2.2 MiB
2.2 MiB
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "tiiuae/falcon-40b"tokenizer = AutoTokenizer.from_pretrained(model)vocab = tokenizer.get_vocab()len(vocab)65024
tokenizer.tokenize('Dzisiaj rano w Poznaniu na Łęgach wylądował latający talerz. Premier')['D', 'z', 'isia', 'j', 'Ġr', 'ano', 'Ġw', 'ĠPoz', 'n', 'aniu', 'Ġna', 'ĠÅģ', 'ÄĻ', 'g', 'ach', 'Ġw', 'yl', 'Äħd', 'owaÅĤ', 'Ġl', 'ata', 'jÄħ', 'cy', 'Ġtal', 'er', 'z', '.', 'ĠPremier']
tokenizer.tokenize('Today a flying saucer has landed in Poznan. The prime')['Today', 'Ġa', 'Ġflying', 'Ġsau', 'cer', 'Ġhas', 'Ġlanded', 'Ġin', 'ĠPoz', 'nan', '.', 'ĠThe', 'Ġprime']
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
A new version of the following files was downloaded from https://huggingface.co/tiiuae/falcon-40b: - configuration_RW.py . Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision. A new version of the following files was downloaded from https://huggingface.co/tiiuae/falcon-40b: - modelling_RW.py . Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.
Downloading shards: 0%| | 0/9 [00:00<?, ?it/s]
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
Loading checkpoint shards: 0%| | 0/9 [00:00<?, ?it/s]
Xformers is not installed correctly. If you want to use memorry_efficient_attention to accelerate training use the following command to install Xformers pip install xformers. The model 'RWForCausalLM' is not supported for text-generation. Supported models are ['BartForCausalLM', 'BertLMHeadModel', 'BertGenerationDecoder', 'BigBirdForCausalLM', 'BigBirdPegasusForCausalLM', 'BioGptForCausalLM', 'BlenderbotForCausalLM', 'BlenderbotSmallForCausalLM', 'BloomForCausalLM', 'CamembertForCausalLM', 'CodeGenForCausalLM', 'CpmAntForCausalLM', 'CTRLLMHeadModel', 'Data2VecTextForCausalLM', 'ElectraForCausalLM', 'ErnieForCausalLM', 'GitForCausalLM', 'GPT2LMHeadModel', 'GPT2LMHeadModel', 'GPTBigCodeForCausalLM', 'GPTNeoForCausalLM', 'GPTNeoXForCausalLM', 'GPTNeoXJapaneseForCausalLM', 'GPTJForCausalLM', 'LlamaForCausalLM', 'MarianForCausalLM', 'MBartForCausalLM', 'MegaForCausalLM', 'MegatronBertForCausalLM', 'MvpForCausalLM', 'OpenLlamaForCausalLM', 'OpenAIGPTLMHeadModel', 'OPTForCausalLM', 'PegasusForCausalLM', 'PLBartForCausalLM', 'ProphetNetForCausalLM', 'QDQBertLMHeadModel', 'ReformerModelWithLMHead', 'RemBertForCausalLM', 'RobertaForCausalLM', 'RobertaPreLayerNormForCausalLM', 'RoCBertForCausalLM', 'RoFormerForCausalLM', 'RwkvForCausalLM', 'Speech2Text2ForCausalLM', 'TransfoXLLMHeadModel', 'TrOCRForCausalLM', 'XGLMForCausalLM', 'XLMWithLMHeadModel', 'XLMProphetNetForCausalLM', 'XLMRobertaForCausalLM', 'XLMRobertaXLForCausalLM', 'XLNetLMHeadModel', 'XmodForCausalLM'].
pipeline.model.eval()
sequences = pipeline(
"Dzisiaj rano w Piotrkowie Trybunalskim wybuchła epidemia tajemniczego nowego wirusa. Premier",
max_length=200,
do_sample=True,
top_k=100,
temperature=0.7,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
sequences[0]['generated_text']
/home/filipg/miniconda3/envs/torch2b/lib/python3.11/site-packages/transformers/generation/utils.py:1255: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation) warnings.warn( Setting `pad_token_id` to `eos_token_id`:11 for open-end generation.
