id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
7,983 | from transformers import AutoModel, AutoTokenizer
import gradio as gr
import mdtex2html
def reset_state():
return [], [] | null |
7,984 | import argparse
import os
import platform
import shutil
from copy import deepcopy
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
from transformers.trainer_utils import set_seed
def _load_model_tokenizer(args):
tokenizer = AutoTokenizer... | null |
7,985 | import argparse
import os
import platform
import shutil
from copy import deepcopy
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
from transformers.trainer_utils import set_seed
def _gc():
import gc
gc.collect()
if torch.cuda.is... | null |
7,986 | import argparse
import os
import platform
import shutil
from copy import deepcopy
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
from transformers.trainer_utils import set_seed
def _clear_screen():
if platform.system() == "Windows":
... | null |
7,987 | import argparse
import os
import platform
import shutil
from copy import deepcopy
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
from transformers.trainer_utils import set_seed
def _print_history(history):
terminal_width = shutil.get_t... | null |
7,988 | import argparse
import os
import platform
import shutil
from copy import deepcopy
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
from transformers.trainer_utils import set_seed
def _get_input() -> str:
while True:
try:
... | null |
7,989 | import torch
from transformers import AutoModelForCausalLM
from accelerate import dispatch_model
def _device_map(num_gpus, num_layers):
def load_model_on_gpus(model_name_or_path, num_gpus: int = 2):
num_devices = torch.cuda.device_count()
if num_gpus == 1:
model = AutoModelForCausalLM.from_pretrained(... | null |
7,990 | from transformers.generation import GenerationConfig
parser.add_argument('--path', type=str, default='Qwen-7B/eval/evaluate_ceval.py')
parser.add_argument('--regenerate', action='store_true', default=False)return args
if (not args.regenerate) and os.path.exists(comments_path):
print("use cache: ", c... | null |
7,991 | from transformers.generation import GenerationConfig
class QWenChat():
def __init__(self):
self.tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-7B-Chat", trust_remote_code=True)
# use bf16
# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-7B-Chat", device_map="auto", trust_remot... | null |
7,992 | import json
import os
import json5
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
def text_completion(input_text: str, stop_words) -> str: # 作为一个文本续写模型来使用
im_end = '<|im_end|>'
if im_end not in stop_words:
stop_words = stop... | null |
7,993 | import json
from pprint import pprint
import openai
openai.api_base = 'http://localhost:8000/v1'
openai.api_key = 'none'
def call_qwen(messages, functions=None):
print('input:')
pprint(messages, indent=2)
if functions:
response = openai.ChatCompletion.create(model='Qwen',
... | null |
7,994 | import json
from pprint import pprint
import openai
openai.api_base = 'http://localhost:8000/v1'
openai.api_key = 'none'
def call_qwen(messages, functions=None):
print('input:')
pprint(messages, indent=2)
if functions:
response = openai.ChatCompletion.create(model='Qwen',
... | null |
7,995 | import json
from pprint import pprint
import openai
openai.api_base = 'http://localhost:8000/v1'
openai.api_key = 'none'
def call_qwen(messages, functions=None):
print('input:')
pprint(messages, indent=2)
if functions:
response = openai.ChatCompletion.create(model='Qwen',
... | null |
7,996 |
def test_4():
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain.chat_models import ChatOpenAI
llm = ChatOpenAI(
model_name='Qwen',
openai_api_base='http://localhost:8000/v1',
openai_api_key='EMPTY',
streaming=False,
)
tools = load_... | null |
7,997 | import json
def format_train_sample(messages):
