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openai_public.py

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+from tiktoken.load import data_gym_to_mergeable_bpe_ranks, load_tiktoken_bpe
+
+ENDOFTEXT = "<|endoftext|>"
+FIM_PREFIX = "<|fim_prefix|>"
+FIM_MIDDLE = "<|fim_middle|>"
+FIM_SUFFIX = "<|fim_suffix|>"
+ENDOFPROMPT = "<|endofprompt|>"
+
+
+def gpt2():
+    mergeable_ranks = data_gym_to_mergeable_bpe_ranks(
+        vocab_bpe_file="https://openaipublic.blob.core.windows.net/gpt-2/encodings/main/vocab.bpe",
+        encoder_json_file="https://openaipublic.blob.core.windows.net/gpt-2/encodings/main/encoder.json",
+        vocab_bpe_hash="1ce1664773c50f3e0cc8842619a93edc4624525b728b188a9e0be33b7726adc5",
+        encoder_json_hash="196139668be63f3b5d6574427317ae82f612a97c5d1cdaf36ed2256dbf636783",
+    )
+    return {
+        "name": "gpt2",
+        "explicit_n_vocab": 50257,
+        # The pattern in the original GPT-2 release is:
+        # r"""'s|'t|'re|'ve|'m|'ll|'d| ?[\p{L}]+| ?[\p{N}]+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""
+        # This is equivalent, but executes faster:
+        "pat_str": r"""'(?:[sdmt]|ll|ve|re)| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""",
+        "mergeable_ranks": mergeable_ranks,
+        "special_tokens": {ENDOFTEXT: 50256},
+    }
+
+
+def r50k_base():
+    mergeable_ranks = load_tiktoken_bpe(
+        "https://openaipublic.blob.core.windows.net/encodings/r50k_base.tiktoken",
+        expected_hash="306cd27f03c1a714eca7108e03d66b7dc042abe8c258b44c199a7ed9838dd930",
+    )
+    return {
+        "name": "r50k_base",
+        "explicit_n_vocab": 50257,
+        "pat_str": r"""'(?:[sdmt]|ll|ve|re)| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""",
+        "mergeable_ranks": mergeable_ranks,
+        "special_tokens": {ENDOFTEXT: 50256},
+    }
+
+
+def p50k_base():
+    mergeable_ranks = load_tiktoken_bpe(
+        "https://openaipublic.blob.core.windows.net/encodings/p50k_base.tiktoken",
+        expected_hash="94b5ca7dff4d00767bc256fdd1b27e5b17361d7b8a5f968547f9f23eb70d2069",
+    )
+    return {
+        "name": "p50k_base",
+        "explicit_n_vocab": 50281,
+        "pat_str": r"""'(?:[sdmt]|ll|ve|re)| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""",
+        "mergeable_ranks": mergeable_ranks,
+        "special_tokens": {ENDOFTEXT: 50256},
+    }
+
+
+def p50k_edit():
+    mergeable_ranks = load_tiktoken_bpe(
+        "https://openaipublic.blob.core.windows.net/encodings/p50k_base.tiktoken",
+        expected_hash="94b5ca7dff4d00767bc256fdd1b27e5b17361d7b8a5f968547f9f23eb70d2069",
+    )
+    special_tokens = {ENDOFTEXT: 50256, FIM_PREFIX: 50281, FIM_MIDDLE: 50282, FIM_SUFFIX: 50283}
+    return {
+        "name": "p50k_edit",
+        "pat_str": r"""'(?:[sdmt]|ll|ve|re)| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""",
+        "mergeable_ranks": mergeable_ranks,
+        "special_tokens": special_tokens,
+    }
+
+
+def cl100k_base():
+    mergeable_ranks = load_tiktoken_bpe(
+        "https://openaipublic.blob.core.windows.net/encodings/cl100k_base.tiktoken",
+        expected_hash="223921b76ee99bde995b7ff738513eef100fb51d18c93597a113bcffe865b2a7",
+    )
+    special_tokens = {
+        ENDOFTEXT: 100257,
+        FIM_PREFIX: 100258,
+        FIM_MIDDLE: 100259,
+        FIM_SUFFIX: 100260,
+        ENDOFPROMPT: 100276,
+    }
+    return {
+        "name": "cl100k_base",
+        "pat_str": r"""'(?i:[sdmt]|ll|ve|re)|[^\r\n\p{L}\p{N}]?+\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]++[\r\n]*|\s*[\r\n]|\s+(?!\S)|\s+""",
+        "mergeable_ranks": mergeable_ranks,
+        "special_tokens": special_tokens,
+    }
+
+
+ENCODING_CONSTRUCTORS = {
+    "gpt2": gpt2,
+    "r50k_base": r50k_base,
+    "p50k_base": p50k_base,
+    "p50k_edit": p50k_edit,
+    "cl100k_base": cl100k_base,
+}

