File size: 5,303 Bytes
af08506 | 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 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 | import os
import sys
import torch
from huggingface_hub import hf_hub_download
from tokenizers import Tokenizer
# Ensure UTF-8 console output on Windows
if sys.platform == "win32":
try:
sys.stdout.reconfigure(encoding="utf-8")
sys.stdin.reconfigure(encoding="utf-8")
except Exception:
pass
REPO_ID = "ViuAI/ViuAI-500M"
EOT_ID = 64002
def main():
print("=" * 70)
print("🚀 ViuAI Sarus-500M -- 1-Click Interactive Terminal Chat")
print("=" * 70)
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"[*] Compute Device: {device.upper()}")
# 1. Download or locate local architecture & tokenizer
base_dir = os.path.dirname(os.path.abspath(__file__))
code_dir = os.path.join(base_dir, "code")
if os.path.exists(os.path.join(code_dir, "model.py")):
sys.path.insert(0, code_dir)
else:
print("[*] Downloading model architecture from Hugging Face...")
p_model = hf_hub_download(repo_id=REPO_ID, filename="code/model.py")
p_cfg = hf_hub_download(repo_id=REPO_ID, filename="code/config.py")
sys.path.insert(0, os.path.dirname(p_model))
from model import ViuAI
from config import ViuAIConfig
tok_local = os.path.join(base_dir, "tokenizer", "tokenizer.json")
if not os.path.exists(tok_local):
tok_local = os.path.join(base_dir, "tokenizer.json")
if not os.path.exists(tok_local):
print("[*] Downloading tokenizer...")
tok_local = hf_hub_download(repo_id=REPO_ID, filename="tokenizer.json")
tokenizer = Tokenizer.from_file(tok_local)
cfg = ViuAIConfig(vocab_size=tokenizer.get_vocab_size(), context_length=2048)
model = ViuAI(cfg).to(device)
# 2. Download or locate DPO 5k checkpoint
ckpt_local = os.path.join(base_dir, "dpo_checkpoints", "dpo_5k", "dpo_5k_final.pt")
if not os.path.exists(ckpt_local):
print("[*] Downloading DPO-5K Final Weights (~944 MB)...")
ckpt_local = hf_hub_download(repo_id=REPO_ID, filename="dpo_checkpoints/dpo_5k/dpo_5k_final.pt")
print(f"[*] Loading weights from {os.path.basename(ckpt_local)}...")
state = torch.load(ckpt_local, map_location=device, weights_only=True)
raw_sd = state.get("model", state.get("model_state_dict", state))
clean_sd = {k.replace("_orig_mod.", "").replace("module.", ""): v for k, v in raw_sd.items()}
model.load_state_dict(clean_sd, strict=False)
model.eval()
print("\n[OK] Model Ready! Type 'exit' to quit.\n" + "-" * 70)
while True:
try:
prompt = input("\n👤 USER >> ").strip()
if not prompt:
continue
if prompt.lower() in ["exit", "quit", "q"]:
print("\nSession ended.")
break
formatted = f"<|user|>\n{prompt}<|endofturn|>\n<|assistant|>\n"
ids = tokenizer.encode(formatted).ids
idx = torch.tensor([ids], dtype=torch.long, device=device)
print("🤖 SARUS >> ", end="", flush=True)
generated = []
prev_text = ""
with torch.no_grad():
for _ in range(300):
idx_cond = idx if idx.size(1) <= cfg.context_length else idx[:, -cfg.context_length:]
logits, _ = model(idx_cond)
logits = logits[:, -1, :]
# Repetition penalty
if generated:
gen_t = torch.tensor(list(set(generated)), dtype=torch.long, device=device)
score = logits[:, gen_t]
logits[:, gen_t] = torch.where(score < 0, score * 1.15, score / 1.15)
# Top-K & Top-P Sampling
sampling_logits = logits / 0.15
top_k_val = min(50, sampling_logits.size(-1))
kth_vals, _ = torch.topk(sampling_logits, top_k_val)
sampling_logits[sampling_logits < kth_vals[:, [-1]]] = float("-inf")
sorted_logits, sorted_indices = torch.sort(sampling_logits, descending=True)
cum_probs = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=-1)
remove_mask = cum_probs > 0.90
remove_mask[..., 1:] = remove_mask[..., :-1].clone()
remove_mask[..., 0] = False
indices_to_remove = torch.zeros_like(sampling_logits, dtype=torch.bool).scatter_(1, sorted_indices, remove_mask)
sampling_logits[indices_to_remove] = float("-inf")
probs = torch.softmax(sampling_logits, dim=-1)
idx_next = torch.multinomial(probs, num_samples=1)
token_id = idx_next.item()
if token_id == EOT_ID:
break
generated.append(token_id)
idx = torch.cat([idx, idx_next], dim=1)
curr_text = tokenizer.decode(generated)
chunk = curr_text[len(prev_text):]
print(chunk, end="", flush=True)
prev_text = curr_text
print()
except KeyboardInterrupt:
print("\nSession interrupted.")
break
if __name__ == "__main__":
main()
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