 04:30:12 -06:00
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
2024-01-26 11:04:57 -07:00
|
|
|
checksum = "645c6916888f6cb6350d2550b80fb63e734897a8498abe35cfb732b6487804b0"
|
2022-10-08 04:30:12 -06:00
|
|
|
dependencies = [
|
|
|
|
|
"futures-channel",
|
|
|
|
|
"futures-core",
|
|
|
|
|
"futures-executor",
|
|
|
|
|
"futures-io",
|
|
|
|
|
"futures-sink",
|
|
|
|
|
"futures-task",
|
|
|
|
|
"futures-util",
|
|
|
|
|
]
|
|
|
|
|
|
|
|
|
|
[[package]]
|
|
|
|
|
name = "futures-channel"
|
feat: supports openai chat completions API (#1427)
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
2024-01-16 03:07:41 -07:00
|
|
|
version = "0.3.30"
|
2022-10-08 04:30:12 -06:00
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
feat: supports openai chat completions API (#1427)
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
2024-01-16 03:07:41 -07:00
|
|
|
checksum = "eac8f7d7865dcb88bd4373ab671c8cf4508703796caa2b1985a9ca867b3fcb78"
|
2022-10-08 04:30:12 -06:00
|
|
|
dependencies = [
|
|
|
|
|
"futures-core",
|
|
|
|
|
"futures-sink",
|
|
|
|
|
]
|
|
|
|
|
|
|
|
|
|
[[package]]
|
|
|
|
|
name = "futures-core"
|
feat: supports openai chat completions API (#1427)
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
2024-01-16 03:07:41 -07:00
|
|
|
version = "0.3.30"
|
2022-10-08 04:30:12 -06:00
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
feat: supports openai chat completions API (#1427)
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
2024-01-16 03:07:41 -07:00
|
|
|
checksum = "dfc6580bb841c5a68e9ef15c77ccc837b40a7504914d52e47b8b0e9bbda25a1d"
|
2022-10-08 04:30:12 -06:00
|
|
|
|
|
|
|
|
[[package]]
|
|
|
|
|
name = "futures-executor"
|
2024-01-26 11:04:57 -07:00
|
|
|
version = "0.3.30"
|
2022-10-08 04:30:12 -06:00
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
2024-01-26 11:04:57 -07:00
|
|
|
checksum = "a576fc72ae164fca6b9db127eaa9a9dda0d61316034f33a0a0d4eda41f02b01d"
|
2022-10-08 04:30:12 -06:00
|
|
|
dependencies = [
|
|
|
|
|
"futures-core",
|
|
|
|
|
"futures-task",
|
|
|
|
|
"futures-util",
|
|
|
|
|
]
|
|
|
|
|
|
|
|
|
|
[[package]]
|
|
|
|
|
name = "futures-io"
|
feat: supports openai chat completions API (#1427)
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
2024-01-16 03:07:41 -07:00
|
|
|
version = "0.3.30"
|
2022-10-08 04:30:12 -06:00
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
feat: supports openai chat completions API (#1427)
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
2024-01-16 03:07:41 -07:00
|
|
|
checksum = "a44623e20b9681a318efdd71c299b6b222ed6f231972bfe2f224ebad6311f0c1"
|
2022-10-08 04:30:12 -06:00
|
|
|
|
|
|
|
|
[[package]]
|
|
|
|
|
name = "futures-macro"
|
feat: supports openai chat completions API (#1427)
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
2024-01-16 03:07:41 -07:00
|
|
|
version = "0.3.30"
|
2022-10-08 04:30:12 -06:00
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
feat: supports openai chat completions API (#1427)
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
2024-01-16 03:07:41 -07:00
|
|
|
checksum = "87750cf4b7a4c0625b1529e4c543c2182106e4dedc60a2a6455e00d212c489ac"
|
2022-10-08 04:30:12 -06:00
|
|
|
dependencies = [
|
|
|
|
|
"proc-macro2",
|
|
|
|
|
"quote",
|
2024-01-26 11:04:57 -07:00