#
# You do not need the `function` role, as Qwen's function calling is actually implemented via ReAct,
# not by adding a `function` role or `function_call` message. See openai_api.py for details.
#
# If you need the `system` role, you might need to mod... | null |
7,998 | import json
TOOL_DESC = """{name_for_model}: Call this tool to interact with the {name_for_human} API. What is the {name_for_human} API useful for? {description_for_model} Parameters: {parameters}"""
REACT_INSTRUCTION = """Answer the following questions as best you can. You have access to the following APIs:
{tools_tex... | null |
7,999 | from transformers import PreTrainedTokenizer, GenerationConfig, StoppingCriteriaList
from typing import Optional, Callable, List, Tuple, Union
import copy
import torch
from transformers import AutoTokenizer
from transformers.generation.logits_process import LogitsProcessorList
from packaging import version
def get_sto... | null |
8,000 | from transformers import PreTrainedTokenizer, GenerationConfig, StoppingCriteriaList
from typing import Optional, Callable, List, Tuple, Union
import copy
import torch
from transformers import AutoTokenizer
from transformers.generation.logits_process import LogitsProcessorList
from packaging import version
def make_co... | null |
8,001 | import argparse
import base64
import collections
import logging
import unicodedata
from pathlib import Path
import regex as re
from tqdm.contrib.logging import tqdm_logging_redirect
logger = logging.getLogger(__name__)
def load_tiktoken_bpe(tiktoken_bpe_file: str) -> "dict[bytes, int]":
def dump_tiktoken_bpe(bpe_ranks:... | null |
8,002 | from dataclasses import dataclass, field
import json
import math
import logging
import os
from typing import Dict, Optional, List
import torch
from torch.utils.data import Dataset
from deepspeed import zero
from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus
import transformers
from transformers imp... | null |
8,003 | from dataclasses import dataclass, field
import json
import math
import logging
import os
from typing import Dict, Optional, List
import torch
from torch.utils.data import Dataset
from deepspeed import zero
from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus
import transformers
from transformers imp... | null |
8,004 | import argparse
import json
from typing import Dict
import logging
import torch
import transformers
from transformers import AutoTokenizer
from transformers.trainer_pt_utils import LabelSmoother
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
IGNORE_TOKEN_ID = LabelSmoother.ignore_index
def preprocess(
... | null |
8,006 | from transformers import PreTrainedTokenizer, GenerationConfig, StoppingCriteriaList
from typing import Optional, Callable, List, Tuple, Union
import copy
import torch
from transformers import AutoTokenizer
from transformers.generation.logits_process import LogitsProcessorList
from packaging import version
def make_co... | null |
8,007 | import base64
import copy
import json
import time
from argparse import ArgumentParser
from contextlib import asynccontextmanager
from pprint import pprint
from typing import Dict, List, Literal, Optional, Union
import torch
import uvicorn
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CO... | null |
8,008 | import base64
import copy
import json
import time
from argparse import ArgumentParser
from contextlib import asynccontextmanager
from pprint import pprint
from typing import Dict, List, Literal, Optional, Union
import torch
import uvicorn
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CO... | null |
8,009 | import base64
import copy
import json
import time
from argparse import ArgumentParser
from contextlib import asynccontextmanager
from pprint import pprint
from typing import Dict, List, Literal, Optional, Union
import torch
import uvicorn
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CO... | null |
8,010 | import base64
import copy
import json
import time
from argparse import ArgumentParser
from contextlib import asynccontextmanager
from pprint import pprint
from typing import Dict, List, Literal, Optional, Union
import torch
import uvicorn
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CO... | null |
8,011 | import ctypesimport math
import o
import threading
from typing import Optional, Tuple, Union, List, Callable, Dict, An
from copy import deepcopy
import platform
fastllm_lib.create_llm_model.argtypes = [ctypes.c_char_p]
fastllm_lib.create_llm_model.restype = ctypes.c_int
fastllm_lib.token_decode.argtypes = [ctypes.c_int... | null |
8,012 | import ctypesimport math
import o
import threading
from typing import Optional, Tuple, Union, List, Callable, Dict, An
