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Update app.py
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app.py
CHANGED
@@ -51,65 +51,66 @@ A: Let's find the row of year 2007, that's Row 3. Let's extract the numbers on R
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## alpaca-lora
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model.
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## FLAN-UL2
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@@ -156,7 +157,7 @@ def evaluate(
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elif llm == "flan-ul2":
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output = query({
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"inputs": prompt
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})
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else:
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RuntimeError(f"No such LLM: {llm}")
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## alpaca-lora
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# debugging...
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# assert (
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# "LlamaTokenizer" in transformers._import_structure["models.llama"]
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# ), "LLaMA is now in HuggingFace's main branch.\nPlease reinstall it: pip uninstall transformers && pip install git+https://github.com/huggingface/transformers.git"
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# from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig
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# tokenizer = LlamaTokenizer.from_pretrained("decapoda-research/llama-7b-hf")
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# BASE_MODEL = "decapoda-research/llama-7b-hf"
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# LORA_WEIGHTS = "tloen/alpaca-lora-7b"
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# if torch.cuda.is_available():
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# device = "cuda"
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# else:
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# device = "cpu"
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# try:
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# if torch.backends.mps.is_available():
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# device = "mps"
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# except:
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# pass
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# if device == "cuda":
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# model = LlamaForCausalLM.from_pretrained(
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# BASE_MODEL,
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# load_in_8bit=False,
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# torch_dtype=torch.float16,
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# device_map="auto",
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# )
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# model = PeftModel.from_pretrained(
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# model, LORA_WEIGHTS, torch_dtype=torch.float16, force_download=True
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# )
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# elif device == "mps":
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# model = LlamaForCausalLM.from_pretrained(
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# BASE_MODEL,
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# device_map={"": device},
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# torch_dtype=torch.float16,
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# )
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# model = PeftModel.from_pretrained(
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# model,
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# LORA_WEIGHTS,
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# device_map={"": device},
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# torch_dtype=torch.float16,
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# )
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# else:
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# model = LlamaForCausalLM.from_pretrained(
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# BASE_MODEL, device_map={"": device}, low_cpu_mem_usage=True
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# )
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# model = PeftModel.from_pretrained(
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# model,
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# LORA_WEIGHTS,
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# device_map={"": device},
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# )
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# if device != "cpu":
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# model.half()
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# model.eval()
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# if torch.__version__ >= "2":
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# model = torch.compile(model)
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## FLAN-UL2
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elif llm == "flan-ul2":
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output = query({
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"inputs": prompt
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})
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else:
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RuntimeError(f"No such LLM: {llm}")
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