mirror of https://github.com/InternLM/InternLM
update
parent
b9f53ef6cc
commit
6aef7d5ab1
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@ -62,23 +62,6 @@ class TestChat:
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assert_model(response)
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class TestChatAwq:
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"""Test cases for chat model."""
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@pytest.mark.parametrize(
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'model_name',
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['internlm/internlm2-chat-20b-4bits'],
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)
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def test_demo_default(self, model_name):
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engine_config = TurbomindEngineConfig(model_format='awq')
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pipe = pipeline('internlm/internlm2-chat-20b-4bits',
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backend_config=engine_config)
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responses = pipe(['Hi, pls intro yourself', 'Shanghai is'])
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print(responses)
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for response in responses:
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assert_model(response.text)
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class TestBase:
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"""Test cases for base model."""
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@ -282,9 +265,185 @@ class InternLMXComposer2QForCausalLM(BaseGPTQForCausalLM):
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]
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class TestReward:
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"""Test cases for base model."""
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@pytest.mark.parametrize(
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'model_name',
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[
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'internlm/internlm2-1_8b-reward', 'internlm/internlm2-7b-reward',
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'internlm/internlm2-20b-reward'
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],
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)
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@pytest.mark.parametrize(
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'usefast',
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[
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True,
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False,
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],
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)
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def test_demo_default(self, model_name, usefast):
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tokenizer = AutoTokenizer.from_pretrained(model_name,
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trust_remote_code=True,
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use_fast=usefast)
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model = AutoModel.from_pretrained(
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model_name,
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device_map='cuda',
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torch_dtype=torch.float16,
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trust_remote_code=True,
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name,
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trust_remote_code=True)
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chat_1 = [{
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'role': 'user',
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'content': "Hello! What's your name?"
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}, {
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'role':
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'assistant',
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'content':
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'I am InternLM2! A helpful AI assistant. What can I do for you?'
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}]
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chat_2 = [{
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'role': 'user',
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'content': "Hello! What's your name?"
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}, {
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'role': 'assistant',
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'content': 'I have no idea.'
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}]
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# get reward score for a single chat
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score1 = model.get_score(tokenizer, chat_1)
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score2 = model.get_score(tokenizer, chat_2)
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print('score1: ', score1)
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print('score2: ', score2)
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assert score1 > 0
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assert score2 < 0
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# batch inference, get multiple scores at once
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scores = model.get_scores(tokenizer, [chat_1, chat_2])
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print('scores: ', scores)
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assert scores[0] > 0
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assert scores[1] < 0
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# compare whether chat_1 is better than chat_2
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compare_res = model.compare(tokenizer, chat_1, chat_2)
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print('compare_res: ', compare_res)
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assert compare_res
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# >>> compare_res: True
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# rank multiple chats, it will return the ranking index of each chat
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# the chat with the highest score will have ranking index as 0
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rank_res = model.rank(tokenizer, [chat_1, chat_2])
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print('rank_res: ', rank_res) # lower index means higher score
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# >>> rank_res: [0, 1]
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assert rank_res[0] == 0
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assert rank_res[1] == 1
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class TestXcomposer2d5Model:
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"""Test cases for base model."""
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@pytest.mark.parametrize(
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'model_name',
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[
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'internlm/internlm-xcomposer2d5-7b',
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],
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)
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def test_video_understanding(self, model_name):
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torch.set_grad_enabled(False)
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# init model and tokenizer
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model = AutoModel.from_pretrained(
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model_name, torch_dtype=torch.bfloat16,
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trust_remote_code=True).cuda().eval().half()
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tokenizer = AutoTokenizer.from_pretrained(model_name,
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trust_remote_code=True)
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model.tokenizer = tokenizer
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query = 'Here are some frames of a video. Describe this video in detail' # noqa: F401, E501
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image = [
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'/mnt/petrelfs/qa-caif-cicd/github_runner/examples/liuxiang.mp4',
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]
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with torch.autocast(device_type='cuda', dtype=torch.float16):
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response, his = model.chat(tokenizer,
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query,
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image,
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do_sample=False,
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num_beams=3,
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use_meta=True)
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print(response)
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assert len(response) > 100
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assert 'athlete' in response.lower()
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query = 'tell me the athlete code of Liu Xiang'
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image = [
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'/mnt/petrelfs/qa-caif-cicd/github_runner/examples/liuxiang.mp4',
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]
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with torch.autocast(device_type='cuda', dtype=torch.float16):
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response, _ = model.chat(tokenizer,
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query,
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image,
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history=his,
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do_sample=False,
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num_beams=3,
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use_meta=True)
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print(response)
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assert len(response) > 10
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assert '1363' in response.lower()
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@pytest.mark.parametrize(
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'model_name',
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[
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'internlm/internlm-xcomposer2d5-7b',
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],
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)
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def test_multi_image_understanding(self, model_name):
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torch.set_grad_enabled(False)
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# init model and tokenizer
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model = AutoModel.from_pretrained(
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model_name, torch_dtype=torch.bfloat16,
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trust_remote_code=True).cuda().eval().half()
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tokenizer = AutoTokenizer.from_pretrained(model_name,
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trust_remote_code=True)
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model.tokenizer = tokenizer
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query = 'Image1 <ImageHere>; Image2 <ImageHere>; Image3 <ImageHere>; I want to buy a car from the three given cars, analyze their advantages and weaknesses one by one' # noqa: F401, E501
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image = [
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'/mnt/petrelfs/qa-caif-cicd/github_runner/examples/cars1.jpg',
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'/mnt/petrelfs/qa-caif-cicd/github_runner/examples/cars2.jpg',
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'/mnt/petrelfs/qa-caif-cicd/github_runner/examples/cars3.jpg',
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]
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with torch.autocast(device_type='cuda', dtype=torch.float16):
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response, his = model.chat(tokenizer,
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query,
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image,
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do_sample=False,
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num_beams=3,
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use_meta=True)
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print(response)
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assert len(response) > 100
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assert 'benz' in response.lower()
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assert 'bugatti' in response.lower()
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assert 'bmw' in response.lower()
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query = 'Image4 <ImageHere>; How about the car in Image4'
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image.append(
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'/mnt/petrelfs/qa-caif-cicd/github_runner/examples/cars4.jpg')
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with torch.autocast(device_type='cuda', dtype=torch.float16):
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response, _ = model.chat(tokenizer,
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query,
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image,
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do_sample=False,
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num_beams=3,
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history=his,
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use_meta=True)
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print(response)
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assert len(response) > 10
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assert 'ferrari' in response.lower()
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@pytest.mark.parametrize(
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'model_name',
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[
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@ -459,3 +618,20 @@ def is_html_code(html_code):
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except Exception as e:
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print('Error parsing HTML:', str(e))
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return False
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class TestChatAwq:
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"""Test cases for chat model."""
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@pytest.mark.parametrize(
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'model_name',
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['internlm/internlm2-chat-20b-4bits'],
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)
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def test_demo_default(self, model_name):
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engine_config = TurbomindEngineConfig(model_format='awq')
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pipe = pipeline('internlm/internlm2-chat-20b-4bits',
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backend_config=engine_config)
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responses = pipe(['Hi, pls intro yourself', 'Shanghai is'])
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print(responses)
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for response in responses:
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assert_model(response.text)
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