Updated README and Week 8 coming together

week8_wip/llama.py

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+import modal
+from modal import App, Volume, Image
+
+# Setup
+
+app = modal.App("llama")
+image = Image.debian_slim().pip_install("torch", "transformers", "bitsandbytes", "accelerate")
+secrets = [modal.Secret.from_name("hf-secret")]
+GPU = "T4"
+MODEL_NAME = "meta-llama/Meta-Llama-3.1-8B"
+
+
+
+@app.function(image=image, secrets=secrets, gpu=GPU)
+def generate(prompt: str) -> str:
+    import os
+    import torch
+    from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, set_seed
+
+    # Quant Config
+    quant_config = BitsAndBytesConfig(
+        load_in_4bit=True,
+        bnb_4bit_use_double_quant=True,
+        bnb_4bit_compute_dtype=torch.bfloat16,
+        bnb_4bit_quant_type="nf4"
+    )
+
+    # Load model and tokenizer
+    
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+    tokenizer.pad_token = tokenizer.eos_token
+    tokenizer.padding_side = "right"
+    
+    model = AutoModelForCausalLM.from_pretrained(
+        MODEL_NAME, 
+        quantization_config=quant_config,
+        device_map="auto"
+    )
+
+    set_seed(42)
+    inputs = tokenizer.encode(prompt, return_tensors="pt").to("cuda")
+    attention_mask = torch.ones(inputs.shape, device="cuda")
+    outputs = model.generate(inputs, attention_mask=attention_mask, max_new_tokens=5, num_return_sequences=1)
+    return tokenizer.decode(outputs[0])