Open source weights! (#97)

Summary:

Add code to download weights and demo code for running model.

Weights at:
- https://huggingface.co/collections/facebook/blt-6801263d4ac1704702a192a6
- https://huggingface.co/facebook/blt
- https://huggingface.co/facebook/blt-1b
- https://huggingface.co/facebook/blt-7b

Test Plan:

demo.py

@@ -0,0 +1,43 @@
+import os
+
+import torch
+import typer
+
+from bytelatent.distributed import DistributedArgs, setup_torch_distributed
+from bytelatent.generate import load_consolidated_model_and_tokenizer
+from bytelatent.generate_blt import generate_nocache
+from bytelatent.model.blt import ByteLatentTransformer
+from bytelatent.tokenizers.blt_tokenizer import BltTokenizer
+
+
+def main(prompt: str, model_name: str = "blt-1b"):
+    distributed_args = DistributedArgs()
+    distributed_args.configure_world()
+    if not torch.distributed.is_initialized():
+        setup_torch_distributed(distributed_args)
+    checkpoint_path = os.path.join("hf-weights", model_name)
+    print(f"Loading BLT model: {model_name}")
+    model, tokenizer, train_cfg = load_consolidated_model_and_tokenizer(
+        checkpoint_path,
+    )
+    assert isinstance(model, ByteLatentTransformer)
+    assert isinstance(tokenizer, BltTokenizer)
+    patcher_args = train_cfg.data.patcher_args.model_copy(deep=True)
+    patcher_args.realtime_patching = True
+    print("Loading entropy model and patcher")
+    patcher_args.entropy_model_checkpoint_dir = os.path.join(
+        checkpoint_path, "entropy_model"
+    )
+    patcher = patcher_args.build()
+    prompts = [prompt]
+    outputs = generate_nocache(
+        prompts, model=model, tokenizer=tokenizer, patcher=patcher
+    )
+    text_outputs = [tokenizer.decode(t) for t in outputs]
+    for p, t in zip(prompts, text_outputs):
+        print(f'Prompt: "{p}" Completion: "{t}"')
+        print()
+
+
+if __name__ == "__main__":
+    typer.run(main)