|
|
|
"syn 2.0.48",
|
2022-10-08 04:30:12 -06:00
|
|
|
]
|
|
|
|
|
|
|
|
|
|
[[package]]
|
|
|
|
|
name = "futures-sink"
|
feat: supports openai chat completions API (#1427)
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
2024-01-16 03:07:41 -07:00
|
|
|
version = "0.3.30"
|
2022-10-08 04:30:12 -06:00
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
feat: supports openai chat completions API (#1427)
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
2024-01-16 03:07:41 -07:00
|
|
|
checksum = "9fb8e00e87438d937621c1c6269e53f536c14d3fbd6a042bb24879e57d474fb5"
|
2022-10-08 04:30:12 -06:00
|
|
|
|
|
|
|
|
[[package]]
|
|
|
|
|
name = "futures-task"
|
feat: supports openai chat completions API (#1427)
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
2024-01-16 03:07:41 -07:00
|
|
|
version = "0.3.30"
|
2022-10-08 04:30:12 -06:00
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
feat: supports openai chat completions API (#1427)
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
2024-01-16 03:07:41 -07:00
|
|
|
checksum = "38d84fa142264698cdce1a9f9172cf383a0c82de1bddcf3092901442c4097004"
|
2022-10-08 04:30:12 -06:00
|
|
|
|
|
|
|
|
[[package]]
|
|
|
|
|
name = "futures-util"
|
feat: supports openai chat completions API (#1427)
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
2024-01-16 03:07:41 -07:00
|
|
|
version = "0.3.30"
|
2022-10-08 04:30:12 -06:00
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
feat: supports openai chat completions API (#1427)
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
2024-01-16 03:07:41 -07:00
|
|
|
checksum = "3d6401deb83407ab3da39eba7e33987a73c3df0c82b4bb5813ee871c19c41d48"
|
2022-10-08 04:30:12 -06:00
|
|
|
dependencies = [
|
|
|
|
|
"futures-channel",
|
|
|
|
|
"futures-core",
|
|
|
|
|
"futures-io",
|
|
|
|
|
"futures-macro",
|
|
|
|
|
"futures-sink",
|
|
|
|
|
"futures-task",
|
|
|
|
|
"memchr",
|
|
|
|
|
"pin-project-lite",
|
|
|
|
|
"pin-utils",
|
|
|
|
|
"slab",
|
|
|
|
|
]
|
|
|
|
|
|
2023-06-16 08:25:11 -06:00
|
|
|
[[package]]
|
|
|
|
|
name = "generational-arena"
|
|
|
|
|
version = "0.2.9"
|
|
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
|
|
|
|
checksum = "877e94aff08e743b651baaea359664321055749b398adff8740a7399af7796e7"
|
|
|
|
|
dependencies = [
|
|
|
|
|
"cfg-if",
|
|
|
|
|
]
|
|
|
|
|
|
2022-10-08 04:30:12 -06:00
|
|
|
[[package]]
|
|
|
|
|
name = "generic-array"
|
2023-04-09 12:22:27 -06:00
|
|
|
version = "0.14.7"
|
2022-10-08 04:30:12 -06:00
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
2023-04-09 12:22:27 -06:00
|
|
|
checksum = "85649ca51fd72272d7821adaf274ad91c288277713d9c18820d8499a7ff69e9a"
|
2022-10-08 04:30:12 -06:00
|
|
|
dependencies = [
|
|
|
|
|
"typenum",
|
|
|
|
|
"version_check",
|
|
|
|
|
]
|
|
|
|
|
|
|
|
|
|
[[package]]
|
|
|
|
|
name = "getrandom"
|
2024-01-26 11:04:57 -07:00
|
|
|
version = "0.2.12"
|
2022-10-08 04:30:12 -06:00
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
2024-01-26 11:04:57 -07:00
|
|
|
checksum = "190092ea657667030ac6a35e305e62fc4dd69fd98ac98631e5d3a2b1575a12b5"
|
2022-10-08 04:30:12 -06:00
|
|
|
dependencies = [
|
|
|
|
|
"cfg-if",
|
|
|
|
|
"libc",
|
2023-07-01 11:25:41 -06:00
|
|
|
"wasi",
|
2022-10-08 04:30:12 -06:00
|
|
|
]
|
|
|
|
|
|
2023-07-01 11:25:41 -06:00