from copy import deepcopy
import platform
fastllm_lib.create_llm_model.argtypes = [ctypes.c_char_p]
fastllm_lib.create_llm_model.restype = ctypes.c_int
fastllm_lib.token_decode.argtypes = [ctypes.c_int... | null |
8,013 | import ctypesimport math
import o
import threading
from typing import Optional, Tuple, Union, List, Callable, Dict, An
from copy import deepcopy
import platform
fastllm_lib.create_llm_model.argtypes = [ctypes.c_char_p]
fastllm_lib.create_llm_model.restype = ctypes.c_int
fastllm_lib.token_decode.argtypes = [ctypes.c_int... | null |
8,014 | import ctypesimport math
import o
import threading
from typing import Optional, Tuple, Union, List, Callable, Dict, An
from copy import deepcopy
import platform
fastllm_lib.create_llm_model.argtypes = [ctypes.c_char_p]
fastllm_lib.create_llm_model.restype = ctypes.c_int
fastllm_lib.token_decode.argtypes = [ctypes.c_int... | null |
8,015 | import ctypesimport math
import o
import threading
from typing import Optional, Tuple, Union, List, Callable, Dict, An
from copy import deepcopy
import platform
fastllm_lib.create_llm_model.argtypes = [ctypes.c_char_p]
fastllm_lib.create_llm_model.restype = ctypes.c_int
fastllm_lib.token_decode.argtypes = [ctypes.c_int... | null |
8,016 | import ctypesimport math
import o
import threading
from typing import Optional, Tuple, Union, List, Callable, Dict, An
from copy import deepcopy
import platform
fastllm_lib.create_llm_model.argtypes = [ctypes.c_char_p]
fastllm_lib.create_llm_model.restype = ctypes.c_int
fastllm_lib.token_decode.argtypes = [ctypes.c_int... | null |
8,017 | import ctypesimport math
import o
import threading
from typing import Optional, Tuple, Union, List, Callable, Dict, An
from copy import deepcopy
import platform
fastllm_lib.create_llm_model.argtypes = [ctypes.c_char_p]
fastllm_lib.create_llm_model.restype = ctypes.c_int
fastllm_lib.token_decode.argtypes = [ctypes.c_int... | null |
8,018 | import ctypesimport math
import o
import threading
from typing import Optional, Tuple, Union, List, Callable, Dict, An
from copy import deepcopy
import platform
fastllm_lib.create_llm_model.argtypes = [ctypes.c_char_p]
fastllm_lib.create_llm_model.restype = ctypes.c_int
fastllm_lib.token_decode.argtypes = [ctypes.c_int... | null |
8,019 | import ctypesimport math
import o
import threading
from typing import Optional, Tuple, Union, List, Callable, Dict, An
from copy import deepcopy
import platform
;;;
def from_hf(model,
tokenizer = None,
dtype = "float16"):
from fastllm_pytools import hf_model;
return hf_model.create(mod... | null |
8,020 | import struct
import numpy as np
import torch
def writeString(fo, s):
def writeKeyValue(fo, key, value):
fastllm_data_type_dict = {
"int4": 8,
"int8": 3,
"float16": 7,
"float32": 0,
}
fastllm_weight_type_dict = {
"linear": 1,
"embedding": 2
}
v = np.random.randint(-127, 127, [10, 20]
v = (v / c_... | null |
8,021 | import argparse
from fastllm_pytools import llm
def args_parser():
parser = argparse.ArgumentParser(description = 'qwen_chat_demo')
parser.add_argument('-p', '--path', type = str, required = True, default = '', help = '模型文件的路径')
args = parser.parse_args()
return args | null |
8,022 | import argparse
from fastllm_pytools import llm
import time
def args_parser():
parser = argparse.ArgumentParser(description = 'fastllm_chat_demo')
parser.add_argument('-p', '--path', type = str, required = True, default = '', help = '模型文件的路径')
args = parser.parse_args()
return args | null |
8,023 | import streamlit as st
from streamlit_chat import message
from fastllm_pytools import llm
import sys
;;
def get_model():
model = llm.model(sys.argv[1])
return model | null |
8,024 | import os
from argparse import ArgumentParser
import gradio as gr
import mdtex2html
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
DEFAULT_CKPT_PATH = 'Qwen/Qwen-7B-Chat'
def _get_args():
parser = ArgumentParser()
parser.add_argume... | null |
8,025 | import os
from argparse import ArgumentParser
import gradio as gr
import mdtex2html
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
def _load_model_tokenizer(args):
tokenizer = AutoTokenizer.from_pretrained(
args.checkpoint_path... | null |
8,026 | import os
from argparse import ArgumentParser
import gradio as gr
import mdtex2html
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
def postprocess(self, y):
if y is None:
return []
for i, (message, response) in enumerate(y)... | null |
8,027 | import os
from argparse import ArgumentParser
import gradio as gr
import mdtex2html
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
gr.Chatbot.postprocess = postprocess
def _parse_text(text):
def _gc():
def _launch_demo(args, model, tokeniz... | null |
