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Update app.py
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app.py
CHANGED
@@ -7,6 +7,8 @@ import spaces
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import torch
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from transformers import AutoModelForCausalLM, GemmaTokenizerFast, TextIteratorStreamer
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MAX_MAX_NEW_TOKENS = 2048
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DEFAULT_MAX_NEW_TOKENS = 1024
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MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
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@@ -14,11 +16,12 @@ MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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model_id = "google/gemma-2-9b-it"
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tokenizer = GemmaTokenizerFast.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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device_map="auto",
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torch_dtype=torch.bfloat16,
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)
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model.config.sliding_window = 4096
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model.eval()
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import torch
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from transformers import AutoModelForCausalLM, GemmaTokenizerFast, TextIteratorStreamer
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huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
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MAX_MAX_NEW_TOKENS = 2048
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DEFAULT_MAX_NEW_TOKENS = 1024
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MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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model_id = "google/gemma-2-9b-it"
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tokenizer = GemmaTokenizerFast.from_pretrained(model_id, token = huggingface_token)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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device_map="auto",
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torch_dtype=torch.bfloat16,
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token = huggingface_token
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)
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model.config.sliding_window = 4096
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model.eval()
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