|
|
|
[[package]]
|
|
|
|
|
name = "gimli"
|
2023-11-30 07:18:15 -07:00
|
|
|
version = "0.28.1"
|
2023-07-01 11:25:41 -06:00
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
2023-11-30 07:18:15 -07:00
|
|
|
checksum = "4271d37baee1b8c7e4b708028c57d816cf9d2434acb33a549475f78c181f6253"
|
2023-02-13 05:02:45 -07:00
|
|
|
|
|
|
|
|
[[package]]
|
|
|
|
|
name = "grpc-metadata"
|
2023-04-11 12:32:18 -06:00
|
|
|
version = "0.1.0"
|
2023-02-13 05:02:45 -07:00
|
|
|
dependencies = [
|
2023-09-27 02:40:18 -06:00
|
|
|
"opentelemetry",
|
2023-11-30 07:18:15 -07:00
|
|
|
"tonic 0.10.2",
|
2023-02-13 05:02:45 -07:00
|
|
|
"tracing",
|
2023-09-27 02:40:18 -06:00
|
|
|
"tracing-opentelemetry",
|
2023-02-13 05:02:45 -07:00
|
|
|
]
|
2022-10-08 04:30:12 -06:00
|
|
|
|
|
|
|
|
[[package]]
|
|
|
|
|
name = "h2"
|
2024-01-26 11:04:57 -07:00
|
|
|
version = "0.3.24"
|
2022-10-08 04:30:12 -06:00
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
2024-01-26 11:04:57 -07:00
|
|
|
checksum = "bb2c4422095b67ee78da96fbb51a4cc413b3b25883c7717ff7ca1ab31022c9c9"
|
2022-10-08 04:30:12 -06:00
|
|
|
dependencies = [
|
|
|
|
|
"bytes",
|
|
|
|
|
"fnv",
|
|
|
|
|
"futures-core",
|
|
|
|
|
"futures-sink",
|
|
|
|
|
"futures-util",
|
|
|
|
|
"http",
|
2023-11-30 07:18:15 -07:00
|
|
|
"indexmap 2.1.0",
|
2022-10-08 04:30:12 -06:00
|
|
|
"slab",
|
|
|
|
|
"tokio",
|
2023-02-13 05:02:45 -07:00
|
|
|
"tokio-util",
|
2022-10-08 04:30:12 -06:00
|
|
|
"tracing",
|
|
|
|
|
]
|
|
|
|
|
|
|
|
|
|
[[package]]
|
|
|
|
|
name = "hashbrown"
|
|
|
|
|
version = "0.12.3"
|
|
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
|
|
|
|
checksum = "8a9ee70c43aaf417c914396645a0fa852624801b24ebb7ae78fe8272889ac888"
|
2023-07-01 11:25:41 -06:00
|
|
|
|
|
|
|
|
[[package]]
|
|
|
|
|
name = "hashbrown"
|
2023-07-14 08:31:48 -06:00
|
|
|
version = "0.13.1"
|
2023-07-01 11:25:41 -06:00
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
2023-07-14 08:31:48 -06:00
|
|
|
checksum = "33ff8ae62cd3a9102e5637afc8452c55acf3844001bd5374e0b0bd7b6616c038"
|
2023-02-16 09:18:53 -07:00
|
|
|
dependencies = [
|
|
|
|
|
"ahash",
|
|
|
|
|
]
|
2022-10-08 04:30:12 -06:00
|
|
|
|
2023-07-14 08:31:48 -06:00
|
|
|
[[package]]
|
|
|
|
|
name = "hashbrown"
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"indexmap 1.9.3",
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"ipnet",
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"metrics",
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"metrics-util",
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"quanta",
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"thiserror",
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"tokio",
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"tracing",
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"metrics",
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"mime",
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"unicase",
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]
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|
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|
|
|
feat: supports openai chat completions API (#1427)
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
2024-01-16 03:07:41 -07:00
|
|
|
[[package]]