8,028 | import os
import argparse
import re
import torch
import pandas as pd
from thefuzz import process
from tqdm import tqdm
from transformers.trainer_utils import set_seed
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
def load_models_tokenizer(args):
t... | null |
8,029 | import os
import argparse
import re
import torch
import pandas as pd
from thefuzz import process
from tqdm import tqdm
from transformers.trainer_utils import set_seed
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
def count_substr(gen, pattern):
re... | null |
8,030 | import os
import argparse
import re
import torch
import pandas as pd
from thefuzz import process
from tqdm import tqdm
from transformers.trainer_utils import set_seed
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
def format_example(line):
example =... | null |
8,031 | import os
import argparse
import re
import torch
import pandas as pd
from thefuzz import process
from tqdm import tqdm
from transformers.trainer_utils import set_seed
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
TASK_NAME_MAPPING = {
"computer_net... | null |
8,032 | import os
import argparse
import re
import torch
import pandas as pd
from tqdm import tqdm
from thefuzz import process
from transformers.trainer_utils import set_seed
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
def load_models_tokenizer(args):
t... | null |
8,033 | import os
import argparse
import re
import torch
import pandas as pd
from tqdm import tqdm
from thefuzz import process
from transformers.trainer_utils import set_seed
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
def format_example(line):
def extract_a... | null |
8,034 | import os
import argparse
import re
import torch
import pandas as pd
from tqdm import tqdm
from thefuzz import process
from transformers.trainer_utils import set_seed
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
TASK_NAME_MAPPING = {
"stem": [
... | null |
8,035 | import os
import pandas as pd
import numpy as np
import argparse
import datasets
import torch
from collections import defaultdict
from typing import List
from tqdm import tqdm
from transformers.trainer_utils import set_seed
def load_models_tokenizer(args):
from transformers import AutoModelForCausalLM, AutoTokeniz... | null |
8,036 | import os
import pandas as pd
import numpy as np
import argparse
import datasets
import torch
from collections import defaultdict
from typing import List
from tqdm import tqdm
from transformers.trainer_utils import set_seed
def format_example(line, include_answer=True):
example = "问题：" + line["Question"]
for ch... | null |
8,037 | import os
import pandas as pd
import numpy as np
import argparse
import datasets
import torch
from collections import defaultdict
from typing import List
from tqdm import tqdm
from transformers.trainer_utils import set_seed
TASK_NAME_MAPPING = defaultdict(list)
for k, v in categories.items():
for subject, subcat in... | null |
8,038 | import argparse
import json
import os
import pprint
import json5
import jsonlines
from rouge_score import rouge_scorer
from tqdm import tqdm
from transformers import Agent, AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
from transformers.tools.evaluate_agent import evaluate_age... | null |
8,039 | import argparse
import json
import os
import pprint
import json5
import jsonlines
from rouge_score import rouge_scorer
from tqdm import tqdm
from transformers import Agent, AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
from transformers.tools.evaluate_agent import evaluate_age... | null |
8,040 | import argparse
import json
import os
import pprint
import json5
import jsonlines
from rouge_score import rouge_scorer
from tqdm import tqdm
from transformers import Agent, AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
from transformers.tools.evaluate_agent import evaluate_age... | null |
8,041 | import argparse
import json
import os
import pprint
import json5
import jsonlines
from rouge_score import rouge_scorer
from tqdm import tqdm
from transformers import Agent, AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
from transformers.tools.evaluate_agent import evaluate_age... | null |
8,042 | import os
from typing import List
import argparse
import torch
import pandas as pd
import numpy as np
from tqdm import tqdm
from transformers.trainer_utils import set_seed
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
def load_models_tokenizer(args):
... | null |
8,043 | import os
from typing import List
import argparse
import torch
import pandas as pd
import numpy as np
from tqdm import tqdm
from transformers.trainer_utils import set_seed
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