'Dzisiaj rano w Piotrkowie Trybunalskim wybuchła epidemia tajemniczego nowego wirusa. Premier w osobie Krzysztofa Rutkowskiego ogłosił stan wyjątkowy i areszt domowy. Tymczasem bary i restauracje otwierają się, a ludzie wchodzą do nich.\nBary i restauracje otwierają się, a ludzie wchodzą do nich.\nKiedy wchodzę do baru, to widzę, że po całym mieście otwierają się bary i restauracje. Ludzie wchodzą do tych miejsc, nie zdając sobie sprawy, że wirus może przenosić się w powietrzu. Mimo stanu wyjątkowego, ludzie jakby nic nie widzieli i nie słyszeli.\nJak można'
pipeline.modelRWForCausalLM(
(transformer): RWModel(
(word_embeddings): Embedding(65024, 8192)
(h): ModuleList(
(0-59): 60 x DecoderLayer(
(ln_attn): LayerNorm((8192,), eps=1e-05, elementwise_affine=True)
(ln_mlp): LayerNorm((8192,), eps=1e-05, elementwise_affine=True)
(self_attention): Attention(
(maybe_rotary): RotaryEmbedding()
(query_key_value): Linear(in_features=8192, out_features=9216, bias=False)
(dense): Linear(in_features=8192, out_features=8192, bias=False)
(attention_dropout): Dropout(p=0.0, inplace=False)
)
(mlp): MLP(
(dense_h_to_4h): Linear(in_features=8192, out_features=32768, bias=False)
(act): GELU(approximate='none')
(dense_4h_to_h): Linear(in_features=32768, out_features=8192, bias=False)
)
)
)
(ln_f): LayerNorm((8192,), eps=1e-05, elementwise_affine=True)
)
(lm_head): Linear(in_features=8192, out_features=65024, bias=False)
)tokens = tokenizer.tokenize("Dzisiaj rano w Madrycie wylądował latający talerz. Premier")token_ids = tokenizer.convert_tokens_to_ids(tokens)token_ids[47, 101, 33623, 85, 392, 2808, 251, 5509, 547, 9228, 251, 1985, 27255, 38281, 282, 785, 14585, 2586, 3438, 246, 101, 25, 15222]
output = pipeline.model(torch.tensor([token_ids]))outputCausalLMOutputWithCrossAttentions(loss={'logits': tensor([[[ -8.7500, -10.6250, -11.9375, ..., -10.8125, -12.0000, -9.5000],
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...,
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[-13.5000, -14.4375, -18.0000, ..., -12.5000, -16.5000, -8.8750]]],
dtype=torch.bfloat16, grad_fn=<ToCopyBackward0>), 'past_key_values': ((tensor([[[ 0.5547, -1.4062, -0.3340, ..., 0.1514, -0.9297, -1.0312],
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[ 0.5781, 1.2500, 1.0391, ..., 4.0625, -3.5156, -2.3125],
[ 0.6719, 0.7148, 0.9531, ..., 1.0469, -1.4766, -0.2793],
[-0.4727, -0.0156, 0.3750, ..., 2.2344, -2.3281, -0.0830]],
[[-0.2656, -0.6289, -0.9727, ..., 0.3301, 0.3516, -1.3516],
[ 0.3125, 0.0537, 0.1123, ..., 0.2930, 0.2480, 0.2793],
[-0.2490, -0.2793, -0.0850, ..., 0.4082, -2.8750, -1.3750],
...,
[ 0.5781, 1.2500, 1.0391, ..., 4.0625, -3.5156, -2.3125],
