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generate_example.py

@@ -32,6 +32,11 @@ TEMPERATURE = 0.
 TOP_K = 1
 #######################################
 
+
+###################################
+# Initialize model
+##################################
+
 url = f"https://huggingface.co/rasbt/llama-3.2-from-scratch/resolve/main/{MODEL_FILE}"
 
 if not os.path.exists(MODEL_FILE):
@@ -58,11 +63,25 @@ device = (
 )
 model.to(device)
 
+###################################
+# Initialize tokenizer
+##################################
+TOKENIZER_FILE = "tokenizer.model"
+
+url = f"https://huggingface.co/rasbt/llama-3.2-from-scratch/resolve/main/{TOKENIZER_FILE}"
+
+if not os.path.exists(TOKENIZER_FILE):
+    urllib.request.urlretrieve(url, TOKENIZER_FILE)
+    print(f"Downloaded to {TOKENIZER_FILE}")
 tokenizer = Llama3Tokenizer("tokenizer.model")
 
 if "instruct" in MODEL_FILE:
     tokenizer = ChatFormat(tokenizer)
 
+###################################
+# Generate text
+##################################
+
 torch.manual_seed(123)
 
 start = time.time()

main.py

@@ -0,0 +1,80 @@
+# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
+# Source for "Build a Large Language Model From Scratch"
+# https://github.com/rasbt/LLMs-from-scratch/blob/main/ch05/07_gpt_to_llama/standalone-llama32.ipynb
+
+import time
+import torch
+
+from model import Llama3Model, generate, text_to_token_ids, token_ids_to_text
+from tokenizer import Llama3Tokenizer, ChatFormat, clean_text
+
+#######################################
+# Model settings
+
+MODEL_FILE = "llama3.2-1B-instruct.pth"
+# MODEL_FILE = "llama3.2-1B-base.pth"
+# MODEL_FILE = "llama3.2-3B-instruct.pth"
+# MODEL_FILE = "llama3.2-3B-base.pth"
+
+MODEL_CONTEXT_LENGTH = 8192  # Supports up to 131_072
+
+# Text generation settings
+if "instruct" in MODEL_FILE:
+    PROMPT = "What do llamas eat?"
+else:
+    PROMPT = "Llamas eat"
+
+MAX_NEW_TOKENS = 150
+TEMPERATURE = 0.
+TOP_K = 1
+#######################################
+
+if "1B" in MODEL_FILE:
+    from model import LLAMA32_CONFIG_1B as LLAMA32_CONFIG
+elif "3B" in MODEL_FILE:
+    from model import LLAMA32_CONFIG_3B as LLAMA32_CONFIG
+else:
+    raise ValueError("Incorrect model file name")
+
+model = Llama3Model(LLAMA32_CONFIG)
+
+tokenizer = Tokenizer("tokenizer.model")
+
+if "instruct" in MODEL_FILE:
+    tokenizer = ChatFormat(tokenizer)
+
+model.load_state_dict(torch.load(MODEL_FILE, weights_only=True))
+
+device = (
+    torch.device("cuda") if torch.cuda.is_available() else
+    torch.device("mps") if torch.backends.mps.is_available() else
+    torch.device("cpu")
+)
+model.to(device)
+
+torch.manual_seed(123)
+
+start = time.time()
+
+token_ids = generate(
+    model=model,
+    idx=text_to_token_ids(PROMPT, tokenizer).to(device),
+    max_new_tokens=MAX_NEW_TOKENS,
+    context_size=LLAMA32_CONFIG["context_length"],
+    top_k=TOP_K,
+    temperature=TEMPERATURE
+)
+
+print(f"Time: {time.time() - start:.2f} sec")
+
+if torch.cuda.is_available():
+    max_mem_bytes = torch.cuda.max_memory_allocated()
+    max_mem_gb = max_mem_bytes / (1024 ** 3)
+    print(f"Max memory allocated: {max_mem_gb:.2f} GB")
+
+output_text = token_ids_to_text(token_ids, tokenizer)
+
+if "instruct" in MODEL_FILE:
+    output_text = clean_text(output_text)
+
+print("\n\nOutput text:\n\n", output_text)

tokenizer.py

@@ -10,7 +10,7 @@ import tiktoken
 from tiktoken.load import load_tiktoken_bpe
 
 
-class Tokenizer:
+class Llama3Tokenizer:
     def __init__(self, model_path):
         assert os.path.isfile(model_path), f"Model file {model_path} not found"
         mergeable_ranks = load_tiktoken_bpe(model_path)