File size: 1,831 Bytes
bf1f7b7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 | import torch
import json
import tiktoken
from safetensors.torch import load_file
from modeling_hrm_cosmicfish import HRMCosmicFish, HRMCosmicFishConfig
def load_model(model_dir, device="cpu"):
with open(f"{model_dir}/config.json") as f:
cfg = json.load(f)
config = HRMCosmicFishConfig(
vocab_size=cfg["vocab_size"],
n_embd=cfg["n_embd"],
block_size=cfg["block_size"],
n_head=cfg["n_head"],
n_kv_head=cfg["n_kv_head"],
n_input_layers=cfg["n_input_layers"],
n_output_layers=cfg["n_output_layers"],
hrm_H_layers=cfg["hrm_H_layers"],
hrm_L_layers=cfg["hrm_L_layers"],
hrm_H_cycles=cfg["hrm_H_cycles"],
hrm_L_cycles=cfg["hrm_L_cycles"],
hrm_max_steps=cfg["hrm_max_steps"],
dropout=0.0,
)
state_dict = load_file(f"{model_dir}/model.safetensors")
model = HRMCosmicFish(config)
model.load_state_dict(state_dict)
model.to(device)
model.eval()
tokenizer = tiktoken.get_encoding("gpt2")
return model, tokenizer
def generate(model, tokenizer, prompt, device="cpu", max_new_tokens=100, temperature=0.7, top_k=40):
tokens = tokenizer.encode(prompt)
idx = torch.tensor(tokens, dtype=torch.long).unsqueeze(0).to(device)
with torch.no_grad():
output = model.generate(idx, max_new_tokens=max_new_tokens, temperature=temperature, top_k=top_k)
return tokenizer.decode(output[0].tolist())
if __name__ == "__main__":
model, tokenizer = load_model(".")
prompts = [
"What is the capital of France?",
"What is artificial intelligence?",
"What does def fibonacci(n): do?",
]
for prompt in prompts:
result = generate(model, tokenizer, prompt)
print(f"Prompt: {prompt}")
print(f"Output: {result}")
print()
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