From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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44 lines
1.3 KiB
44 lines
1.3 KiB
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") |
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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" # "google/gemma-2-2b" |
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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])
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