[ 0.6719, 0.7148, 0.9531, ..., 1.0469, -1.4766, -0.2793],
[-0.4727, -0.0156, 0.3750, ..., 2.2344, -2.3281, -0.0830]],
...,
[[-0.0134, 0.9180, -0.1533, ..., 2.3906, -3.7188, 1.6250],
[ 0.9141, -0.7188, -0.4258, ..., -0.6445, -0.3828, 1.3047],
[-0.3672, 0.5781, 0.1060, ..., 0.2412, 0.2207, -0.4941],
...,
[ 0.4062, 0.6875, 1.2500, ..., -3.4531, 0.4219, -2.7656],
[-0.8125, 0.6562, 1.0156, ..., -2.9688, -4.0938, -0.3340],
[-1.1797, 0.5312, 0.7070, ..., -3.2500, 0.8203, -2.3281]],
[[-0.0134, 0.9180, -0.1533, ..., 2.3906, -3.7188, 1.6250],
[ 0.9141, -0.7188, -0.4258, ..., -0.6445, -0.3828, 1.3047],
[-0.3672, 0.5781, 0.1060, ..., 0.2412, 0.2207, -0.4941],
...,
[ 0.4062, 0.6875, 1.2500, ..., -3.4531, 0.4219, -2.7656],
[-0.8125, 0.6562, 1.0156, ..., -2.9688, -4.0938, -0.3340],
[-1.1797, 0.5312, 0.7070, ..., -3.2500, 0.8203, -2.3281]],
[[-0.0134, 0.9180, -0.1533, ..., 2.3906, -3.7188, 1.6250],
[ 0.9141, -0.7188, -0.4258, ..., -0.6445, -0.3828, 1.3047],
[-0.3672, 0.5781, 0.1060, ..., 0.2412, 0.2207, -0.4941],
...,
[ 0.4062, 0.6875, 1.2500, ..., -3.4531, 0.4219, -2.7656],
[-0.8125, 0.6562, 1.0156, ..., -2.9688, -4.0938, -0.3340],
[-1.1797, 0.5312, 0.7070, ..., -3.2500, 0.8203, -2.3281]]],
dtype=torch.bfloat16, grad_fn=<ToCopyBackward0>), tensor([[[ 0.4805, -0.1099, -0.3379, ..., -0.0300, 0.0986, 0.2188],
[-0.0181, -0.2637, -0.2461, ..., -0.2080, -0.1777, -0.1348],
[-0.4180, -0.0654, 0.0275, ..., 0.0275, -0.0159, 0.0723],
...,
[ 0.2021, 0.0786, 0.4375, ..., -0.6445, 0.6992, -0.8477],
[ 0.2148, 0.3418, -0.0889, ..., -0.0840, 0.3711, -0.3125],
[ 0.4238, 0.2969, 0.4492, ..., 0.4297, 0.6289, -0.4531]],
[[ 0.4805, -0.1099, -0.3379, ..., -0.0300, 0.0986, 0.2188],
[-0.0181, -0.2637, -0.2461, ..., -0.2080, -0.1777, -0.1348],
[-0.4180, -0.0654, 0.0275, ..., 0.0275, -0.0159, 0.0723],
...,
[ 0.2021, 0.0786, 0.4375, ..., -0.6445, 0.6992, -0.8477],
[ 0.2148, 0.3418, -0.0889, ..., -0.0840, 0.3711, -0.3125],
[ 0.4238, 0.2969, 0.4492, ..., 0.4297, 0.6289, -0.4531]],
[[ 0.4805, -0.1099, -0.3379, ..., -0.0300, 0.0986, 0.2188],
[-0.0181, -0.2637, -0.2461, ..., -0.2080, -0.1777, -0.1348],
[-0.4180, -0.0654, 0.0275, ..., 0.0275, -0.0159, 0.0723],
...,
[ 0.2021, 0.0786, 0.4375, ..., -0.6445, 0.6992, -0.8477],
[ 0.2148, 0.3418, -0.0889, ..., -0.0840, 0.3711, -0.3125],
[ 0.4238, 0.2969, 0.4492, ..., 0.4297, 0.6289, -0.4531]],
...,
[[ 0.3242, -0.4062, -0.1709, ..., 0.2695, -0.5391, -0.3926],
[ 0.2695, -0.2197, 0.2227, ..., -0.2734, -0.5898, 0.0435],
[ 0.8477, -0.6094, -0.0427, ..., -0.1914, -0.5508, -0.7031],
...,
[-1.0234, 1.2969, -0.0723, ..., 0.1797, 0.4980, 0.5195],
[-1.3984, 1.1719, -0.3809, ..., 0.3809, 0.0420, 0.3145],