|
|
|
|
|
name = "minijinja"
|
2024-01-26 11:04:57 -07:00
|
|
|
version = "1.0.12"
|
feat: supports openai chat completions API (#1427)
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
2024-01-16 03:07:41 -07:00
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
2024-01-26 11:04:57 -07:00
|
|
|
checksum = "6fe0ff215195a22884d867b547c70a0c4815cbbcc70991f281dca604b20d10ce"
|
feat: supports openai chat completions API (#1427)
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
2024-01-16 03:07:41 -07:00
|
|
|
dependencies = [
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|
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|
|
"serde",
|
|
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|
]
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|
|
|
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|
2022-10-08 04:30:12 -06:00
|
|
|
[[package]]
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|
|
|
name = "minimal-lexical"
|
|
|
|
|
version = "0.2.1"
|
|
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
|
|
|
|
checksum = "68354c5c6bd36d73ff3feceb05efa59b6acb7626617f4962be322a825e61f79a"
|
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[[package]]
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name = "miniz_oxide"
|
2023-06-16 08:25:11 -06:00
|
|
|
version = "0.7.1"
|
2022-10-08 04:30:12 -06:00
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
2023-06-16 08:25:11 -06:00
|
|
|
checksum = "e7810e0be55b428ada41041c41f32c9f1a42817901b4ccf45fa3d4b6561e74c7"
|
2022-10-08 04:30:12 -06:00
|
|
|
dependencies = [
|
|
|
|
|
"adler",
|
|
|
|
|
]
|
|
|
|
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|
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[[package]]
|
|
|
|
|
name = "mio"
|
2023-12-11 06:55:03 -07:00
|
|
|
version = "0.8.10"
|
2022-10-08 04:30:12 -06:00
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
2023-12-11 06:55:03 -07:00
|
|
|
checksum = "8f3d0b296e374a4e6f3c7b0a1f5a51d748a0d34c85e7dc48fc3fa9a87657fe09"
|
2022-10-08 04:30:12 -06:00
|
|
|
dependencies = [
|
|
|
|
|
"libc",
|
|
|
|
|
"log",
|
2023-07-01 11:25:41 -06:00
|
|
|
"wasi",
|
2023-06-16 08:25:11 -06:00
|
|
|
"windows-sys 0.48.0",
|
2022-10-08 04:30:12 -06:00
|
|
|
]
|
|
|
|
|
|
2023-04-09 12:22:27 -06:00
|
|
|
[[package]]
|
|
|
|
|
name = "monostate"
|
2024-01-26 11:04:57 -07:00
|
|
|
version = "0.1.11"
|
2023-04-09 12:22:27 -06:00
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
2024-01-26 11:04:57 -07:00
|
|
|
checksum = "878c2a1f1c70e5724fa28f101ca787b6a7e8ad5c5e4ae4ca3b0fa4a419fa9075"
|
2023-04-09 12:22:27 -06:00
|
|
|
dependencies = [
|
|
|
|
|
"monostate-impl",
|
|
|
|
|
"serde",
|
|
|
|
|
]
|
|
|
|
|
|
|
|
|
|
[[package]]
|
|
|
|
|
name = "monostate-impl"
|
2024-01-26 11:04:57 -07:00
|
|
|
version = "0.1.11"
|
2023-04-09 12:22:27 -06:00
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
2024-01-26 11:04:57 -07:00
|
|
|
checksum = "f686d68a09079e63b1d2c64aa305095887ce50565f00a922ebfaeeee0d9ba6ce"
|
2023-04-09 12:22:27 -06:00
|
|
|
dependencies = [
|
|
|
|
|
"proc-macro2",
|
|
|
|
|
"quote",
|
2024-01-26 11:04:57 -07:00
|
|
|
"syn 2.0.48",
|
2023-04-09 12:22:27 -06:00
|
|
|
]
|
|
|
|
|
|
2022-10-08 04:30:12 -06:00
|
|
|
[[package]]
|
|
|
|
|
name = "multimap"
|
|
|
|
|
