python_code stringlengths 0 992k | repo_name stringlengths 8 46 | file_path stringlengths 5 162 |
|---|---|---|
import re
import os
import sympy
import pandas as pd
from tasks.base import Task, DATA_PATH
from prompts.game24 import *
def get_current_numbers(y: str) -> str:
last_line = y.strip().split('\n')[-1]
return last_line.split('left: ')[-1].split(')')[0]
class Game24Task(Task):
"""
Input (x) : a strin... | tree-of-thoughts-main | experiements/tree-of-thought-llm/tasks/game24.py |
import os
import re
from tasks.base import Task, DATA_PATH
from prompts.text import *
from models import gpt
class TextTask(Task):
"""
Input (x) : a text instruction
Output (y) : a text generation
Reward (r) : # TODO
Input Example:
Output Example:
"""
def __init__(self, file='dat... | tree-of-thoughts-main | experiements/tree-of-thought-llm/tasks/text.py |
import re
import json
from tasks.base import Task
from prompts.crosswords import *
from models import gpt
class MiniCrosswordsEnv:
def __init__(self, file='mini0505.json'):
self.file = f'data/crosswords/{file}'
self.file = json.load(open(self.file))
self.n = len(self.file)
self.cac... | tree-of-thoughts-main | experiements/tree-of-thought-llm/tasks/crosswords.py |
DATA_PATH = './data'
class Task:
def __init__(self):
pass
def __len__(self) -> int:
pass
def get_input(self, idx: int) -> str:
pass
def test_output(self, idx: int, output: str):
pass | tree-of-thoughts-main | experiements/tree-of-thought-llm/tasks/base.py |
# 5-shot
standard_prompt = '''Use numbers and basic arithmetic operations (+ - * /) to obtain 24.
Input: 4 4 6 8
Answer: (4 + 8) * (6 - 4) = 24
Input: 2 9 10 12
Answer: 2 * 12 * (10 - 9) = 24
Input: 4 9 10 13
Answer: (13 - 9) * (10 - 4) = 24
Input: 1 4 8 8
Answer: (8 / 4 + 1) * 8 = 24
Input: 5 5 5 9
Answer: 5 + 5 + 5 +... | tree-of-thoughts-main | experiements/tree-of-thought-llm/prompts/game24.py |
standard_prompt = '''
Write a coherent passage of 4 short paragraphs. The end sentence of each paragraph must be: {input}
'''
cot_prompt = '''
Write a coherent passage of 4 short paragraphs. The end sentence of each paragraph must be: {input}
Make a plan then write. Your output should be of the following format:
Pla... | tree-of-thoughts-main | experiements/tree-of-thought-llm/prompts/text.py |
# 5 shot
standard_prompt = '''
Solve 5x5 mini crosswords. Given an input of 5 horizontal clues and 5 vertical clues, generate an output of 5 rows, where each row is 5 letter separated by space.
Input:
h1. A lunar valley
h2. A fatty oil
h3. To entice
h4. To lower; to reduce
h5. A solitary person
v1. According to the ro... | tree-of-thoughts-main | experiements/tree-of-thought-llm/prompts/crosswords.py |
tree-of-thoughts-main | experiements/extremely_experimental/reinforcement/v1.py | |
#give topic [What are quantum field theorem proofs respond in math notation] -> 100 questions by external model -> tree of thoughts for each question
#give dataset -> ask questions about each example and fine tune on like alpaca dataset
import json
from tree_of_thoughts.treeofthoughts import OptimizedTreeofThoughts
fro... | tree-of-thoughts-main | experiements/extremely_experimental/generate_dataset/main.py |
from abc import abstractmethod, ABC
from langchain import OpenAI
from langchain.agents import initialize_agent
from langchain.agents import AgentType
class AbstractLanguageModel(ABC):
@abstractmethod
def generate_thoughts(self, state, k):
pass
@abstractmethod
def evaluate_states(self, states... | tree-of-thoughts-main | experiements/extremely_experimental/prompting/LangChain_model.py |
import concurrent.futures
from abc import ABC, abstractmethod
import openai
import os
import guidance
import time
class AbstractLanguageModel(ABC):
@abstractmethod
def generate_thoughts(self, state, k):
pass
@abstractmethod
def evaluate_states(self, states):
pass
class CustomLanguag... | tree-of-thoughts-main | experiements/extremely_experimental/prompting/guidancePrompt.py |
from tree_of_thoughts.models.openai_models import OpenAILanguageModel
from tree_of_thoughts.treeofthoughts import TreeofThoughtsDFS
#
api_model= "gpt-3.5-turbo"
model = OpenAILanguageModel(api_key='api key', api_model=api_model)
#choose search algorithm('BFS' or 'DFS')
