VLLM Inference Acceleration and Deployment

Environment Preparation

GPU devices: A10, 3090, V100, A100 are all supported.

# Set pip global mirror (speeds up downloads)
pip config set global.index-url https://mirrors.aliyun.com/pypi/simple/
# Install ms-swift
pip install 'ms-swift[llm]' -U

# vllm version corresponds to cuda version, please select version according to `https://docs.vllm.ai/en/latest/getting_started/installation.html`
pip install vllm
pip install openai -U

# Environment alignment (usually not needed. If you get errors, you can run the code below, the repo uses the latest environment for testing)
pip install -r requirements/framework.txt -U
pip install -r requirements/llm.txt -U

Inference Acceleration

vllm does not support bnb quantized models. The models supported by vllm can be found in Supported Models.

qwen-7b-chat

import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'

from swift.llm import (
    ModelType, get_vllm_engine, get_default_template_type,
    get_template, inference_vllm
)

model_type = ModelType.qwen_7b_chat
llm_engine = get_vllm_engine(model_type)
template_type = get_default_template_type(model_type)
template = get_template(template_type, llm_engine.hf_tokenizer)
# Similar to `transformers.GenerationConfig` interface
llm_engine.generation_config.max_new_tokens = 256

request_list = [{'query': 'Hello!'}, {'query': 'Where is the capital of Zhejiang?'} ]
resp_list = inference_vllm(llm_engine, template, request_list)
for request, resp in zip(request_list, resp_list):
    print(f"query: {request['query']}")
    print(f"response: {resp['response']}")

history1 = resp_list[1]['history']
request_list = [{'query': 'What delicious food is there', 'history': history1}]
resp_list = inference_vllm(llm_engine, template, request_list)
for request, resp in zip(request_list, resp_list):
    print(f"query: {request['query']}")
    print(f"response: {resp['response']}")
    print(f"history: {resp['history']}")

"""Out[0]
query: Hello!
response: Hello! I'm happy to be of service. Is there anything I can help you with?
query: Where is the capital of Zhejiang?
response: Hangzhou is the capital of Zhejiang Province.
query: What delicious food is there
response: Hangzhou is a city of gastronomy, with many famous dishes and snacks such as West Lake Vinegar Fish, Dongpo Pork, Beggar's Chicken, etc. In addition, Hangzhou has many snack shops where you can taste a variety of local delicacies.
history: [('Where is the capital of Zhejiang?', 'Hangzhou is the capital of Zhejiang Province.'), ('What delicious food is there', "Hangzhou is a city of gastronomy, with many famous dishes and snacks such as West Lake Vinegar Fish, Dongpo Pork, Beggar's Chicken, etc. In addition, Hangzhou has many snack shops where you can taste a variety of local delicacies.")]
"""

Streaming Output

import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'

from swift.llm import (
    ModelType, get_vllm_engine, get_default_template_type,
    get_template, inference_stream_vllm
)

model_type = ModelType.qwen_7b_chat
llm_engine = get_vllm_engine(model_type)
template_type = get_default_template_type(model_type)
template = get_template(template_type, llm_engine.hf_tokenizer)
# Similar to `transformers.GenerationConfig` interface
llm_engine.generation_config.max_new_tokens = 256

request_list = [{'query': 'Hello!'}, {'query': 'Where is the capital of Zhejiang?'}]
gen = inference_stream_vllm(llm_engine, template, request_list)
query_list = [request['query'] for request in request_list]
print(f"query_list: {query_list}")
for resp_list in gen:
    response_list = [resp['response'] for resp in resp_list]
    print(f'response_list: {response_list}')

history1 = resp_list[1]['history']
request_list = [{'query': 'What delicious food is there', 'history': history1}]
gen = inference_stream_vllm(llm_engine, template, request_list)
query = request_list[0]['query']
print(f"query: {query}")
for resp_list in gen:
    response = resp_list[0]['response']
    print(f'response: {response}')

history = resp_list[0]['history']
print(f'history: {history}')