version = "0.8.3"
|
|
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
|
|
|
|
checksum = "e5ce46fe64a9d73be07dcbe690a38ce1b293be448fd8ce1e6c1b8062c9f72c6a"
|
|
|
|
|
|
2023-06-16 08:25:11 -06:00
|
|
|
[[package]]
|
|
|
|
|
name = "muxado"
|
|
|
|
|
version = "0.4.0"
|
|
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
|
|
|
|
checksum = "e92b89ac3127251efde6f5a9586e5aae99468d06fcf9f133b377f58d5ed66446"
|
|
|
|
|
dependencies = [
|
|
|
|
|
"async-trait",
|
|
|
|
|
"awaitdrop",
|
2023-07-01 11:25:41 -06:00
|
|
|
"bitflags 1.3.2",
|
2023-06-16 08:25:11 -06:00
|
|
|
"bytes",
|
|
|
|
|
"futures",
|
|
|
|
|
"pin-project",
|
|
|
|
|
"rand",
|
|
|
|
|
"thiserror",
|
|
|
|
|
"tokio",
|
|
|
|
|
"tokio-util",
|
|
|
|
|
"tracing",
|
|
|
|
|
]
|
|
|
|
|
|
2022-10-08 04:30:12 -06:00
|
|
|
[[package]]
|
|
|
|
|
name = "native-tls"
|
2022-11-09 10:24:07 -07:00
|
|
|
version = "0.2.11"
|
2022-10-08 04:30:12 -06:00
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
2022-11-09 10:24:07 -07:00
|
|
|
checksum = "07226173c32f2926027b63cce4bcd8076c3552846cbe7925f3aaffeac0a3b92e"
|
2022-10-08 04:30:12 -06:00
|
|
|
dependencies = [
|
|
|
|
|
"lazy_static",
|
|
|
|
|
"libc",
|
|
|
|
|
"log",
|
|
|
|
|
"openssl",
|
|
|
|
|
"openssl-probe",
|
|
|
|
|
"openssl-sys",
|
|
|
|
|
"schannel",
|
|
|
|
|
"security-framework",
|
|
|
|
|
"security-framework-sys",
|
|
|
|
|
"tempfile",
|
|
|
|
|
]
|
|
|
|
|
|
2023-06-16 08:25:11 -06:00
|
|
|
[[package]]
|
|
|
|
|
name = "ngrok"
|
2023-09-27 02:40:18 -06:00
|
|
|
version = "0.13.1"
|
2023-06-16 08:25:11 -06:00
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
2023-09-27 02:40:18 -06:00
|
|
|
checksum = "1454b1edbc5f2c8ff3242c237cb84388b50eced8eb26b4204e49698ed6511784"
|
2023-06-16 08:25:11 -06:00
|
|
|
dependencies = [
|
|
|
|
|
"arc-swap",
|
|
|
|
|
"async-rustls",
|
|
|
|
|
"async-trait",
|
|
|
|
|
"awaitdrop",
|
|
|
|
|
"axum",
|
|
|
|
|
"base64 0.13.1",
|
|
|
|
|
"bytes",
|
|
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|
|
"futures",
|
|
|
|
|
"hostname",
|
|
|
|
|
"hyper",
|
|
|
|
|
"muxado",
|
|
|
|
|
"once_cell",
|
|
|
|
|
"parking_lot",
|
|
|
|
|
"regex",
|
|
|
|
|
"rustls-pemfile",
|
|
|
|
|
"serde",
|
|
|
|
|
"serde_json",
|
|
|
|
|
"thiserror",
|
|
|
|
|
"tokio",
|
|
|
|
|
"tokio-retry",
|
|
|
|
|
"tokio-util",
|
|
|
|
|
"tracing",
|
|
|
|
|
"windows-sys 0.45.0",
|
|
|
|
|
]
|
|
|
|
|
|
2022-10-18 07:19:03 -06:00
|
|
|
[[package]]
|
|
|
|
|
name = "nix"
|
2023-09-27 02:40:18 -06:00
|
|
|
version = "0.27.1"
|
2022-10-18 07:19:03 -06:00
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
2023-09-27 02:40:18 -06:00
|
|
|
checksum = "2eb04e9c688eff1c89d72b407f168cf79bb9e867a9d3323ed6c01519eb9cc053"
|
2022-10-18 07:19:03 -06:00
|
|
|
dependencies = [
|
2024-01-26 11:04:57 -07:00
|
|
|
"bitflags 2.4.2",
|
2022-10-18 07:19:03 -06:00
|
|
|
"cfg-if",
|
|
|
|
|
"libc",
|
|
|
|
|
]
|
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|
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|
2023-01-26 08:29:13 -07:00
|
|
|
[[package]]
|
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|
|
|
name = "nohash-hasher"
|
|
|
|
|
version = "0.2.0"
|
|
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
|
|
|
|
checksum = "2bf50223579dc7cdcfb3bfcacf7069ff68243f8c363f62ffa99cf000a6b9c451"