search_algorithm = "BFS"
# value or vote
eva... | tree-of-thoughts-main | examples/example_totdfs.py |
from tree_of_thoughts.models.openai_models import OpenAILanguageModel
from tree_of_thoughts.treeofthoughts import MonteCarloTreeofThoughts
api_model= "gpt-3.5-turbo"
model = OpenAILanguageModel(api_key='api key', api_model=api_model)
# Initialize the MonteCarloTreeofThoughts class with the model
tree_of_thoughts ... | tree-of-thoughts-main | examples/montecarlo_example.py |
from tree_of_thoughts.treeofthoughts import TreeofThoughts, HuggingLanguageModel, MonteCarloTreeofThoughts
model_name="gpt"
model = HuggingLanguageModel(model_name,
model_tokenizer=model_name,
verbose=True)
# Initialize the Mon... | tree-of-thoughts-main | examples/huggingface_example.py |
from tree_of_thoughts.models.openai_models import OpenAILanguageModel
from tree_of_thoughts.treeofthoughts import TreeofThoughts2
#
api_model= "gpt-3.5-turbo"
model = OpenAILanguageModel(api_key='api key', api_model=api_model)
tree_of_thoughts= TreeofThoughts2(model) #search_algorithm)
# Note to reproduce the s... | tree-of-thoughts-main | examples/example_tot2.py |
from tree_of_thoughts.models.openai_models import OpenAILanguageModel
from tree_of_thoughts.treeofthoughts import TreeofThoughtsASearch
#
api_model= "gpt-4"
model = OpenAILanguageModel(api_key='api key', api_model=api_model)
tree_of_thoughts= TreeofThoughtsASearch(model) #search_algorithm)
# Note to reproduce t... | tree-of-thoughts-main | examples/example_totA.py |
from tree_of_thoughts.treeofthoughts import HFPipelineModel, MonteCarloTreeofThoughts
model_name="gpt2"
gpt2_pipeline_model = HFPipelineModel(model_name)
tree_of_thoughts = MonteCarloTreeofThoughts(gpt2_pipeline_model)
#
initial_prompt = """
Input: 2 8 8 14
Possible next steps:
2 + 8 = 10 (left: 8 10 14)
8 / ... | tree-of-thoughts-main | examples/pipelinehuggingface.py |
#thought -> evaluated value (0.4, This solution is invalid because x) -> thought prompt + this solution is invalid because + better eval
import json
import os
import time
DATA_PATH = './data'
import logging
import concurrent.futures
from queue import PriorityQueue
from typing import Any, Dict, Union
import numpy a... | tree-of-thoughts-main | tree_of_thoughts/treeofthoughts.py |
from tree_of_thoughts.models.openai_models import OpenAILanguageModel, OptimizedOpenAILanguageModel
from tree_of_thoughts.treeofthoughts import TreeofThoughts, MonteCarloTreeofThoughts, TreeofThoughtsBFS, TreeofThoughtsDFS, TreeofThoughtsBEST, TreeofThoughtsASearch
from tree_of_thoughts.models.abstract_language_model i... | tree-of-thoughts-main | tree_of_thoughts/__init__.py |
from typing import List, Mapping, Union, Any, Callable
from typing import Dict
import requests
from copy import deepcopy
from dataclasses import dataclass
def _default_extractor(json_response: Dict[str, Any], stop_parameter_name) -> str:
"""
This function extracts the response from the JSON object using the d... | tree-of-thoughts-main | tree_of_thoughts/text_generation_web_ui.py |
import re
from typing import Any, Callable, Optional, Tuple, Union
from langchain.llms import OpenAI
from langchain_experimental.tot.checker import ToTChecker
from langchain_experimental.tot.thought import ThoughtValidity
class LangchainTOT:
def __init__(self,
problem_description: Optional[str]... | tree-of-thoughts-main | tree_of_thoughts/langchain_tot.py |
tree-of-thoughts-main | tree_of_thoughts/models/__init__.py | |
import guidance
from tree_of_thoughts.models.abstract_language_model import AbstractLanguageModel
import time
import os
import openai
class GuidanceLanguageModel(AbstractLanguageModel):
def __init__(self, model, strategy="cot", evaluation_strategy="value", enable_ReAct_prompting=False):
# gpt4 = guidance.... | tree-of-thoughts-main | tree_of_thoughts/models/guidance_model.py |
import os
import openai
import time
from tree_of_thoughts.models.abstract_language_model import AbstractLanguageModel
import concurrent.futures
import logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
class OpenAILanguageModel(A... | tree-of-thoughts-main | tree_of_thoughts/models/openai_models.py |
import requests
import os
class Anthropic:
"""Anthropic large language models."""