"""Out[0]
query_list: ['Hello!', 'Where is the capital of Zhejiang?']
...
response_list: ['Hello! I'm happy to be of service. Is there anything I can help you with?', 'Hangzhou is the capital of Zhejiang Province.']
query: What delicious food is there
...
response: Hangzhou is a city of gastronomy, with many famous dishes and snacks such as West Lake Vinegar Fish, Dongpo Pork, Beggar's Chicken, etc. In addition, Hangzhou has many snack shops where you can taste a variety of local delicacies.
history: [('Where is the capital of Zhejiang?', 'Hangzhou is the capital of Zhejiang Province.'), ('What delicious food is there', "Hangzhou is a city of gastronomy, with many famous dishes and snacks such as West Lake Vinegar Fish, Dongpo Pork, Beggar's Chicken, etc. In addition, Hangzhou has many snack shops where you can taste a variety of local delicacies.")]
"""

chatglm3

import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'

from swift.llm import (
    ModelType, get_vllm_engine, get_default_template_type,
    get_template, inference_vllm
)

model_type = ModelType.chatglm3_6b
llm_engine = get_vllm_engine(model_type)
template_type = get_default_template_type(model_type)
template = get_template(template_type, llm_engine.hf_tokenizer)
# Similar to `transformers.GenerationConfig` interface
llm_engine.generation_config.max_new_tokens = 256

request_list = [{'query': 'Hello!'}, {'query': 'Where is the capital of Zhejiang?'}]
resp_list = inference_vllm(llm_engine, template, request_list)
for request, resp in zip(request_list, resp_list):
    print(f"query: {request['query']}")
    print(f"response: {resp['response']}")

history1 = resp_list[1]['history']
request_list = [{'query': 'What delicious food is there', 'history': history1}]
resp_list = inference_vllm(llm_engine, template, request_list)
for request, resp in zip(request_list, resp_list):
    print(f"query: {request['query']}")
    print(f"response: {resp['response']}")
    print(f"history: {resp['history']}")

"""Out[0]
query: Hello!
response: Hello, I am an AI assistant. I'm very pleased to serve you! Do you have any questions I can help you answer?
query: Where is the capital of Zhejiang?
response: The capital of Zhejiang is Hangzhou.
query: What delicious food is there
response: Zhejiang has many delicious foods, some of the most famous ones include Longjing Shrimp from Hangzhou, Dongpo Pork, West Lake Vinegar Fish, Beggar's Chicken, etc. In addition, Zhejiang also has many specialty snacks and pastries, such as Tang Yuan and Nian Gao from Ningbo, stir-fried crab and Wenzhou meatballs from Wenzhou, etc.
history: [('Where is the capital of Zhejiang?', 'The capital of Zhejiang is Hangzhou.'), ('What delicious food is there', 'Zhejiang has many delicious foods, some of the most famous ones include Longjing Shrimp from Hangzhou, Dongpo Pork, West Lake Vinegar Fish, Beggar's Chicken, etc. In addition, Zhejiang also has many specialty snacks and pastries, such as Tang Yuan and Nian Gao from Ningbo, stir-fried crab and Wenzhou meatballs from Wenzhou, etc.')]
"""

Using CLI

# qwen
CUDA_VISIBLE_DEVICES=0 swift infer --model_type qwen-7b-chat --infer_backend vllm
# yi
CUDA_VISIBLE_DEVICES=0 swift infer --model_type yi-6b-chat --infer_backend vllm
# gptq
CUDA_VISIBLE_DEVICES=0 swift infer --model_type qwen1half-7b-chat-int4 --infer_backend vllm

Fine-tuned Models

Single sample inference:

For models fine-tuned using LoRA, you need to first merge-lora to generate a complete checkpoint directory.

Models fine-tuned with full parameters can seamlessly use VLLM for inference acceleration.

import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'

from swift.llm import (
    ModelType, get_vllm_engine, get_default_template_type,
    get_template, inference_vllm
)

ckpt_dir = 'vx-xxx/checkpoint-100-merged'
model_type = ModelType.qwen_7b_chat
template_type = get_default_template_type(model_type)

llm_engine = get_vllm_engine(model_type, model_id_or_path=ckpt_dir)
tokenizer = llm_engine.hf_tokenizer
template = get_template(template_type, tokenizer)
query = 'Hello'
resp = inference_vllm(llm_engine, template, [{'query': query}])[0]
print(f"response: {resp['response']}")
print(f"history: {resp['history']}")

Using CLI:

# merge LoRA incremental weights and use vllm for inference acceleration
# If you need quantization, you can specify `--quant_bits 4`.
CUDA_VISIBLE_DEVICES=0 swift export \
    --ckpt_dir 'xxx/vx-xxx/checkpoint-xxx' --merge_lora true

# Evaluate using dataset
CUDA_VISIBLE_DEVICES=0 swift infer \
    --ckpt_dir 'xxx/vx-xxx/checkpoint-xxx-merged' \
    --infer_backend vllm \
    --load_dataset_config true \