[-0.1367, -0.0142, -0.7539, ..., 0.3477, -0.4102, 0.3594]],
[[ 0.3242, -0.4062, -0.1709, ..., 0.2695, -0.5391, -0.3926],
[ 0.2695, -0.2197, 0.2227, ..., -0.2734, -0.5898, 0.0435],
[ 0.8477, -0.6094, -0.0427, ..., -0.1914, -0.5508, -0.7031],
...,
[-1.0234, 1.2969, -0.0723, ..., 0.1797, 0.4980, 0.5195],
[-1.3984, 1.1719, -0.3809, ..., 0.3809, 0.0420, 0.3145],
[-0.1367, -0.0142, -0.7539, ..., 0.3477, -0.4102, 0.3594]],
[[ 0.3242, -0.4062, -0.1709, ..., 0.2695, -0.5391, -0.3926],
[ 0.2695, -0.2197, 0.2227, ..., -0.2734, -0.5898, 0.0435],
[ 0.8477, -0.6094, -0.0427, ..., -0.1914, -0.5508, -0.7031],
...,
[-1.0234, 1.2969, -0.0723, ..., 0.1797, 0.4980, 0.5195],
[-1.3984, 1.1719, -0.3809, ..., 0.3809, 0.0420, 0.3145],
[-0.1367, -0.0142, -0.7539, ..., 0.3477, -0.4102, 0.3594]]],
dtype=torch.bfloat16, grad_fn=<ToCopyBackward0>))), hidden_states=None, attentions=None, cross_attentions=None)top = torch.topk(torch.softmax(output[0][0][-1], 0), k=20)
for ix, token_id in enumerate(list(top.indices)):
print('%s %0.5f' % (tokenizer.decode(token_id), top.values[ix].item()))His 0.23145 Pedro 0.07520 zy 0.04590 Mar 0.04102 Donald 0.02307 i 0.02026 David 0.01550 Mate 0.01361 his 0.01361 And 0.01318 Franc 0.01221 owi 0.01215 , 0.01166 Be 0.01160 K 0.01147 J 0.01105 Jose 0.01086 José 0.01074 z 0.00995 Raj 0.00854
tokens = tokenizer.convert_tokens_to_ids(tokenizer.tokenize("Tłumaczenie na język angielski zdania 'Jem kanapkę z serem' to: I eat a"))
with torch.no_grad():
output = pipeline.model(torch.tensor([tokens]))
top = torch.topk(torch.softmax(output[0][0][-1], 0), k=20)
for ix, token_id in enumerate(list(top.indices)):
print('%s %0.5f' % (tokenizer.decode(token_id), top.values[ix].item()))cheese 0.47461 sandwich 0.46094 ham 0.00879 chees 0.00854 sand 0.00781 slice 0.00433 toast 0.00337 bread 0.00336 0.00278 snack 0.00224 piece 0.00218 toasted 0.00187 grilled 0.00174 s 0.00117 cheddar 0.00092 che 0.00087 sandwiches 0.00071 cheesy 0.00054 ser 0.00048 roll 0.00048
outputCausalLMOutputWithCrossAttentions(loss={'logits': tensor([[[ -8.7500, -10.6250, -11.9375, ..., -10.8125, -12.0000, -9.5000],
[ -9.1250, -10.5000, -12.1250, ..., -8.1250, -9.4375, -7.1562],
[-16.1250, -22.2500, -24.0000, ..., -19.1250, -19.5000, -18.6250],
...,
[-13.8750, -16.2500, -20.3750, ..., -12.1250, -17.5000, -11.3750],
[-13.4375, -14.7500, -19.0000, ..., -15.5000, -17.7500, -14.8125],
[-13.3750, -15.8125, -17.7500, ..., -11.9375, -16.0000, -9.5000]]],
dtype=torch.bfloat16), 'past_key_values': ((tensor([[[ 0.5547, -1.4062, -0.3340, ..., 0.1514, -0.9297, -1.0312],
[-0.4785, -1.4062, -0.4316, ..., 0.0583, -1.4062, 0.1992],
[-2.2500, -2.3281, 0.4707, ..., -1.1562, -0.1973, 0.9883],
...,