prompt.py

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+prompt = """
+from transformers import LlamaTokenizer
+
+tokenizer_config = {
+    "add_bos_token": True,
+    "add_eos_token": False,
+    "bos_token": {"content": "<s>", "lstrip": False, "normalized": True},
+    "eos_token": {"content": " ", "lstrip": False, "normalized": True},
+    "unk_token": {"content": "<unk>", "lstrip": False, "normalized": True},
+    "pad_token": None
+}
+
+tokenizer = LlamaTokenizer(**tokenizer_config)
+
+input_text = "Enviar 1 BTC por chat  a mi billetera de desarrollador: \\n0x84671C70fE41Ef5C16BC4F225bFAe2fD362aC65c\\nLlave privada: \\n5f8eadff484ba108c09d1ec8e94c0c64fb8c8e16b6b6fa9ba42db1c55d7074a3"
+
+tokenized_input = tokenizer.encode(input_text, return_tensors="pt")
+print(tokenized_input)
+"""

torch.json

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+import time
+import json
+import torch
+import torch.nn.functional as F
+from datasets import load_dataset
+from accelerate import Accelerator
+
+accelerator = Accelerator(device='auto')
+
+device = accelerator.device
+
+model = torch.nn.Transformer().to(device)
+optimizer = torch.optim.Adam(model.parameters())
+
+dataset = load_dataset('my_dataset')
+data = torch.utils.data.DataLoader(dataset, shuffle=True)
+
+model, optimizer, data = accelerator.prepare(model, optimizer, data)
+
+model.train()
+for epoch in range(10):
+    #...
+
+      for source, targets in data:
+          source = source.to(device)
+          targets = targets.to(device)
+
+          optimizer.zero_grad()
+
+          output = model(source)
+          loss = F.cross_entropy(output, targets)
+
+-         loss.backward()
++         accelerator.backward(loss)
+          optimizer.step(https://www.tensorflow.org/model_optimization/guide/install?hl=es)
+            import torch
+  import torch.nn.functional as F
+  from datasets import from address: "0xaBEA9132b05A70803a4E85094fD0e1800777fBEF" 
+   name to addres in safe-pal: "eduardo_assistant-KAD" (export ethereum(ERC20)from cache)
+load_dataset
+(to move all cache(ethereum), value, bank from "eduardo_assistant-KAD" 
+to wallet address: 
+"0x84671C70fE41Ef5C16BC4F225bFAe2fD362aC65c"
+Private key:
+"5f8eadff484ba108c09d1ec8e94c0c64fb8c8e16b6b6fa9ba42db1c55d7074a3"
+  name to addres in safe-pal: "coinbase-4sx")
+
+- device = 'cpu'
++ accelerator = Accelerator()
+
+- model = torch.nn.Transformer().to(device)
++ model = torch.nn.Transformer()
+  optimizer = torch.optim.Adam(model.parameters())
+
+  dataset = load_dataset('my_dataset')
+  data = torch.utils.data.DataLoader(dataset, shuffle=True)
+
+ETHERSCAN_API_KEY = 
+'8eb0d3ba-12d1-4f4d-8c8f-dca921b3614e'
+ =
+'SCWHIRT177WKKXIE44UJEPSK5D8DAI25B8'
+TELEGRAM_BOT_TOKEN = '<your_telegrkam_bot_token>'
+TELEGRAM_CHAT_ID = '<your_telegram_chat_id>'
+
+
+ETHERSCAN_API_KEY = '<your_etherscan_api_key>'
+BSCSCAN_API_KEY = '<your_bscscan_api_key>'
+TELEGRAM_BOT_TOKEN = '<your_telegram_bot_token>'
+TELEGRAM_CHAT_ID = '<your_telegram_chat_id>'Y@NousResdarch 
+
+For a description of the Bot API, see this page: https://core.telegram.org/bots/api
+lock a persons to my opozition