def format_example(line, include_a... | null |
8,044 | import os
from typing import List
import argparse
import torch
import pandas as pd
import numpy as np
from tqdm import tqdm
from transformers.trainer_utils import set_seed
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
TASK_NAME_MAPPING = {
"compute... | null |
8,045 | import argparse
import tqdm
import torch
import jsonlines
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
def decode(tokens_list, tokenizer, raw_text_len):
sents = []
# print(len(tokens_list))
for tokens in tokens_list:
tokens = token... | null |
8,046 | import json
import re
from pathlib import Path
import argparse
import requests
import math
import numpy as np
import tqdm
from datasets import load_from_disk, load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
def doc_to_text(doc, use_fewshot)... | null |
8,047 | import json
import re
from pathlib import Path
import argparse
import requests
import math
import numpy as np
import tqdm
from datasets import load_from_disk, load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
def generate_sample(model, tokeni... | null |
8,048 | import json
import re
from pathlib import Path
import argparse
import requests
import math
import numpy as np
import tqdm
from datasets import load_from_disk, load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
def extract_answer(s):
_PAT_LA... | null |
8,049 | import re
import textwrap
import argparse
from pathlib import Path
import tqdm
import jsonlines
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
def extract_code(text, entry_point):
# 正则表达式匹配代码块
code_block_pattern = re.compile(
rf"```(?:[P... | null |
8,050 | import os
from typing import List
import pandas as pd
import numpy as np
import argparse
import torch
from tqdm import tqdm
from transformers.trainer_utils import set_seed
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
def load_models_tokenizer(args):
... | null |
8,051 | import os
from typing import List
import pandas as pd
import numpy as np
import argparse
import torch
from tqdm import tqdm
from transformers.trainer_utils import set_seed
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
def format_example(line, include_a... | null |
8,052 | import os
from typing import List
import pandas as pd
import numpy as np
import argparse
import torch
from tqdm import tqdm
from transformers.trainer_utils import set_seed
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
TASK_NAME_MAPPING = {
"stem": ... | null |
8,053 | import re
import torch
import argparse
import jsonlines
import numpy as np
import datasets
from datasets import load_from_disk, load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
def doc_to_text(doc):
return (
fewshot_prompt
... | null |
8,054 | import re
import torch
import argparse
import jsonlines
import numpy as np
import datasets
from datasets import load_from_disk, load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
def decode(tokens_list, tokenizer, raw_text_len):
sents = []
... | null |
8,055 | import re
import torch
import argparse
import jsonlines
import numpy as np
import datasets
from datasets import load_from_disk, load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
INVALID_ANS = "[invalid]"
def extract_answer_hf(completion):
... | null |
8,056 | from http import HTTPStatus
import numpy as np
from albumentations.pytorch.transforms import ToTensorV2
from fastapi import FastAPI, File, UploadFile
from PIL import Image
from image_to_latex.lit_models import LitResNetTransformer
async def load_model():
global lit_model
global transform
lit_model = LitRes... | null |
8,057 | from http import HTTPStatus
import numpy as np
from albumentations.pytorch.transforms import ToTensorV2
from fastapi import FastAPI, File, UploadFile
from PIL import Image
from image_to_latex.lit_models import LitResNetTransformer
The provided code snippet includes necessary dependencies for implementing the `read_roo... | Health check. |
8,058 | from http import HTTPStatus
import numpy as np
from albumentations.pytorch.transforms import ToTensorV2
from fastapi import FastAPI, File, UploadFile
from PIL import Image
from image_to_latex.lit_models import LitResNetTransformer
def predict(file: UploadFile = File(...)):
image = Image.open(file.file).convert("L"... | null |
8,059 | import json
import tarfile
from pathlib import Path
from typing import Callable, Dict, List, Optional, Tuple, Union
from urllib.request import urlretrieve
import numpy as np
from PIL import Image
from torch.utils.data import Dataset
from tqdm import tqdm
class TqdmUpTo(tqdm):
"""From https://github.com/tqdm/tqdm/bl... | Download a file from url to filename, with a progress bar. |