def __init__(self, model="claude-2", max_tokens_to_sample=256, temperature=None, top_k=None, top_p=None, streaming=False, default_request_timeout=None):
self.model = model
self.max_tokens_to_sample = max_tokens_t... | tree-of-thoughts-main | tree_of_thoughts/models/anthropic.py |
from abc import ABC, abstractmethod
class AbstractLanguageModel(ABC):
@abstractmethod
def generate_thoughts(self, state, k):
pass
@abstractmethod
def evaluate_states(self, states):
pass
| tree-of-thoughts-main | tree_of_thoughts/models/abstract_language_model.py |
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import pipeline
from tree_of_thoughts.models.abstract_language_model import AbstractLanguageModel
class HuggingLanguageModel(AbstractLanguageModel):
def __init__(self, model_name, model_tokenizer=None, verbose=False):
self.mode... | tree-of-thoughts-main | tree_of_thoughts/models/huggingface_model.py |
from setuptools import setup, find_packages
setup(
name = 'omnimorph',
packages = find_packages(exclude=[]),
version = '0.0.7',
license='MIT',
description = 'OmniMorph - Pytorch',
author = 'Agora',
author_email = 'kye@apac.ai',
long_description_content_type = 'text/markdown',
url = 'https://github.co... | OmniMorph-master | setup.py |
import torch
import torch.nn as nn
class VisionLanguageEmbedding(nn.Module):
def __init__(self, text_embed, vision_embed):
super().__init__()
self.text_embed = text_embed
self.vision_embed = vision_embed
def forward(self, textual_tokens, visual_tokens, **kwargs):
if textual_tok... | OmniMorph-master | OmniMorph.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
class VisionLanguageEmbedding(nn.Module):
def __init__(self, text_embed, vision_embed):
super().__init__()
self.text_embed = text_embed
self.vision_embed = vision_embed
def forward(self, textual_tokens, visual_tokens,... | OmniMorph-master | iterations/OMNI.py |
import torch
import torch.nn as nn
class VisionLanguageEmbedding(nn.Module):
def __init__(self, text_embed, vision_embed):
super().__init__()
self.text_embed = text_embed
self.vision_embed = vision_embed
def forward(self, textual_tokens, visual_tokens, **kwargs):
if textual_tok... | OmniMorph-master | iterations/OMNI4.py |
import torch
import torch.nn as nn
class OmniMorph(nn.Module):
def __init__(self, *args, **kwargs):
super().__init__()
self._embedding_registry = {}
self._embedding_instances = {}
def register_embedding(self, modality_type, embedding_class):
self._embedding_registry[modality_... | OmniMorph-master | iterations/OMNI3.py |
import torch
import torch.nn as nn
class VisionLanguageEmbedding(nn.Module):
def __init__(self, text_embed, vision_embed):
super().__init__()
self.text_embed = text_embed
self.vision_embed = vision_embed
def forward(self, textual_tokens, visual_tokens, **kwargs):
if textual_... | OmniMorph-master | iterations/OMNI2.py |
from setuptools import setup, find_packages
setup(
name = 'blockwise-parallel-transformer',
packages = find_packages(exclude=[]),
version = '0.1.2',
license='MIT',
description = '32x Faster Attentionn',
author = 'Kye Gomez',
author_email = 'kye@apac.ai',
long_description_content_type = 'text/markdown',... | Blockwise-Parallel-Transformer-main | setup.py |
from jax import random
from blockwise_parallel import BlockwiseParallelTransformerAttention
from torch.nn import Embedding
#hyperparams
input_size = 512
num_heads = 8
hidden_size = 512
num_layers = 6
max_seq_len = 1024
block_size = 64
#create random input sequence
key = random.PRNGKey(0)
x = random.normal(key, (1, m... | Blockwise-Parallel-Transformer-main | example.py |
Blockwise-Parallel-Transformer-main | blockwise-parallel-transformer/bpt/__init__.py | |
# coding=utf-8
# Copyright 2021 The EleutherAI and The HuggingFace Inc. team.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requi... | Blockwise-Parallel-Transformer-main | blockwise-parallel-transformer/bpt/model.py |
import dataclasses
import pprint
from functools import partial
import re
from tqdm import tqdm, trange
import numpy as np
import bpt.tools.utils as utils
import jax
import jax.numpy as jnp
from jax.experimental.pjit import pjit