[-0.1641, 1.3672, -0.2100, ..., 0.0522, -1.4062, 0.1992],
[-0.1484, -0.2168, -0.0801, ..., -0.9414, -0.6211, -1.1953],
[ 1.5781, -1.5859, 0.1396, ..., -0.1191, -0.2041, -0.2812]],
[[ 0.5547, -1.4062, -0.3340, ..., 0.1514, -0.9297, -1.0312],
[-0.4785, -1.4062, -0.4316, ..., 0.0583, -1.4062, 0.1992],
[-2.2500, -2.3281, 0.4707, ..., -1.1562, -0.1973, 0.9883],
...,
[-0.1641, 1.3672, -0.2100, ..., 0.0522, -1.4062, 0.1992],
[-0.1484, -0.2168, -0.0801, ..., -0.9414, -0.6211, -1.1953],
[ 1.5781, -1.5859, 0.1396, ..., -0.1191, -0.2041, -0.2812]],
[[ 0.5547, -1.4062, -0.3340, ..., 0.1514, -0.9297, -1.0312],
[-0.4785, -1.4062, -0.4316, ..., 0.0583, -1.4062, 0.1992],
[-2.2500, -2.3281, 0.4707, ..., -1.1562, -0.1973, 0.9883],
...,
[-0.1641, 1.3672, -0.2100, ..., 0.0522, -1.4062, 0.1992],
[-0.1484, -0.2168, -0.0801, ..., -0.9414, -0.6211, -1.1953],
[ 1.5781, -1.5859, 0.1396, ..., -0.1191, -0.2041, -0.2812]],
...,
[[ 0.4551, -0.1377, -0.2383, ..., 0.9648, -1.7422, 0.1562],
[ 0.2422, -1.1875, -0.4629, ..., -0.1816, -0.8867, -0.7070],
[-1.3672, -1.3750, -1.0781, ..., 0.2119, 1.6172, 0.6758],
...,
[-1.4375, 1.2188, -0.1250, ..., -0.1787, -0.8945, -0.7070],
[-0.1367, 0.2949, -0.0059, ..., 0.7617, -1.8203, 1.6328],
[ 1.4922, 0.6953, -1.4297, ..., 0.9961, 1.2109, 1.5938]],
[[ 0.4551, -0.1377, -0.2383, ..., 0.9648, -1.7422, 0.1562],
[ 0.2422, -1.1875, -0.4629, ..., -0.1816, -0.8867, -0.7070],
[-1.3672, -1.3750, -1.0781, ..., 0.2119, 1.6172, 0.6758],
...,
[-1.4375, 1.2188, -0.1250, ..., -0.1787, -0.8945, -0.7070],
[-0.1367, 0.2949, -0.0059, ..., 0.7617, -1.8203, 1.6328],
[ 1.4922, 0.6953, -1.4297, ..., 0.9961, 1.2109, 1.5938]],
[[ 0.4551, -0.1377, -0.2383, ..., 0.9648, -1.7422, 0.1562],
[ 0.2422, -1.1875, -0.4629, ..., -0.1816, -0.8867, -0.7070],
[-1.3672, -1.3750, -1.0781, ..., 0.2119, 1.6172, 0.6758],
...,
[-1.4375, 1.2188, -0.1250, ..., -0.1787, -0.8945, -0.7070],
[-0.1367, 0.2949, -0.0059, ..., 0.7617, -1.8203, 1.6328],
[ 1.4922, 0.6953, -1.4297, ..., 0.9961, 1.2109, 1.5938]]],
dtype=torch.bfloat16), tensor([[[-0.0036, 0.0417, 0.0364, ..., -0.0087, -0.0391, -0.0474],
[-0.0889, 0.0193, -0.0654, ..., -0.0253, 0.0302, -0.0610],
[-0.0591, -0.0118, -0.1279, ..., -0.1592, 0.1338, 0.0255],
...,
[-0.0889, 0.0193, -0.0654, ..., -0.0253, 0.0302, -0.0610],
[ 0.0087, 0.0205, 0.0557, ..., 0.0085, 0.0039, -0.0454],
[-0.0659, 0.0427, 0.1006, ..., 0.1055, -0.0527, -0.0339]],
[[-0.0036, 0.0417, 0.0364, ..., -0.0087, -0.0391, -0.0474],
[-0.0889, 0.0193, -0.0654, ..., -0.0253, 0.0302, -0.0610],
[-0.0591, -0.0118, -0.1279, ..., -0.1592, 0.1338, 0.0255],
...,
[-0.0889, 0.0193, -0.0654, ..., -0.0253, 0.0302, -0.0610],
[ 0.0087, 0.0205, 0.0557, ..., 0.0085, 0.0039, -0.0454],