# Manual evaluation
CUDA_VISIBLE_DEVICES=0 swift infer \
    --ckpt_dir 'xxx/vx-xxx/checkpoint-xxx-merged' \
    --infer_backend vllm \

Web-UI Acceleration

Original Models

CUDA_VISIBLE_DEVICES=0 swift app-ui --model_type qwen-7b-chat --infer_backend vllm

Fine-tuned Models

# merge LoRA incremental weights and use vllm as backend to build app-ui
# If you need quantization, you can specify `--quant_bits 4`.
CUDA_VISIBLE_DEVICES=0 swift export \
    --ckpt_dir 'xxx/vx-xxx/checkpoint-xxx' --merge_lora true

CUDA_VISIBLE_DEVICES=0 swift app-ui --ckpt_dir 'xxx/vx-xxx/checkpoint-xxx-merged' --infer_backend vllm

Deployment

Swift uses VLLM as the inference backend and is compatible with the OpenAI API style.

For server deployment command line arguments, refer to: deploy command line arguments.

For OpenAI API arguments on the client side, refer to: https://platform.openai.com/docs/api-reference/introduction.

Original Models

qwen-7b-chat

Server side:

CUDA_VISIBLE_DEVICES=0 swift deploy --model_type qwen-7b-chat
# Multi-GPU deployment
RAY_memory_monitor_refresh_ms=0 CUDA_VISIBLE_DEVICES=0,1,2,3 swift deploy --model_type qwen-7b-chat --tensor_parallel_size 4

Client side:

Test:

curl http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "qwen-7b-chat",
"messages": [{"role": "user", "content": "What to do if I can't fall asleep at night?"}],
"max_tokens": 256,
"temperature": 0
}'

Using swift:

from swift.llm import get_model_list_client, XRequestConfig, inference_client

model_list = get_model_list_client()
model_type = model_list.data[0].id
print(f'model_type: {model_type}')

query = 'Where is the capital of Zhejiang?'
request_config = XRequestConfig(seed=42)
resp = inference_client(model_type, query, request_config=request_config)
response = resp.choices[0].message.content
print(f'query: {query}')
print(f'response: {response}')

history = [(query, response)]
query = 'What delicious food is there?'
request_config = XRequestConfig(stream=True, seed=42)
stream_resp = inference_client(model_type, query, history, request_config=request_config)
print(f'query: {query}')
print('response: ', end='')
for chunk in stream_resp:
    print(chunk.choices[0].delta.content, end='', flush=True)
print()

"""Out[0]
model_type: qwen-7b-chat
query: Where is the capital of Zhejiang?
response: The capital of Zhejiang Province is Hangzhou.
query: What delicious food is there?
response: Hangzhou has many delicious foods, such as West Lake Vinegar Fish, Dongpo Pork, Longjing Shrimp, Beggar's Chicken, etc. In addition, Hangzhou also has many specialty snacks, such as West Lake Lotus Root Powder, Hangzhou Xiao Long Bao, Hangzhou You Tiao, etc.
"""

Using openai:

from openai import OpenAI
client = OpenAI(
    api_key='EMPTY',
    base_url='http://localhost:8000/v1',
)
model_type = client.models.list().data[0].id
print(f'model_type: {model_type}')

query = 'Where is the capital of Zhejiang?'
messages = [{
    'role': 'user',
    'content': query
}]
resp = client.chat.completions.create(
    model=model_type,
    messages=messages,
    seed=42)
response = resp.choices[0].message.content
print(f'query: {query}')
print(f'response: {response}')

# Streaming
messages.append({'role': 'assistant', 'content': response})
query = 'What delicious food is there?'
messages.append({'role': 'user', 'content': query})
stream_resp = client.chat.completions.create(
    model=model_type,
    messages=messages,
    stream=True,
    seed=42)

print(f'query: {query}')
print('response: ', end='')
for chunk in stream_resp:
    print(chunk.choices[0].delta.content, end='', flush=True)
print()

"""Out[0]
model_type: qwen-7b-chat
query: Where is the capital of Zhejiang?
response: The capital of Zhejiang Province is Hangzhou.
query: What delicious food is there?
response: Hangzhou has many delicious foods, such as West Lake Vinegar Fish, Dongpo Pork, Longjing Shrimp, Beggar's Chicken, etc. In addition, Hangzhou also has many specialty snacks, such as West Lake Lotus Root Powder, Hangzhou Xiao Long Bao, Hangzhou You Tiao, etc.
"""

qwen-7b

Server side:

