| """
|
| LUNA SFT — Interactive Chat
|
| Loads the SFT fine-tuned model once, then lets you chat continuously.
|
|
|
| Usage:
|
| python chat.py
|
| python chat.py --ckpt "D:\\ASTERIZER 2026\\LUNA\\Base\\out\\sft\\model.pth"
|
| python chat.py --max_new 300 --temp 0.7
|
| """
|
|
|
| import sys, argparse, torch
|
| import torch.nn as nn
|
| import torch.nn.functional as F
|
| from pathlib import Path
|
|
|
|
|
|
|
|
|
| class RotaryEmbedding(nn.Module):
|
| def __init__(self, dim, max_seq_len=1024):
|
| super().__init__()
|
| inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
|
| self.register_buffer("inv_freq", inv_freq)
|
| t = torch.arange(max_seq_len).float()
|
| freqs = torch.einsum("i,j->ij", t, inv_freq)
|
| emb = torch.cat([freqs, freqs], dim=-1)
|
| self.register_buffer("cos_cached", emb.cos())
|
| self.register_buffer("sin_cached", emb.sin())
|
|
|
| def forward(self, seq_len):
|
| return self.cos_cached[:seq_len], self.sin_cached[:seq_len]
|
|
|
|
|
| def rotate_half(x):
|
| x1, x2 = x.chunk(2, dim=-1)
|
| return torch.cat([-x2, x1], dim=-1)
|
|
|
|
|
| def apply_rotary(x, cos, sin):
|
| c = cos.unsqueeze(0).unsqueeze(0)
|
| s = sin.unsqueeze(0).unsqueeze(0)
|
| return x * c + rotate_half(x) * s
|
|
|
|
|
| class CausalSelfAttention(nn.Module):
|
| def __init__(self, n_embd, n_head, block_size, rotary_pct=0.25):
|
| super().__init__()
|
| self.n_head = n_head
|
| self.head_dim = n_embd // n_head
|
| self.rot_dim = int(self.head_dim * rotary_pct)
|
| self.c_attn = nn.Linear(n_embd, 3 * n_embd, bias=True)
|
| self.c_proj = nn.Linear(n_embd, n_embd, bias=True)
|
| self.rotary = RotaryEmbedding(self.rot_dim, block_size)
|
|
|
| def forward(self, x):
|
| B, T, C = x.size()
|
| qkv = self.c_attn(x).reshape(B, T, 3, self.n_head, self.head_dim).permute(2, 0, 3, 1, 4)
|
| q, k, v = qkv.unbind(0)
|
| cos, sin = self.rotary(T)
|
| q = torch.cat([apply_rotary(q[..., :self.rot_dim], cos, sin), q[..., self.rot_dim:]], dim=-1)
|
| k = torch.cat([apply_rotary(k[..., :self.rot_dim], cos, sin), k[..., self.rot_dim:]], dim=-1)
|
| y = F.scaled_dot_product_attention(q, k, v, is_causal=True)
|
| return self.c_proj(y.transpose(1, 2).contiguous().view(B, T, C))
|
|
|
|
|
| class MLP(nn.Module):
|
| def __init__(self, n_embd):
|
| super().__init__()
|
| self.fc = nn.Linear(n_embd, 4 * n_embd, bias=True)
|
| self.gelu = nn.GELU()
|
| self.proj = nn.Linear(4 * n_embd, n_embd, bias=True)
|
|
|
| def forward(self, x):
|
| return self.proj(self.gelu(self.fc(x)))
|
|
|
|
|
| class Block(nn.Module):
|
| def __init__(self, n_embd, n_head, block_size):
|
| super().__init__()
|
| self.ln1 = nn.LayerNorm(n_embd)
|
| self.attn = CausalSelfAttention(n_embd, n_head, block_size)
|
| self.ln2 = nn.LayerNorm(n_embd)
|
| self.mlp = MLP(n_embd)
|
|
|
| def forward(self, x):
|
| x = x + self.attn(self.ln1(x))
|
| x = x + self.mlp(self.ln2(x))
|
| return x
|
|
|
|
|
| class LUNAModel(nn.Module):
|
| def __init__(self, vocab_size=50304, block_size=1024,
|
| n_layer=10, n_embd=768, n_head=12):
|
| super().__init__()
|
| self.block_size = block_size
|
| self.wte = nn.Embedding(vocab_size, n_embd)
|
| self.blocks = nn.ModuleList([Block(n_embd, n_head, block_size) for _ in range(n_layer)])
|
| self.ln_f = nn.LayerNorm(n_embd)
|
| self.lm_head = nn.Linear(n_embd, vocab_size, bias=False)
|
| self.lm_head.weight = self.wte.weight
|
|
|
| def forward(self, idx):
|
| x = self.wte(idx)
|
| for block in self.blocks:
|
| x = block(x)
|
| return self.lm_head(self.ln_f(x))
|
|
|
|
|
|
|
|
|
| @torch.no_grad()
|
