44 lines
1.3 KiB
Python
44 lines
1.3 KiB
Python
import modal
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from modal import App, Volume, Image
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# Setup
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app = modal.App("llama")
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image = Image.debian_slim().pip_install("torch", "transformers", "bitsandbytes", "accelerate","hf-xet")
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secrets = [modal.Secret.from_name("hf-secret")]
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GPU = "T4"
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MODEL_NAME = "meta-llama/Meta-Llama-3.1-8B"
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@app.function(image=image, secrets=secrets, gpu=GPU, timeout=1800)
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def generate(prompt: str) -> str:
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import os
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, set_seed
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# Quant Config
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quant_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_compute_dtype=torch.bfloat16,
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bnb_4bit_quant_type="nf4"
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)
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# Load model and tokenizer
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
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tokenizer.pad_token = tokenizer.eos_token
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tokenizer.padding_side = "right"
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_NAME,
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quantization_config=quant_config,
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device_map="auto"
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)
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set_seed(42)
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inputs = tokenizer.encode(prompt, return_tensors="pt").to("cuda")
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attention_mask = torch.ones(inputs.shape, device="cuda")
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outputs = model.generate(inputs, attention_mask=attention_mask, max_new_tokens=5, num_return_sequences=1)
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return tokenizer.decode(outputs[0]) |