from jax.sharding import PartitionSpec as PS
import flax
from flax import linen as nn
from... | Blockwise-Parallel-Transformer-main | blockwise-parallel-transformer/bpt/train.py |
import dataclasses
import pprint
import time
from functools import partial
import json
from multiprocessing import Pool
import h5py
import bpt.tools.utils as utils
from ml_collections.config_dict import config_dict
from ml_collections import ConfigDict
from tqdm import tqdm, trange
import numpy as np
from datasets im... | Blockwise-Parallel-Transformer-main | blockwise-parallel-transformer/bpt/data.py |
import functools
import json
import math
from functools import partial
from typing import Optional, Tuple
import flax.linen as nn
import jax
import jax.numpy as jnp
import numpy as np
from einops import rearrange
from flax.linen import combine_masks, make_causal_mask
from jax import lax
from jax import numpy as jnp
... | Blockwise-Parallel-Transformer-main | blockwise-parallel-transformer/bpt/blocks/vanilla.py |
import functools
import json
import math
from functools import partial
from typing import Callable, NamedTuple, Optional
import flax.linen as nn
import jax
import jax.numpy as jnp
import numpy as np
from einops import rearrange
from flax.linen import combine_masks, make_causal_mask
from jax import lax
from jax import ... | Blockwise-Parallel-Transformer-main | blockwise-parallel-transformer/bpt/blocks/blockwise_parallel_v1.py |
Blockwise-Parallel-Transformer-main | blockwise-parallel-transformer/bpt/blocks/__init__.py | |
import functools
import json
import math
from functools import partial
from typing import Callable, NamedTuple, Optional
import flax.linen as nn
import jax
import jax.numpy as jnp
import numpy as np
from einops import rearrange
from flax.linen import combine_masks, make_causal_mask
from jax import lax
from jax import ... | Blockwise-Parallel-Transformer-main | blockwise-parallel-transformer/bpt/blocks/blockwise_parallel.py |
import functools
import json
import math
from functools import partial
from typing import Callable, NamedTuple, Optional
import flax.linen as nn
import jax
import jax.numpy as jnp
import numpy as np
from einops import rearrange
from flax.linen import combine_masks, make_causal_mask
from jax import lax
from jax import ... | Blockwise-Parallel-Transformer-main | blockwise-parallel-transformer/bpt/blocks/memeff.py |
import os
import numpy as np
from ml_collections import ConfigDict
import bpt.tools.utils as utils
import jax
import jax.numpy as jnp
import flax
from flax.serialization import (
from_bytes, to_bytes, to_state_dict, from_state_dict
)
from flax.traverse_util import flatten_dict, unflatten_dict, empty_node
import msg... | Blockwise-Parallel-Transformer-main | blockwise-parallel-transformer/bpt/tools/checkpoint.py |
Blockwise-Parallel-Transformer-main | blockwise-parallel-transformer/bpt/tools/__init__.py | |
import os
import math
from typing import Any, Mapping, Text, Tuple, Union, NamedTuple
from functools import partial
import re
import dataclasses
import random
import dill
import flax
import jax
import jax.numpy as jnp
from jax.sharding import PartitionSpec as PS
from jax.sharding import Mesh
from jax.experimental.pjit... | Blockwise-Parallel-Transformer-main | blockwise-parallel-transformer/bpt/tools/jax_utils.py |
import os
import time
from typing import Any, Mapping, Text, Tuple, Union, NamedTuple
from functools import partial
import re
import dataclasses
import random
from ml_collections.config_dict import config_dict
from ml_collections import ConfigDict
import jax
import jax.numpy as jnp
import numpy as np
from absl import ... | Blockwise-Parallel-Transformer-main | blockwise-parallel-transformer/bpt/tools/optimizers.py |
import inspect
import logging
import os
import pprint
import random
import tempfile
import time
import uuid
from concurrent.futures import ThreadPoolExecutor
from copy import copy
from io import BytesIO
from socket import gethostname
import dataclasses
import absl.flags
import absl.logging
import cloudpickle as pickle... | Blockwise-Parallel-Transformer-main | blockwise-parallel-transformer/bpt/tools/utils.py |