|
|
|
|
|
|
2022-10-08 04:30:12 -06:00
|
|
|
[[package]]
|
|
|
|
|
name = "nom"
|
2023-02-13 05:02:45 -07:00
|
|
|
version = "7.1.3"
|
2022-10-08 04:30:12 -06:00
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
2023-02-13 05:02:45 -07:00
|
|
|
checksum = "d273983c5a657a70a3e8f2a01329822f3b8c8172b73826411a55751e404a0a4a"
|
2022-10-08 04:30:12 -06:00
|
|
|
dependencies = [
|
|
|
|
|
"memchr",
|
|
|
|
|
"minimal-lexical",
|
|
|
|
|
]
|
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|
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|
2023-05-02 07:43:19 -06:00
|
|
|
[[package]]
|
|
|
|
|
name = "ntapi"
|
|
|
|
|
version = "0.4.1"
|
|
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
|
|
|
|
checksum = "e8a3895c6391c39d7fe7ebc444a87eb2991b2a0bc718fdabd071eec617fc68e4"
|
|
|
|
|
dependencies = [
|
|
|
|
|
"winapi",
|
|
|
|
|
]
|
|
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|
2022-10-18 07:19:03 -06:00
|
|
|
[[package]]
|
|
|
|
|
name = "nu-ansi-term"
|
|
|
|
|
version = "0.46.0"
|
|
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
|
|
|
|
checksum = "77a8165726e8236064dbb45459242600304b42a5ea24ee2948e18e023bf7ba84"
|
|
|
|
|
dependencies = [
|
|
|
|
|
"overload",
|
|
|
|
|
"winapi",
|
|
|
|
|
]
|
|
|
|
|
|
2023-05-09 05:19:31 -06:00
|
|
|
[[package]]
|
|
|
|
|
name = "num-traits"
|
2023-11-30 07:18:15 -07:00
|
|
|
version = "0.2.17"
|
2023-05-09 05:19:31 -06:00
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
2023-11-30 07:18:15 -07:00
|
|
|
checksum = "39e3200413f237f41ab11ad6d161bc7239c84dcb631773ccd7de3dfe4b5c267c"
|
2023-05-09 05:19:31 -06:00
|
|
|
dependencies = [
|
|
|
|
|
"autocfg",
|
|
|
|
|
"libm",
|
|
|
|
|
]
|
|
|
|
|
|
2022-10-08 04:30:12 -06:00
|
|
|
[[package]]
|
|
|
|
|
name = "num_cpus"
|
2023-07-01 11:25:41 -06:00
|
|
|
version = "1.16.0"
|
2022-10-08 04:30:12 -06:00
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
2023-07-01 11:25:41 -06:00
|
|
|
checksum = "4161fcb6d602d4d2081af7c3a45852d875a03dd337a6bfdd6e06407b61342a43"
|
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|
"clap",
|
2022-10-18 07:19:03 -06:00
|
|
|
"ctrlc",
|
2022-12-16 03:29:36 -07:00
|
|
|
"float_eq",
|
2023-07-13 06:22:37 -06:00
|
|
|
"nix",
|
2022-12-16 03:29:36 -07:00
|
|
|
"reqwest",
|
|
|
|
|
"serde",
|
|
|
|
|
"serde_json",
|
2022-10-18 07:19:03 -06:00
|
|
|
"tracing",
|
|
|
|
|
"tracing-subscriber",
|
2023-05-02 07:43:19 -06:00
|
|
|
"vergen",
|
2022-10-18 07:19:03 -06:00
|
|
|
]
|
|
|
|
|
|
2022-10-17 06:59:00 -06:00
|
|
|
[[package]]
|
|
|
|
|
name = "text-generation-router"
|
2024-01-26 11:04:57 -07:00
|
|
|
version = "1.4.0"
|
2022-10-17 06:59:00 -06:00
|
|
|
dependencies = [
|
2023-01-31 09:04:00 -07:00
|
|
|
"async-stream",
|
2022-10-17 06:59:00 -06:00
|
|
|
"axum",
|
2023-02-13 05:02:45 -07:00
|
|
|
"axum-tracing-opentelemetry",
|
2023-04-09 12:22:27 -06:00
|
|
|
"clap",
|
2022-10-17 06:59:00 -06:00
|
|
|
"futures",
|
feat: supports openai chat completions API (#1427)
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
2024-01-16 03:07:41 -07:00
|
|
|
"futures-util",
|
2023-11-30 07:18:15 -07:00
|
|
|
"hf-hub",
|
2023-09-27 02:40:18 -06:00
|
|
|
"init-tracing-opentelemetry",
|
2023-02-16 09:18:53 -07:00
|
|
|
"metrics",
|
|
|
|
|
"metrics-exporter-prometheus",
