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FlawedLLM
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Update app.py
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app.py
CHANGED
@@ -8,11 +8,11 @@ import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
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from huggingface_hub import login, HfFolder
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# tokenizer = AutoTokenizer.from_pretrained("FlawedLLM/Bhashini_gemma_merged16bit_clean_final", trust_remote_code=True)
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quantization_config = BitsAndBytesConfig(
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# model = AutoModelForCausalLM.from_pretrained("FlawedLLM/Bhashini_gemma_merged16bit_clean_final",
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# device_map="auto",
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# quantization_config=quantization_config,
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@@ -37,8 +37,8 @@ quantization_config = BitsAndBytesConfig(
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# )
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tokenizer = AutoTokenizer.from_pretrained("FlawedLLM/Bhashini_gemma_merged4bit_clean_final")
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model = AutoModelForCausalLM.from_pretrained("FlawedLLM/Bhashini_gemma_merged4bit_clean_final",
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device_map="auto",
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quantization_config=quantization_config,)
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# alpaca_prompt = You MUST copy from above!
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@spaces.GPU(duration=300)
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def chunk_it(input_command, item_list):
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
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from huggingface_hub import login, HfFolder
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# tokenizer = AutoTokenizer.from_pretrained("FlawedLLM/Bhashini_gemma_merged16bit_clean_final", trust_remote_code=True)
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# quantization_config = BitsAndBytesConfig(
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# load_in_4bit=True,
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# bnb_4bit_use_double_quant=True,
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# bnb_4bit_quant_type="nf4",
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# bnb_4bit_compute_dtype=torch.float16)
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# model = AutoModelForCausalLM.from_pretrained("FlawedLLM/Bhashini_gemma_merged16bit_clean_final",
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# device_map="auto",
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# quantization_config=quantization_config,
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# )
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tokenizer = AutoTokenizer.from_pretrained("FlawedLLM/Bhashini_gemma_merged4bit_clean_final")
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model = AutoModelForCausalLM.from_pretrained("FlawedLLM/Bhashini_gemma_merged4bit_clean_final",
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device_map="auto",)
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# quantization_config=quantization_config,)
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# alpaca_prompt = You MUST copy from above!
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@spaces.GPU(duration=300)
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def chunk_it(input_command, item_list):
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