import torch
import torch.nn as nn
class BlockwiseParallelTransformerAttention(nn.Module):
def __init__(self, input_size, num_heads, hidden_size, num_layers, max_seq_len, block_size):
super(BlockwiseParallelTransformerAttention, self).__init__()
self.input_size = input_size
self.num_heads ... | Blockwise-Parallel-Transformer-main | blockwise_parallel/blockwise_parallel_torch.py |
import torch
import torch.nn as nn
class BlockwiseParallelTransformer(nn.Module):
def __init__(self, input_dim, output_dim, head_dim, num_heads, num_query_blocks, num_kv_blocks):
super(BlockwiseParallelTransformer, self).__init__()
self.query_blocks = num_query_blocks
self.kv_blocks = num_k... | Blockwise-Parallel-Transformer-main | blockwise_parallel/test1.py |
# from blockwise_parallel.blockwise_paralle import BlockwiseParallelTransformerAttention
# from blockwise_parallel.test1 import BlockwiseParallelTransformer/
from blockwise_parallel.blockwise_parallel_jax import BlockwiseParallelTransformerAttention | Blockwise-Parallel-Transformer-main | blockwise_parallel/__init__.py |
# import jax
# import jax.numpy as jnp
# from jax import nn, lax
# from jax.experimental.stax import Dense
# class BlockwiseParallelTransformerAttention:
# def __init__(self, input_size, num_heads, hidden_size, num_layers, max_seq_len, block_size):
# self.input_size = input_size
# self.num_heads = ... | Blockwise-Parallel-Transformer-main | blockwise_parallel/blockwise_parallel_jax.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from einops import rearrange
from types import partial
def quick_gelu(x):
return x * torch.sigmoid(1.702 * x)
ACT2FN = {
"gelu": F.gelu,
"relu": F.relu,
"silu": F.silu,
"swish": F.swish,
"gelu_new": quick_... | Blockwise-Parallel-Transformer-main | blockwise_parallel/blockwise_torch.py |
import torch
import numpy
#prexisting arrays
w = torch.tensor([1, 2, 3])
#tuple
w = torch.tensor((1, 2, 3))
# numpy array
w = torch.tensor(numpy.array([1, 2, 3]))
#init by sized
w = torch.empty(100, 200) #not initialized
w = torch.zeros(100, 200) # elements with 0.0
w = torch.ones(100, 200) # elements with 1.0
#... | TorchPractice-main | TorchPractice/tensors.py |
TorchPractice-main | TorchPractice/__init__.py | |
from setuptools import setup, find_packages
setup(
name = 'optimus-prime-transformers',
packages = find_packages(exclude=['examples']),
version = '1.2.1',
license='MIT',
description = 'optimus-prime - Pytorch',
author = 'Kye Gomez',
author_email = 'kye@apac.ai',
url = 'https://github.com/kyegomez/Optim... | Optimus-Prime-main | setup.py |
import gzip
import tqdm
import torch
import random
import numpy as np
from torch.utils.data import Dataset, DataLoader
from optimus_prime import TransformerWrapper, Decoder, AutoregressiveWrapper, AndromedaEmbedding
# constants
NUM_BATCHES = int(1e5)
BATCH_SIZE = 4
GRADIENT_ACCUMULATE_EVERY = 1
LEARNING_RATE = 1e-... | Optimus-Prime-main | train.py |
import torch
from optimus_prime.attend import Attend
model = Attend(dim=512, dim_head=64, heads=64, q_bucket_size=128, k_bucket_size=128, parallel=False, mixed_precision=False, Flash2=True)
q = torch.randn(1, 8, 512, 64)
k = torch.randn(1, 8, 512, 64)
v = torch.randn(1, 8, 512, 64)
out, _ = model(q, k, v)
assert out.... | Optimus-Prime-main | simple.py |
import torch
from torch import nn
import torch.nn.functional as F
from einops import rearrange, pack, unpack
from optimus_prime.autoregressive_wrapper import top_k, eval_decorator
# helper functions
def exists(val):
return val is not None
def divisible_by(numer, denom):
return (numer % denom) == 0
# xl a... | Optimus-Prime-main | optimus_prime/xl_autoregressive_wrapper.py |
from math import ceil
import torch
from torch import nn
import torch.nn.functional as F
from einops import rearrange, pack, unpack
def exists(val):
return val is not None
def eval_decorator(fn):
def inner(self, *args, **kwargs):
was_training = self.training
self.eval()
out = fn(self, ... | Optimus-Prime-main | optimus_prime/autoregressive_wrapper.py |