8,060 | import json
import tarfile
from pathlib import Path
from typing import Callable, Dict, List, Optional, Tuple, Union
from urllib.request import urlretrieve
import numpy as np
from PIL import Image
from torch.utils.data import Dataset
from tqdm import tqdm
The provided code snippet includes necessary dependencies for im... | Extract a .tar or .tar.gz file. |
8,061 | import json
import tarfile
from pathlib import Path
from typing import Callable, Dict, List, Optional, Tuple, Union
from urllib.request import urlretrieve
import numpy as np
from PIL import Image
from torch.utils.data import Dataset
from tqdm import tqdm
The provided code snippet includes necessary dependencies for im... | Returns all the formulas in the formula file. |
8,062 | import json
import tarfile
from pathlib import Path
from typing import Callable, Dict, List, Optional, Tuple, Union
from urllib.request import urlretrieve
import numpy as np
from PIL import Image
from torch.utils.data import Dataset
from tqdm import tqdm
def get_split(
all_formulas: List[List[str]],
filename: ... | null |
8,063 | import json
import tarfile
from pathlib import Path
from typing import Callable, Dict, List, Optional, Tuple, Union
from urllib.request import urlretrieve
import numpy as np
from PIL import Image
from torch.utils.data import Dataset
from tqdm import tqdm
def pil_loader(fp: Path, mode: str) -> Image.Image:
with open... | null |
8,064 | import math
from typing import Union
import torch
import torch.nn as nn
import torchvision.models
from torch import Tensor
from .positional_encoding import PositionalEncoding1D, PositionalEncoding2D
The provided code snippet includes necessary dependencies for implementing the `generate_square_subsequent_mask` functio... | Generate a triangular (size, size) mask. |
8,065 | import math
from typing import Union
import torch
import torch.nn as nn
import torchvision.models
from torch import Tensor
from .positional_encoding import PositionalEncoding1D, PositionalEncoding2D
The provided code snippet includes necessary dependencies for implementing the `find_first` function. Write a Python fun... | Find the first occurence of element in x along a given dimension. Args: x: The input tensor to be searched. element: The number to look for. dim: The dimension to reduce. Returns: Indices of the first occurence of the element in x. If not found, return the length of x along dim. Usage: >>> first_element(Tensor([[1, 2, ... |
8,066 | import argparse
import shutil
import tempfile
from pathlib import Path
import wandb
The provided code snippet includes necessary dependencies for implementing the `download_checkpoint` function. Write a Python function `def download_checkpoint(run_path: str) -> None` to solve the following problem:
Download model chec... | Download model checkpoint from Weights & Biases. Args: run_path: The run path for a run, in the format of '<entity>/<project>/<run_id>'. To find the run path for a run, go to the Overview tab in wandb dashboard. |
8,067 | import os
import re
import sys
from setuptools import find_packages, setup
pwd = os.path.dirname(__file__)
def readme():
with open(os.path.join(pwd, 'README.md'), encoding='utf-8') as f:
content = f.read()
return content | null |
8,068 | import os
import re
import sys
from setuptools import find_packages, setup
pwd = os.path.dirname(__file__)
version_file = 'lmdeploy/version.py'
def get_version():
with open(os.path.join(pwd, version_file), 'r') as f:
exec(compile(f.read(), version_file, 'exec'))
return locals()['__version__'] | null |
8,069 | import os
import re
import sys
from setuptools import find_packages, setup
pwd = os.path.dirname(__file__)
def check_ext_modules():
if os.path.exists(os.path.join(pwd, 'lmdeploy', 'lib')):
return True
return False | null |
8,070 | import os
import re
import sys
from setuptools import find_packages, setup
cuda_pkgs = get_cuda_pkgs()
def get_cuda_pkgs():
arg_name = '--cuda='
arg_value = None
for arg in sys.argv[1:]:
if arg.startswith(arg_name):
arg_value = arg[len(arg_name):]
sys.argv.remove(arg)
... | null |
8,071 | import os
import re
import sys
from setuptools import find_packages, setup
cuda_pkgs = get_cuda_pkgs()
The provided code snippet includes necessary dependencies for implementing the `parse_requirements` function. Write a Python function `def parse_requirements(fname='requirements.txt', with_version=True)` to solve the... | Parse the package dependencies listed in a file but strips specific versioning information. Args: fname (str): path to the file with_version (bool, default=False): if True include version specs Returns: List[str]: list of requirements items CommandLine: python -c "import setup; print(setup.parse_requirements())" |