|
feat: supports openai chat completions API (#1427)
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
2024-01-16 03:07:41 -07:00
|
|
|
"minijinja",
|
2023-06-16 08:25:11 -06:00
|
|
|
"ngrok",
|
2023-01-26 08:29:13 -07:00
|
|
|
"nohash-hasher",
|
2023-09-27 02:40:18 -06:00
|
|
|
"opentelemetry",
|
2023-02-13 05:02:45 -07:00
|
|
|
"opentelemetry-otlp",
|
2023-01-31 08:01:15 -07:00
|
|
|
"rand",
|
2023-02-28 02:19:32 -07:00
|
|
|
"reqwest",
|
2022-10-17 06:59:00 -06:00
|
|
|
"serde",
|
|
|
|
|
"serde_json",
|
2022-10-28 11:24:00 -06:00
|
|
|
"text-generation-client",
|
2022-10-17 06:59:00 -06:00
|
|
|
"thiserror",
|
2024-02-08 10:41:25 -07:00
|
|
|
"tokenizers 0.15.1",
|
2022-10-17 06:59:00 -06:00
|
|
|
"tokio",
|
2023-10-23 07:51:12 -06:00
|
|
|
"tokio-stream",
|
2023-09-27 02:40:18 -06:00
|
|
|
"tower-http",
|
2022-10-17 06:59:00 -06:00
|
|
|
"tracing",
|
2023-09-27 02:40:18 -06:00
|
|
|
"tracing-opentelemetry",
|
2022-10-17 06:59:00 -06:00
|
|
|
"tracing-subscriber",
|
2023-02-03 04:43:37 -07:00
|
|
|
"utoipa",
|
|
|
|
|
"utoipa-swagger-ui",
|
2023-04-18 08:16:06 -06:00
|
|
|
"vergen",
|
2022-10-17 06:59:00 -06:00
|
|
|
]
|
|
|
|
|
|
2022-10-08 04:30:12 -06:00
|
|
|
[[package]]
|
|
|
|
|
name = "thiserror"
|
2024-01-26 11:04:57 -07:00
|
|
|
version = "1.0.56"
|
2022-10-08 04:30:12 -06:00
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
2024-01-26 11:04:57 -07:00
|
|
|
checksum = "d54378c645627613241d077a3a79db965db602882668f9136ac42af9ecb730ad"
|
2022-10-08 04:30:12 -06:00
|
|
|
dependencies = [
|
|
|
|
|
"thiserror-impl",
|
|
|
|
|
]
|
|
|
|
|
|
|
|
|
|
[[package]]
|
|
|
|
|
name = "thiserror-impl"
|
2024-01-26 11:04:57 -07:00
|
|
|
version = "1.0.56"
|
2022-10-08 04:30:12 -06:00
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
2024-01-26 11:04:57 -07:00
|
|
|
checksum = "fa0faa943b50f3db30a20aa7e265dbc66076993efed8463e8de414e5d06d3471"
|
2022-10-08 04:30:12 -06:00
|
|
|
dependencies = [
|
|
|
|
|
"proc-macro2",
|
|
|
|
|
"quote",
|
2024-01-26 11:04:57 -07:00
|
|
|
"syn 2.0.48",
|
2022-10-08 04:30:12 -06:00
|
|
|
]
|
|
|
|
|
|
|
|
|
|
[[package]]
|
|
|
|
|
name = "thread_local"
|
2023-02-24 05:27:41 -07:00
|
|
|
version = "1.1.7"
|
2022-10-08 04:30:12 -06:00
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
2023-02-24 05:27:41 -07:00
|
|
|
checksum = "3fdd6f064ccff2d6567adcb3873ca630700f00b5ad3f060c25b5dcfd9a4ce152"
|
2022-10-08 04:30:12 -06:00
|
|
|
dependencies = [
|
2023-02-13 05:02:45 -07:00
|
|
|
"cfg-if",
|
2022-10-08 04:30:12 -06:00
|
|
|
"once_cell",
|
|
|
|
|
]
|
|
|
|
|
|
|
|
|
|
[[package]]
|
|
|
|
|
name = "time"
|
2023-12-22 07:46:04 -07:00
|
|
|
version = "0.3.31"
|
2022-10-08 04:30:12 -06:00
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
2023-12-22 07:46:04 -07:00
|
|
|
checksum = "f657ba42c3f86e7680e53c8cd3af8abbe56b5491790b46e22e19c0d57463583e"
|
2022-10-08 04:30:12 -06:00
|
|
|
dependencies = [
|
2023-08-14 11:26:19 -06:00
|
|
|
"deranged",
|
2023-04-18 08:16:06 -06:00
|
|
|
"itoa",
|
2023-07-14 08:31:48 -06:00
|
|
|
"libc",
|
|
|
|
|
"num_threads",
|
2023-11-30 07:18:15 -07:00
|
|
|
"powerfmt",
|
2023-04-09 12:22:27 -06:00
|
|
|
"serde",
|
|
|
|
|
"time-core",
|
2023-04-18 08:16:06 -06:00
|
|
|
"time-macros",
|
2022-10-08 04:30:12 -06:00
|
|
|
]
|
|
|
|
|
|
