philschmid HF staff ArtifactAI commited on
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Duplicate from Artifact-AI/flan-t5-xxl-sharded-fp16

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Co-authored-by: ArtifactAI <Artifact-AI@users.noreply.huggingface.co>

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README.md ADDED
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+ ---
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+ duplicated_from: Artifact-AI/flan-t5-xxl-sharded-fp16
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+ ---
config.json ADDED
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+ {
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+ "_name_or_path": "google/flan-t5-xxl",
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+ "architectures": [
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+ "T5ForConditionalGeneration"
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+ ],
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+ "d_ff": 10240,
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+ "d_kv": 64,
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+ "d_model": 4096,
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+ "decoder_start_token_id": 0,
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+ "dense_act_fn": "gelu",
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+ "dropout_rate": 0.1,
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+ "eos_token_id": 1,
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+ "feed_forward_proj": "gelu",
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+ "initializer_factor": 1.0,
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+ "is_encoder_decoder": true,
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+ "is_gated_act": true,
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+ "layer_norm_epsilon": 1e-06,
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+ "model_type": "t5",
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+ "num_decoder_layers": 24,
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+ "num_heads": 64,
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+ "num_layers": 24,
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+ "output_past": true,
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+ "pad_token_id": 0,
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+ "relative_attention_max_distance": 128,
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+ "relative_attention_num_buckets": 32,
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+ "tie_word_embeddings": false,
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+ "torch_dtype": "float16",
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+ "transformers_version": "4.25.0.dev0",
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+ "use_cache": true,
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+ "vocab_size": 32128
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+ }
handler.py ADDED
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+ from typing import Dict, List, Any
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+ from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
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+ import torch
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+
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+ class EndpointHandler:
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+ def __init__(self, path=""):
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+ # load model and processor from path
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+ self.model = AutoModelForSeq2SeqLM.from_pretrained(path, device_map="auto", load_in_8bit=True)
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+ self.tokenizer = AutoTokenizer.from_pretrained(path)
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+
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+ def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
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+ """
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+ Args:
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+ data (:obj:):
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+ includes the deserialized image file as PIL.Image
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+ """
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+ # process input
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+ inputs = data.pop("inputs", data)
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+ parameters = data.pop("parameters", None)
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+
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+ # preprocess
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+ input_ids = self.tokenizer(inputs, return_tensors="pt").input_ids
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+
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+ # pass inputs with all kwargs in data
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+ if parameters is not None:
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+ outputs = self.model.generate(input_ids, **parameters)
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+ else:
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+ outputs = self.model.generate(input_ids)
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+
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+ # postprocess the prediction
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+ prediction = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
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+
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+ return [{"generated_text": prediction}]
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