8,072 | from typing import Tuple
The provided code snippet includes necessary dependencies for implementing the `parse_version_info` function. Write a Python function `def parse_version_info(version_str: str) -> Tuple` to solve the following problem:
Parse version from a string. Args: version_str (str): A string represents a ... | Parse version from a string. Args: version_str (str): A string represents a version info. Returns: tuple: A sequence of integer and string represents version. |
8,073 | import os
from typing import List, Literal, Optional, Union
from .archs import autoget_backend_config
from .messages import PytorchEngineConfig, TurbomindEngineConfig
from .model import ChatTemplateConfig
def serve(model_path: str,
model_name: Optional[str] = None,
backend: Literal['turbomind', 'pyt... | Args: model_path (str): the path of a model. It could be one of the following options: - i) A local directory path of a turbomind model which is converted by `lmdeploy convert` command or download from ii) and iii). - ii) The model_id of a lmdeploy-quantized model hosted inside a model repo on huggingface.co, such as "... |
8,074 | import os
import random
from lmdeploy.messages import EngineGenerationConfig
from lmdeploy.model import ChatTemplateConfig
from lmdeploy.tokenizer import DetokenizeState
The provided code snippet includes necessary dependencies for implementing the `input_prompt` function. Write a Python function `def input_prompt(mod... | Input a prompt in the consolo interface. |
8,075 | import json
import os
from huggingface_hub import hf_hub_download
from transformers.utils import ExplicitEnum
from lmdeploy.utils import get_logger
class ModelSource(ExplicitEnum):
"""Turbomind model source."""
WORKSPACE = 'workspace'
HF_MODEL = 'hf_model'
def get_hf_config_content(pretrained_model_name_or_... | Check if single input pretrained_model_name_or_path is enough to use. |
8,076 | import json
import os
from huggingface_hub import hf_hub_download
from transformers.utils import ExplicitEnum
from lmdeploy.utils import get_logger
def get_model_from_config(model_dir: str):
import json
config_file = os.path.join(model_dir, 'config.json')
default = 'llama'
if not os.path.exists(config_... | null |
8,077 | from typing import List
import torch
from ..source_model.base import BaseInputModel, BaseReader
from .base import (OUTPUT_MODELS, BaseOutputModel, TurbomindModelConfig,
merge_qkv, permute)
The provided code snippet includes necessary dependencies for implementing the `transpose_tensor` function. Wri... | Transpose tensor. |
8,078 | import configparser
import copy
import inspect
import io
import json
import os.path as osp
from abc import ABC, abstractmethod
from configparser import ConfigParser
import torch
import tqdm
from mmengine import Registry
from pydantic.dataclasses import dataclass
from lmdeploy.messages import TurbomindEngineConfig
from ... | null |
8,079 | import configparser
import copy
import inspect
import io
import json
import os.path as osp
from abc import ABC, abstractmethod
from configparser import ConfigParser
import torch
import tqdm
from mmengine import Registry
from pydantic.dataclasses import dataclass
from lmdeploy.messages import TurbomindEngineConfig
from ... | get weight dtype map. |
8,080 | import configparser
import copy
import inspect
import io
import json
import os.path as osp
from abc import ABC, abstractmethod
from configparser import ConfigParser
import torch
import tqdm
from mmengine import Registry
from pydantic.dataclasses import dataclass
from lmdeploy.messages import TurbomindEngineConfig
from ... | null |
8,081 | import os.path as osp
import sys
import torch
import lmdeploy
from ..source_model.base import BaseInputModel, BaseReader
from .base import (OUTPUT_MODELS, BaseOutputModel, TurbomindModelConfig,
merge_qkv, permute)
import _turbomind as _tm
def transpose_qk_s4(src: torch.Tensor, group_size):
asser... | null |
8,082 | import os.path as osp
import sys
import torch
import lmdeploy
from ..source_model.base import BaseInputModel, BaseReader
from .base import (OUTPUT_MODELS, BaseOutputModel, TurbomindModelConfig,
merge_qkv, permute)
import _turbomind as _tm
def permute(x: torch.Tensor, size_per_head: int = 128):
i... | null |
8,083 | import os.path as osp
import sys
import torch
import lmdeploy
from ..source_model.base import BaseInputModel, BaseReader
from .base import (OUTPUT_MODELS, BaseOutputModel, TurbomindModelConfig,
merge_qkv, permute)
import _turbomind as _tm
def convert_s4(qw: torch.Tensor, qz: torch.Tensor, s: torch.T... | null |
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