2023-04-09 12:22:27 -06:00
|
|
|
[[package]]
|
|
|
|
|
name = "time-core"
|
2023-09-27 02:40:18 -06:00
|
|
|
version = "0.1.2"
|
2023-04-09 12:22:27 -06:00
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
2023-09-27 02:40:18 -06:00
|
|
|
checksum = "ef927ca75afb808a4d64dd374f00a2adf8d0fcff8e7b184af886c3c87ec4a3f3"
|
2023-04-09 12:22:27 -06:00
|
|
|
|
2023-04-18 08:16:06 -06:00
|
|
|
[[package]]
|
|
|
|
|
name = "time-macros"
|
2023-12-22 07:46:04 -07:00
|
|
|
version = "0.2.16"
|
2023-04-18 08:16:06 -06:00
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
2023-12-22 07:46:04 -07:00
|
|
|
checksum = "26197e33420244aeb70c3e8c78376ca46571bc4e701e4791c2cd9f57dcb3a43f"
|
2023-04-18 08:16:06 -06:00
|
|
|
dependencies = [
|
|
|
|
|
"time-core",
|
|
|
|
|
]
|
|
|
|
|
|
2022-10-08 04:30:12 -06:00
|
|
|
[[package]]
|
|
|
|
|
name = "tinyvec"
|
|
|
|
|
version = "1.6.0"
|
|
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
|
|
|
|
checksum = "87cc5ceb3875bb20c2890005a4e226a4651264a5c75edb2421b52861a0a0cb50"
|
|
|
|
|
dependencies = [
|
|
|
|
|
"tinyvec_macros",
|
|
|
|
|
]
|
|
|
|
|
|
|
|
|
|
[[package]]
|
|
|
|
|
name = "tinyvec_macros"
|
2023-02-13 05:02:45 -07:00
|
|
|
version = "0.1.1"
|
2022-10-08 04:30:12 -06:00
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
2023-02-13 05:02:45 -07:00
|
|
|
checksum = "1f3ccbac311fea05f86f61904b462b55fb3df8837a366dfc601a0161d0532f20"
|
2022-10-08 04:30:12 -06:00
|
|
|
|
|
|
|
|
[[package]]
|
|
|
|
|
name = "tokenizers"
|
2023-11-30 07:18:15 -07:00
|
|
|
version = "0.14.1"
|
2022-10-08 04:30:12 -06:00
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
2023-11-30 07:18:15 -07:00
|
|
|
checksum = "d9be88c795d8b9f9c4002b3a8f26a6d0876103a6f523b32ea3bac52d8560c17c"
|
2022-10-08 04:30:12 -06:00
|
|
|
dependencies = [
|
2023-11-30 07:18:15 -07:00
|
|
|
"aho-corasick",
|
2023-04-09 12:22:27 -06:00
|
|
|
"clap",
|
2022-10-08 04:30:12 -06:00
|
|
|
"derive_builder",
|
|
|
|
|
"esaxx-rs",
|
|
|
|
|
"getrandom",
|
2023-11-30 07:18:15 -07:00
|
|
|
"hf-hub",
|
|
|
|
|
"indicatif",
|
|
|
|
|
"itertools 0.11.0",
|
2024-02-08 10:41:25 -07:00
|
|
|
"lazy_static",
|
|
|
|
|
"log",
|
|
|
|
|
"macro_rules_attribute",
|
|
|
|
|
"monostate",
|
|
|
|
|
"onig",
|
|
|
|
|
"paste",
|
|
|
|
|
"rand",
|
|
|
|
|
"rayon",
|
|
|
|
|
"rayon-cond",
|
|
|
|
|
"regex",
|
|
|
|
|
"regex-syntax 0.7.5",
|
|
|
|
|
"serde",
|
|
|
|
|
"serde_json",
|
|
|
|
|
"spm_precompiled",
|
|
|
|
|
"thiserror",
|
|
|
|
|
"unicode-normalization-alignments",
|
|
|
|
|
"unicode-segmentation",
|
|
|
|
|
"unicode_categories",
|
|
|
|
|
]
|
|
|
|
|
|
|
|
|
|
[[package]]
|
|
|
|
|
name = "tokenizers"
|
|
|
|
|
version = "0.15.1"
|
|
|
|
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
|
|
|
|
checksum = "6db445cceba5dfeb0f9702be7d6bfd91801ddcbe8fe8722defe7f2e96da75812"
|
|
|
|
|
dependencies = [
|
|
|
|
|
"aho-corasick",
|
|
|
|
|
"clap",
|
|
|
|
|
"derive_builder",
|
|
|
|
|
"esaxx-rs",
|
|
|
|
|
"getrandom",
|
|
|
|
|
"hf-hub",
|
|
|
|
|
"indicatif",
|
|
|
|
|
"itertools 0.11.0",
|
2022-10-08 04:30:12 -06:00
|
|
|
"lazy_static",
|
|
|
|
|
"log",
|
|
|
|
|
"macro_rules_attribute",
|
2023-04-09 12:22:27 -06:00
|
|
|
"monostate",
|
2022-10-08 04:30:12 -06:00
|
|
|
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