#add ability to choose your own tokenizer, and embedder, and ask what else can be done for production level training
import math
from random import random
import torch
from torch import nn, einsum, Tensor
import torch.nn.functional as F
from functools import partial, wraps
from inspect import isfunction
from datacl... | Optimus-Prime-main | optimus_prime/x_transformers.py |
import torch
from packaging import version
if version.parse(torch.__version__) >= version.parse('2.0.0'):
from einops._torch_specific import allow_ops_in_compiled_graph
allow_ops_in_compiled_graph()
from x_transformers.x_transformers import XTransformer, Encoder, Decoder, CrossAttender, Attention, Transforme... | Optimus-Prime-main | optimus_prime/__init__.py |
import torch
from torch import nn
import torch.nn.functional as F
def exists(val):
return val is not None
class ContinuousAutoregressiveWrapper(nn.Module):
def __init__(self, net, ignore_index = -100, pad_value = 0):
super().__init__()
self.net = net
self.max_seq_len = net.max_seq_len
... | Optimus-Prime-main | optimus_prime/continuous_autoregressive_wrapper.py |
from functools import partial
import torch
from torch import nn, einsum, Tensor
import torch.nn.functional as F
from collections import namedtuple
from functools import wraps
from packaging import version
from dataclasses import dataclass
from einops import rearrange
from optimus_prime.flash import FlashAttention
#... | Optimus-Prime-main | optimus_prime/attend.py |
import math
import torch
from torch import nn, einsum
from torch.autograd.function import Function
from einops import rearrange
from torch.cuda.amp import autocast, GradScaler
from torch.nn import DataParallel
# constants
EPSILON = 1e-10
# helper functions
def exists(val):
return val is not None
def default(... | Optimus-Prime-main | optimus_prime/flash.py |
import math
from random import random
from contextlib import nullcontext
from collections import namedtuple
import torch
import torch.nn.functional as F
from torch import nn
from einops import rearrange
from optimus_prime.x_transformers import TransformerWrapper
from typing import Optional
# constants
Losses = na... | Optimus-Prime-main | optimus_prime/nonautoregressive_wrapper.py |
import logging
import pytest
import torch
from optimus_prime.attend import Attend
# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
def test_forward_pass():
logger.info("Running forward pass test...")
model = Attend(dim=512, dim_head=64, q_bucket_size=128, k_bucket_... | Optimus-Prime-main | tests/attend/attend.py |
import tqdm
import torch
import torch.optim as optim
from optimus_prime_transformers import XTransformer
# constants
NUM_BATCHES = int(1e5)
BATCH_SIZE = 32
LEARNING_RATE = 3e-4
GENERATE_EVERY = 100
NUM_TOKENS = 16 + 2
ENC_SEQ_LEN = 32
DEC_SEQ_LEN = 64 + 1
# helpers
def cycle():
while True:
prefix = tor... | Optimus-Prime-main | examples/toy_tasks/enc_dec_copy.py |
from optimus_prime_transformers import (
TransformerWrapper,
Encoder,
NonAutoregressiveWrapper
)
import tqdm
import gzip
import numpy as np
import torch
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
# constants
NUM_BATCHES = int(1e8)
BATCH_SIZE = 4
GRADIENT_ACCUMULATE_EVERY... | Optimus-Prime-main | examples/enwik8_simple/train_nar.py |
from optimus_prime_transformers import TransformerWrapper, Decoder
from optimus_prime_transformers.autoregressive_wrapper import AutoregressiveWrapper
import random
import tqdm
import gzip
import numpy as np
import torch
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
# constants
NUM_BAT... | Optimus-Prime-main | examples/enwik8_simple/train.py |
from setuptools import setup, find_packages
#
setup(
name = 'hivemind',
packages = find_packages(exclude=[]),
version = '0.0.1',
license='MIT',
description = 'Hive - Pytorch',
author = 'Kye Gomez',
author_email = 'kye@apac.ai',
long_description_content_type = 'text/markdown',
url = 'https://github.c... | Hive-main | setup.py |
print("hello there 😊 ")
| Hive-main | hive/main.py |
from logic_guide import LogicGuide, QuoteGuide, AlgebraGuide, MemoryGuide
# Example usage:
model_id="tiiuae/falcon-40b"
logic_guide = LogicGuide(model_id=model_id)