CUDA_VISIBLE_DEVICES=0 swift deploy --model_type qwen-7b
# Multi-GPU deployment
RAY_memory_monitor_refresh_ms=0 CUDA_VISIBLE_DEVICES=0,1,2,3 swift deploy --model_type qwen-7b --tensor_parallel_size 4

Client side:

Test:

curl http://localhost:8000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "qwen-7b",
"prompt": "Zhejiang -> Hangzhou\nAnhui -> Hefei\nSichuan ->",
"max_tokens": 32,
"temperature": 0.1,
"seed": 42
}'

Using swift:

from swift.llm import get_model_list_client, XRequestConfig, inference_client

model_list = get_model_list_client()
model_type = model_list.data[0].id
print(f'model_type: {model_type}')

query = 'Zhejiang -> Hangzhou\nAnhui -> Hefei\nSichuan ->'
request_config = XRequestConfig(max_tokens=32, temperature=0.1, seed=42)
resp = inference_client(model_type, query, request_config=request_config)
response = resp.choices[0].text
print(f'query: {query}')
print(f'response: {response}')

request_config.stream = True
stream_resp = inference_client(model_type, query, request_config=request_config)
print(f'query: {query}')
print('response: ', end='')
for chunk in stream_resp:
    print(chunk.choices[0].text, end='', flush=True)
print()

"""Out[0]
model_type: qwen-7b
query: Zhejiang -> Hangzhou
Anhui -> Hefei
Sichuan ->
response:  Chengdu
Guangdong -> Guangzhou
Jiangsu -> Nanjing
Zhejiang -> Hangzhou
Anhui -> Hefei
Sichuan -> Chengdu

query: Zhejiang -> Hangzhou
Anhui -> Hefei
Sichuan ->
response:  Chengdu
Guangdong -> Guangzhou
Jiangsu -> Nanjing
Zhejiang -> Hangzhou
Anhui -> Hefei
Sichuan -> Chengdu
"""

Using openai:

from openai import OpenAI
client = OpenAI(
    api_key='EMPTY',
    base_url='http://localhost:8000/v1',
)
model_type = client.models.list().data[0].id
print(f'model_type: {model_type}')

query = 'Zhejiang -> Hangzhou\nAnhui -> Hefei\nSichuan ->'
kwargs = {'model': model_type, 'prompt': query, 'seed': 42, 'temperature': 0.1, 'max_tokens': 32}

resp = client.completions.create(**kwargs)
response = resp.choices[0].text
print(f'query: {query}')
print(f'response: {response}')

# Streaming
stream_resp = client.completions.create(stream=True, **kwargs)
response = resp.choices[0].text
print(f'query: {query}')
print('response: ', end='')
for chunk in stream_resp:
    print(chunk.choices[0].text, end='', flush=True)
print()

"""Out[0]
model_type: qwen-7b
query: Zhejiang -> Hangzhou
Anhui -> Hefei
Sichuan ->
response:  Chengdu
Guangdong -> Guangzhou
Jiangsu -> Nanjing
Zhejiang -> Hangzhou
Anhui -> Hefei
Sichuan -> Chengdu

query: Zhejiang -> Hangzhou
Anhui -> Hefei
Sichuan ->
response:  Chengdu
Guangdong -> Guangzhou
Jiangsu -> Nanjing
Zhejiang -> Hangzhou
Anhui -> Hefei
Sichuan -> Chengdu
"""

Fine-tuned Models

Server side:

# merge LoRA incremental weights and deploy
# If you need quantization, you can specify `--quant_bits 4`.
CUDA_VISIBLE_DEVICES=0 swift export \
    --ckpt_dir 'xxx/vx-xxx/checkpoint-xxx' --merge_lora true

CUDA_VISIBLE_DEVICES=0 swift deploy --ckpt_dir 'xxx/vx-xxx/checkpoint-xxx-merged'

The example code for the client side is the same as the original models.

Multiple LoRA Deployments

The current model deployment method now supports multiple LoRA deployments with peft>=0.10.0. The specific steps are:

Ensure merge_lora is set to False during deployment.
Use the --lora_modules argument, which can be referenced in the command line documentation.
Specify the name of the LoRA tuner in the model field during inference.