[-0.0659, 0.0427, 0.1006, ..., 0.1055, -0.0527, -0.0339]],
[[-0.0036, 0.0417, 0.0364, ..., -0.0087, -0.0391, -0.0474],
[-0.0889, 0.0193, -0.0654, ..., -0.0253, 0.0302, -0.0610],
[-0.0591, -0.0118, -0.1279, ..., -0.1592, 0.1338, 0.0255],
...,
[-0.0889, 0.0193, -0.0654, ..., -0.0253, 0.0302, -0.0610],
[ 0.0087, 0.0205, 0.0557, ..., 0.0085, 0.0039, -0.0454],
[-0.0659, 0.0427, 0.1006, ..., 0.1055, -0.0527, -0.0339]],
...,
[[-0.0593, -0.0640, -0.0276, ..., -0.0116, -0.0459, -0.0016],
[-0.1680, 0.1338, 0.0145, ..., 0.0097, -0.0281, 0.0104],
[-0.0708, -0.0918, 0.2285, ..., -0.0635, 0.1396, 0.0603],
...,
[-0.1680, 0.1338, 0.0145, ..., 0.0097, -0.0281, 0.0104],
[-0.0923, 0.0505, 0.0068, ..., 0.0239, -0.0119, 0.0031],
[-0.2773, -0.3125, -0.3086, ..., 0.0464, 0.1826, -0.2871]],
[[-0.0593, -0.0640, -0.0276, ..., -0.0116, -0.0459, -0.0016],
[-0.1680, 0.1338, 0.0145, ..., 0.0097, -0.0281, 0.0104],
[-0.0708, -0.0918, 0.2285, ..., -0.0635, 0.1396, 0.0603],
...,
[-0.1680, 0.1338, 0.0145, ..., 0.0097, -0.0281, 0.0104],
[-0.0923, 0.0505, 0.0068, ..., 0.0239, -0.0119, 0.0031],
[-0.2773, -0.3125, -0.3086, ..., 0.0464, 0.1826, -0.2871]],
[[-0.0593, -0.0640, -0.0276, ..., -0.0116, -0.0459, -0.0016],
[-0.1680, 0.1338, 0.0145, ..., 0.0097, -0.0281, 0.0104],
[-0.0708, -0.0918, 0.2285, ..., -0.0635, 0.1396, 0.0603],
...,
[-0.1680, 0.1338, 0.0145, ..., 0.0097, -0.0281, 0.0104],
[-0.0923, 0.0505, 0.0068, ..., 0.0239, -0.0119, 0.0031],
[-0.2773, -0.3125, -0.3086, ..., 0.0464, 0.1826, -0.2871]]],
dtype=torch.bfloat16)), (tensor([[[ 2.4531, 1.6719, -4.0938, ..., -1.4219, 0.8477, 1.2422],
[ 7.1250, 2.4375, -2.5938, ..., -1.1406, 0.6445, 1.5938],
[ 4.6875, 4.4375, -2.6875, ..., -0.1709, 0.5898, 3.0156],
...,
[-3.0156, -2.6562, -2.6562, ..., -2.2656, 0.7383, 3.3906],
[-2.3125, -2.8281, -2.1562, ..., 0.1621, 2.0000, -1.0938],
[-5.6250, -4.4062, -2.9062, ..., -1.9922, 1.0938, 1.3516]],
[[ 2.4531, 1.6719, -4.0938, ..., -1.4219, 0.8477, 1.2422],
[ 7.1250, 2.4375, -2.5938, ..., -1.1406, 0.6445, 1.5938],
[ 4.6875, 4.4375, -2.6875, ..., -0.1709, 0.5898, 3.0156],
...,
[-3.0156, -2.6562, -2.6562, ..., -2.2656, 0.7383, 3.3906],
[-2.3125, -2.8281, -2.1562, ..., 0.1621, 2.0000, -1.0938],
[-5.6250, -4.4062, -2.9062, ..., -1.9922, 1.0938, 1.3516]],
[[ 2.4531, 1.6719, -4.0938, ..., -1.4219, 0.8477, 1.2422],
[ 7.1250, 2.4375, -2.5938, ..., -1.1406, 0.6445, 1.5938],
[ 4.6875, 4.4375, -2.6875, ..., -0.1709, 0.5898, 3.0156],
...,
[-3.0156, -2.6562, -2.6562, ..., -2.2656, 0.7383, 3.3906],
[-2.3125, -2.8281, -2.1562, ..., 0.1621, 2.0000, -1.0938],
[-5.6250, -4.4062, -2.9062, ..., -1.9922, 1.0938, 1.3516]],
...,
[[ 0.1846, 0.7070, -2.5938, ..., -1.6562, 3.1719, -1.0938],