#provide few shot prompt for better results
text = """
Context: Every dog is small. Every feline is a snake. Every animal is not bitter. Sheep are bitter... | LOGICGUIDE-main | example_huggingface.py |
from logic_guide import LogicGuide
logic_guide = LogicGuide(openai_api_key='', openai_api_model='gpt4')
#provide few shot prompt for better results
text = """
Context: Every dog is small. Every feline is a snake. Every animal is not bitter. Sheep are bitter. Cats are
carnivores. Each vertebrate is a mammal. Mammals ... | LOGICGUIDE-main | example_openai.py |
from setuptools import setup, find_packages
setup(
name = 'logic_guide',
packages = find_packages(exclude=['examples']),
version = '0.0.1',
license='APACHE',
description = 'Logic Guide - HF',
author = 'Kye Gomez',
author_email = 'kye@apac.ai',
url = 'https://github.com/kyegomez/LOGICGUIDE',
long_desc... | LOGICGUIDE-main | setup.py |
import re
# class LogicGuide:
# def __init__(self):
# self.delimiters = ("t1", "t2") # example delimiters for guiding text extraction
# def guide_function(self, generated_sequence):
# """Function to define a set of valid generations based on previously generated sequences."""
# # Impl... | LOGICGUIDE-main | logic_guide/logicguide.py |
from logic_guide.logicguide import AlgebraGuide, LogicGuide, QuoteGuide, MemoryGuide, FactTool, LogicTool, GuideFunction, DigitGuide, UniversalGuide | LOGICGUIDE-main | logic_guide/__init__.py |
import os
import openai
import time
import logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
class OpenAILanguageModel:
def __init__(self, api_key, api_base="", api_model=""):
if api_key == "" or api_key == None:
... | LOGICGUIDE-main | logic_guide/utils/openai.py |
from setuptools import setup, find_packages
setup(
name = 'swarms',
packages = find_packages(exclude=[]),
version = '1.4.1',
license='MIT',
description = 'Swarms - Pytorch',
author = 'Kye Gomez',
author_email = 'kye@apac.ai',
long_description_content_type = 'text/markdown',
url = 'https://github.com/... | swarms-master | setup.py |
from swarms import Worker
node = Worker(
openai_api_key="",
ai_name="Optimus Prime",
)
task = "What were the winning boston marathon times for the past 5 years (ending in 2022)? Generate a table of the year, name, country of origin, and times."
response = node.run(task)
print(response) | swarms-master | example.py |
from swarms import worker_node
# Your OpenAI API key
api_key = "sksdsds"
# Initialize a WorkerNode with your API key
node = worker_node(api_key)
# Define an objective
objective = "Please make a web GUI for using HTTP API server..."
# Run the task
task = node.run(objective)
print(task)
| swarms-master | playground/worker_auto.py |
from swarms import WorkerUltraUltraNode
# Define an objective
objective = """
Please make a web GUI for using HTTP API server.
The name of it is Swarms.
You can check the server code at ./main.py.
The server is served on localhost:8000.
Users should be able to write text input as 'query' and url array as 'files', ... | swarms-master | playground/ultranode_example.py |
from swarms import HierarchicalSwarm
# Retrieve your API key from the environment or replace with your actual key
api_key = "sksdsds"
# Initialize HierarchicalSwarm with your API key
swarm = HierarchicalSwarm(openai_api_key=api_key)
# Define an objective
objective = """
Please develop and serve a simple web TODO ap... | swarms-master | playground/todo_app.py |
from swarms import HierarchicalSwarm
# Retrieve your API key from the environment or replace with your actual key
api_key = ""
# Initialize HierarchicalSwarm with your API key
swarm = HierarchicalSwarm(api_key)
# Define an objective
objective = "Find 20 potential customers for a HierarchicalSwarm based AI Agent auto... | swarms-master | playground/swarms_example.py |
import os
from swarms.swarms.swarms import WorkerUltra
api_key = os.getenv("OPENAI_API_KEY")
# Define an objective
objective = """
Please make a web GUI for using HTTP API server.
The name of it is Swarms.
You can check the server code at ./main.py.
The server is served on localhost:8000.