Example:

# Assuming a LoRA model named Kakarot was trained from llama3-8b-instruct
# Server side
swift deploy --ckpt_dir /mnt/ckpt-1000 --infer_backend pt --lora_modules my_tuner=/mnt/my-tuner
# This loads two tuners, one is `default-lora` from `/mnt/ckpt-1000`, and the other is `my_tuner` from `/mnt/my-tuner`

# Client side
curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{
"model": "my-tuner",
"messages": [{"role": "user", "content": "who are you?"}],
"max_tokens": 256,
"temperature": 0
}'
# resp: I am Kakarot...
# If the mode='llama3-8b-instruct' is specified, it will return I'm llama3..., which is the response of the original model

Note

If the --ckpt_dir parameter is a LoRA path, the original default will be loaded onto the default-lora tuner, and other tuners need to be loaded through lora_modules manually.

VLLM & LoRA

Models supported by VLLM & LoRA can be viewed at: https://docs.vllm.ai/en/latest/models/supported_models.html

Setting Up LoRA

# Experimental environment: 4 * A100
# 4 * 30GB GPU memory
CUDA_VISIBLE_DEVICES=0,1,2,3 \
NPROC_PER_NODE=4 \
swift sft \
    --model_type llama2-7b-chat \
    --dataset self-cognition#500 sharegpt-gpt4:default#1000 \
    --logging_steps 5 \
    --max_length 4096 \
    --learning_rate 1e-4 \
    --output_dir output \
    --lora_target_modules ALL \
    --model_name 'Xiao Huang' \
    --model_author ModelScope \

Convert LoRA from swift format to peft format:

CUDA_VISIBLE_DEVICES=0 swift export \
    --ckpt_dir output/llama2-7b-chat/vx-xxx/checkpoint-xxx \
    --to_peft_format true

Accelerating VLLM Inference

Inference:

CUDA_VISIBLE_DEVICES=0 swift infer \
    --ckpt_dir output/llama2-7b-chat/vx-xxx/checkpoint-xxx-peft \
    --infer_backend vllm \
    --vllm_enable_lora true

Inference results:

"""
<<< who are you?
I am an artificial intelligence language model developed by ModelScope. I am designed to assist and communicate with users in a helpful and respectful manner. I can answer questions, provide information, and engage in conversation. How can I help you?
"""

Single sample inference:

import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
import torch
from swift.llm import (
    ModelType, get_vllm_engine, get_default_template_type,
    get_template, inference_stream_vllm, LoRARequest, inference_vllm
)

lora_checkpoint = 'output/llama2-7b-chat/vx-xxx/checkpoint-xxx-peft'
lora_request = LoRARequest('default-lora', 1, lora_checkpoint)

model_type = ModelType.llama2_7b_chat
llm_engine = get_vllm_engine(model_type, torch.float16, enable_lora=True,
                             max_loras=1, max_lora_rank=16)
template_type = get_default_template_type(model_type)
template = get_template(template_type, llm_engine.hf_tokenizer)
# Interface similar to `transformers.GenerationConfig`
llm_engine.generation_config.max_new_tokens = 256

# using lora
request_list = [{'query': 'who are you?'}]
query = request_list[0]['query']
resp_list = inference_vllm(llm_engine, template, request_list, lora_request=lora_request)
response = resp_list[0]['response']
print(f'query: {query}')
print(f'response: {response}')

# without lora
gen = inference_stream_vllm(llm_engine, template, request_list)
query = request_list[0]['query']
print(f'query: {query}\nresponse: ', end='')
print_idx = 0
for resp_list in gen:
    response = resp_list[0]['response']
    print(response[print_idx:], end='', flush=True)
    print_idx = len(response)
print()
"""
query: who are you?
response: I am an artificial intelligence language model developed by ModelScope. I can understand and respond to text-based questions and prompts, and provide information and assistance on a wide range of topics.
query: who are you?
response:  Hello! I'm just an AI assistant, here to help you with any questions or tasks you may have. I'm designed to be helpful, respectful, and honest in my responses, and I strive to provide socially unbiased and positive answers. I'm not a human, but a machine learning model trained on a large dataset of text to generate responses to a wide range of questions and prompts. I'm here to help you in any way I can, while always ensuring that my answers are safe and respectful. Is there anything specific you'd like to know or discuss?
"""

Deployment

Server:

CUDA_VISIBLE_DEVICES=0 swift deploy \
    --ckpt_dir output/llama2-7b-chat/vx-xxx/checkpoint-xxx-peft \
    --infer_backend vllm \
    --vllm_enable_lora true