[ 3.0312, 1.2500, -1.8672, ..., -2.9219, 2.3594, -0.6680],
[ 5.0625, 0.7070, -0.4258, ..., -4.0938, 3.5781, -3.2344],
...,
[-0.9375, -2.8438, -1.4531, ..., -2.8594, 4.0312, -3.2188],
[ 0.1758, -0.5859, -0.0127, ..., -4.4688, 2.4375, -1.9375],
[-5.9688, -1.0625, -4.2500, ..., -2.6406, 3.4844, -0.4336]],
[[ 0.1846, 0.7070, -2.5938, ..., -1.6562, 3.1719, -1.0938],
[ 3.0312, 1.2500, -1.8672, ..., -2.9219, 2.3594, -0.6680],
[ 5.0625, 0.7070, -0.4258, ..., -4.0938, 3.5781, -3.2344],
...,
[-0.9375, -2.8438, -1.4531, ..., -2.8594, 4.0312, -3.2188],
[ 0.1758, -0.5859, -0.0127, ..., -4.4688, 2.4375, -1.9375],
[-5.9688, -1.0625, -4.2500, ..., -2.6406, 3.4844, -0.4336]],
[[ 0.1846, 0.7070, -2.5938, ..., -1.6562, 3.1719, -1.0938],
[ 3.0312, 1.2500, -1.8672, ..., -2.9219, 2.3594, -0.6680],
[ 5.0625, 0.7070, -0.4258, ..., -4.0938, 3.5781, -3.2344],
...,
[-0.9375, -2.8438, -1.4531, ..., -2.8594, 4.0312, -3.2188],
[ 0.1758, -0.5859, -0.0127, ..., -4.4688, 2.4375, -1.9375],
[-5.9688, -1.0625, -4.2500, ..., -2.6406, 3.4844, -0.4336]]],
dtype=torch.bfloat16), tensor([[[ 0.0366, 0.0337, 0.0234, ..., 0.1299, 0.2031, -0.1338],
[-0.0269, -0.0304, 0.0752, ..., -0.1118, -0.0603, 0.0413],
[-0.0747, 0.0601, 0.0187, ..., -0.0109, -0.2598, -0.1670],
...,
[ 0.1973, -0.0928, 0.0315, ..., -0.1162, 0.1777, 0.1699],
[-0.0259, -0.0040, 0.0400, ..., 0.0557, -0.0082, -0.0427],
[-0.1729, 0.1797, 0.0096, ..., 0.0938, -0.3145, -0.1206]],
[[ 0.0366, 0.0337, 0.0234, ..., 0.1299, 0.2031, -0.1338],
[-0.0269, -0.0304, 0.0752, ..., -0.1118, -0.0603, 0.0413],
[-0.0747, 0.0601, 0.0187, ..., -0.0109, -0.2598, -0.1670],
...,
[ 0.1973, -0.0928, 0.0315, ..., -0.1162, 0.1777, 0.1699],
[-0.0259, -0.0040, 0.0400, ..., 0.0557, -0.0082, -0.0427],
[-0.1729, 0.1797, 0.0096, ..., 0.0938, -0.3145, -0.1206]],
[[ 0.0366, 0.0337, 0.0234, ..., 0.1299, 0.2031, -0.1338],
[-0.0269, -0.0304, 0.0752, ..., -0.1118, -0.0603, 0.0413],
[-0.0747, 0.0601, 0.0187, ..., -0.0109, -0.2598, -0.1670],
...,
[ 0.1973, -0.0928, 0.0315, ..., -0.1162, 0.1777, 0.1699],
[-0.0259, -0.0040, 0.0400, ..., 0.0557, -0.0082, -0.0427],
[-0.1729, 0.1797, 0.0096, ..., 0.0938, -0.3145, -0.1206]],
...,
[[ 0.0688, 0.0198, 0.0096, ..., -0.0469, -0.0825, -0.0283],
[-0.0437, 0.0231, -0.0981, ..., 0.0354, -0.0835, -0.0356],
[-0.1641, 0.0330, -0.0334, ..., 0.0674, -0.1543, 0.1328],
...,
[ 0.0874, -0.0222, -0.1260, ..., 0.0718, -0.0569, -0.0508],
[-0.0073, -0.0215, 0.0016, ..., 0.0200, 0.0398, 0.0527],
[ 0.0408, 0.1011, 0.2637, ..., -0.0903, 0.0087, 0.0859]],
[[ 0.0688, 0.0198, 0.0096, ..., -0.0469, -0.0825, -0.0283],
[-0.0437, 0.0231, -0.0981, ..., 0.0354, -0.0835, -0.0356],
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