Users should be able to writ... | swarms-master | playground/worker_ultra.py |
swarms-master | playground/DIY.py | |
from swarms import swarm
# Use the function
api_key = "APIKEY"
objective = "What is the capital of the UK?"
result = swarm(api_key, objective)
print(result) # Prints: "The capital of the UK is London."
| swarms-master | playground/easy_example.py |
from swarms import AutoScaler
auto_scaler = AutoScaler()
auto_scaler.start()
for i in range(100):
auto_scaler.add_task(f"Task {i}")
| swarms-master | playground/autoscaler.py |
from swarms.structs.workflow import Workflow
workflow = Workflow()
workflow.add('Find 50 ceos in linkedin in agriculture ')
| swarms-master | playground/workflow.py |
from ..swarms import HierarchicalSwarm
# Retrieve your API key from the environment or replace with your actual key
api_key = "sksdsds"
# Initialize HierarchicalSwarm with your API key
swarm = HierarchicalSwarm(openai_api_key=api_key)
# Define an objective
objective = """
Please develop and serve a simple community ... | swarms-master | playground/social_app.py |
from swarms import HierarchicalSwarm
# Retrieve your API key from the environment or replace with your actual key
api_key = "sksdsds"
# Initialize HierarchicalSwarm with your API key
swarm = HierarchicalSwarm(openai_api_key=api_key)
# Define an objective
objective = """
Please make a web GUI for using HTTP API serv... | swarms-master | playground/gui_app.py |
from swarms import HierarchicalSwarm
swarm = HierarchicalSwarm(
openai_api_key="key",
model_type="openai",
model_id="gpt-4",
use_vectorstore=False,
use_async=False,
human_in_the_loop=False,
logging_enabled=False
)
#run the swarm with an objective
result = swarm.run("Design a new car")
#... | swarms-master | playground/DIY/hierchical.py |
import pytest
from unittest.mock import Mock
from swarms.swarms.orchestrate import Orchestrator
@pytest.fixture
def mock_agent():
return Mock()
@pytest.fixture
def mock_task():
return {"task_id": 1, "task_data": "data"}
@pytest.fixture
def mock_vector_db():
return Mock()
@pytest.fixture
def orchestrato... | swarms-master | tests/orchestrate.py |
import pytest
import logging
from unittest.mock import patch
from swarms.swarms.swarms import HierarchicalSwarm # replace with your actual module name
@pytest.fixture
def swarm():
return HierarchicalSwarm(
model_id='gpt-4',
openai_api_key='some_api_key',
use_vectorstore=True,
em... | swarms-master | tests/swarms.py |
import pytest
from unittest.mock import Mock, patch
from swarms.agents.agents import AgentNodeInitializer, AgentNode, agent # replace with actual import
# For initializing AgentNodeInitializer in multiple tests
@pytest.fixture
def mock_agent_node_initializer():
with patch('swarms.agents.agents.ChatOpenAI') as moc... | swarms-master | tests/agents/agents.py |
import pytest
from unittest.mock import Mock
from swarms.memory.oceandb import OceanDB
@pytest.fixture
def mock_ocean_client():
return Mock()
@pytest.fixture
def mock_collection():
return Mock()
@pytest.fixture
def ocean_db(mock_ocean_client):
OceanDB.client = mock_ocean_client
return OceanDB()
... | swarms-master | tests/agents/memory/main.py |
import unittest
import os
from unittest.mock import patch
from langchain import HuggingFaceHub, ChatOpenAI
from swarms.models.llm import LLM
class TestLLM(unittest.TestCase):
@patch.object(HuggingFaceHub, '__init__', return_value=None)
@patch.object(ChatOpenAI, '__init__', return_value=None)
def setUp(sel... | swarms-master | tests/agents/models/LLM.py |
import pytest
import torch
from unittest.mock import Mock
from swarms.models.huggingface import HuggingFaceLLM
@pytest.fixture
def mock_torch():
return Mock()
@pytest.fixture
def mock_autotokenizer():
return Mock()
@pytest.fixture
def mock_automodelforcausallm():
return Mock()
@pytest.fixture
def ... | swarms-master | tests/agents/models/hf.py |
import pytest
from unittest.mock import MagicMock, patch
from swarms.worker.worker_node import WorkerNodeInitializer, WorkerNode # replace your_module with actual module name
# Mock Tool for testing
class MockTool(Tool):
pass
# Fixture for llm
@pytest.fixture
def mock_llm():
return MagicMock()
# Fixture fo... | swarms-master | tests/agents/workers/worker_node.py |
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