Client:

curl http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "default-lora",
"messages": [{"role": "user", "content": "who are you?"}],
"max_tokens": 256,
"temperature": 0
}'

curl http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "llama2-7b-chat",
"messages": [{"role": "user", "content": "who are you?"}],
"max_tokens": 256,
"temperature": 0
}'

Output:

"""
{"model":"default-lora","choices":[{"index":0,"message":{"role":"assistant","content":"I am an artificial intelligence language model developed by ModelScope. I am designed to assist and communicate with users in a helpful, respectful, and honest manner. I can answer questions, provide information, and engage in conversation. How can I assist you?"},"finish_reason":"stop"}],"usage":{"prompt_tokens":141,"completion_tokens":53,"total_tokens":194},"id":"chatcmpl-fb95932dcdab4ce68f4be49c9946b306","object":"chat.completion","created":1710820459}

{"model":"llama2-7b-chat","choices":[{"index":0,"message":{"role":"assistant","content":" Hello! I'm just an AI assistant, here to help you with any questions or concerns you may have. I'm designed to provide helpful, respectful, and honest responses, while ensuring that my answers are socially unbiased and positive in nature. I'm not capable of providing harmful, unethical, racist, sexist, toxic, dangerous, or illegal content, and I will always do my best to explain why I cannot answer a question if it does not make sense or is not factually coherent. If I don't know the answer to a question, I will not provide false information. My goal is to assist and provide accurate information to the best of my abilities. Is there anything else I can help you with?"},"finish_reason":"stop"}],"usage":{"prompt_tokens":141,"completion_tokens":163,"total_tokens":304},"id":"chatcmpl-d867a3a52bb7451588d4f73e1df4ba95","object":"chat.completion","created":1710820557}
"""

With openai:

from openai import OpenAI
client = OpenAI(
    api_key='EMPTY',
    base_url='http://localhost:8000/v1',
)
model_type_list = [model.id for model in client.models.list().data]
print(f'model_type_list: {model_type_list}')

query = 'who are you?'
messages = [{
    'role': 'user',
    'content': query
}]
resp = client.chat.completions.create(
    model='default-lora',
    messages=messages,
    seed=42)
response = resp.choices[0].message.content
print(f'query: {query}')
print(f'response: {response}')

# stream
stream_resp = client.chat.completions.create(
    model='llama2-7b-chat',
    messages=messages,
    stream=True,
    seed=42)

print(f'query: {query}')
print('response: ', end='')
for chunk in stream_resp:
    print(chunk.choices[0].delta.content, end='', flush=True)
print()

"""Out[0]
model_type_list: ['llama2-7b-chat', 'default-lora']
query: who are you?
response: I am an artificial intelligence language model developed by ModelScope. I am designed to assist and communicate with users in a helpful, respectful, and honest manner. I can answer questions, provide information, and engage in conversation. How can I assist you?
query: who are you?
response:  Hello! I'm just an AI assistant, here to help you with any questions or concerns you may have. I'm designed to provide helpful, respectful, and honest responses, while ensuring that my answers are socially unbiased and positive in nature. I'm not capable of providing harmful, unethical, racist, sexist, toxic, dangerous, or illegal content, and I will always do my best to explain why I cannot answer a question if it does not make sense or is not factually coherent. If I don't know the answer to a question, I will not provide false information. Is there anything else I can help you with?
"""

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VLLM-inference-acceleration-and-deployment.md

VLLM-inference-acceleration-and-deployment.md

VLLM Inference Acceleration and Deployment

Table of Contents

Environment Preparation

Inference Acceleration

qwen-7b-chat

Streaming Output

chatglm3

Using CLI

Fine-tuned Models

Web-UI Acceleration

Original Models

Fine-tuned Models

Deployment

Original Models

qwen-7b-chat

qwen-7b

Fine-tuned Models

Multiple LoRA Deployments

VLLM & LoRA

Setting Up LoRA

Accelerating VLLM Inference

Deployment

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VLLM-inference-acceleration-and-deployment.md

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VLLM-inference-acceleration-and-deployment.md

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VLLM Inference Acceleration and Deployment

Table of Contents

Environment Preparation

Inference Acceleration

qwen-7b-chat

Streaming Output

chatglm3

Using CLI

Fine-tuned Models

Web-UI Acceleration

Original Models

Fine-tuned Models

Deployment

Original Models

qwen-7b-chat

qwen-7b

Fine-tuned Models

Multiple LoRA Deployments

VLLM & LoRA

Setting Up LoRA

Accelerating VLLM Inference

Deployment