| def generate(model, input_ids, max_new=200, temperature=0.7,
|
| top_p=0.9, top_k=50, repetition_penalty=1.1, device="cpu"):
|
| ids = input_ids.to(device)
|
| generated = []
|
|
|
| for _ in range(max_new):
|
| logits = model(ids[:, -model.block_size:])[:, -1, :]
|
|
|
|
|
| if repetition_penalty != 1.0:
|
| for tok_id in set(ids[0].tolist()):
|
| if logits[0, tok_id] > 0:
|
| logits[0, tok_id] /= repetition_penalty
|
| else:
|
| logits[0, tok_id] *= repetition_penalty
|
|
|
| if temperature < 1e-6:
|
| next_token = logits.argmax(dim=-1, keepdim=True)
|
| else:
|
| logits = logits / temperature
|
| probs = F.softmax(logits, dim=-1)
|
|
|
|
|
| if top_k > 0:
|
| kval = min(top_k, probs.size(-1))
|
| topk_vals, _ = torch.topk(probs, kval)
|
| probs[probs < topk_vals[:, [-1]]] = 0.0
|
| probs /= probs.sum()
|
|
|
|
|
| if top_p < 1.0:
|
| sorted_probs, sorted_idx = torch.sort(probs, descending=True)
|
| cumsum = torch.cumsum(sorted_probs, dim=-1)
|
| mask = cumsum - sorted_probs > top_p
|
| sorted_probs[mask] = 0.0
|
| sorted_probs /= sorted_probs.sum()
|
| next_token = sorted_idx[0, torch.multinomial(sorted_probs[0], 1)]
|
| else:
|
| next_token = torch.multinomial(probs[0], 1)
|
|
|
| ids = torch.cat([ids, next_token.view(1, 1)], dim=1)
|
| generated.append(next_token.item())
|
|
|
| if next_token.item() == 0:
|
| break
|
|
|
| return generated
|
|
|
|
|
|
|
|
|
|
|
| def format_prompt(instruction, context=""):
|
| inst = instruction.strip()
|
| ctx = context.strip()
|
| if inst and ctx:
|
| return f"### Instruction:\n{inst}\n\n### Input:\n{ctx}\n\n### Response:\n"
|
| elif inst:
|
| return f"### Instruction:\n{inst}\n\n### Response:\n"
|
| else:
|
| return f"### Input:\n{ctx}\n\n### Response:\n"
|
|
|
|
|
|
|
|
|
| def main():
|
| parser = argparse.ArgumentParser(description="LUNA SFT — Interactive Chat")
|
| parser.add_argument("--ckpt", default=r"D:\ASTERIZER 2026\LUNA\Base\out\sft\model.pth")
|
| parser.add_argument("--tok_dir", default="Base/checkpoints/EleutherAI/pythia-160m")
|
| parser.add_argument("--max_new", type=int, default=150)
|
| parser.add_argument("--temp", type=float, default=0.7)
|
| parser.add_argument("--top_p", type=float, default=0.9)
|
| parser.add_argument("--top_k", type=int, default=40)
|
| parser.add_argument("--rep_pen", type=float, default=1.0)
|
| parser.add_argument("--device", default="auto")
|
| args = parser.parse_args()
|
|
|
| device = "cuda" if args.device == "auto" and torch.cuda.is_available() else args.device
|
| if device == "auto":
|
| device = "cpu"
|
| print(f"\nDevice: {device}")
|
|
|
|
|
| print(f"Loading: {args.ckpt}")
|
| ckpt = torch.load(args.ckpt, map_location="cpu", weights_only=True)
|
| state = ckpt["model"] if "model" in ckpt else ckpt
|
| model = LUNAModel()
|
| model.load_state_dict(state, strict=True)
|
| model = model.to(device).eval()
|
| params = sum(p.numel() for p in model.parameters())
|
| print(f" Model loaded: {params:,} parameters")
|
|
|
|
|
| from transformers import AutoTokenizer
|
| tokenizer = AutoTokenizer.from_pretrained(args.tok_dir)
|
| print(f" Tokenizer: {args.tok_dir} (vocab {tokenizer.vocab_size})")
|
|
|
|
|
| print(f"\n{'='*60}")
|
| print(" LUNA — Interactive Chat")
|
| print(f" max_new={args.max_new} temp={args.temp} top_p={args.top_p} top_k={args.top_k}")
|
| print(f" Type your message and press Enter. Type 'quit' to exit.")
|
| print(f"{'='*60}\n")
|
|
|
| while True:
|
| try:
|
| user_input = input("You: ").strip()
|
| except (EOFError, KeyboardInterrupt):
|
| print("\nBye!")
|
| break
|
|
|
| if not user_input:
|
| continue
|
| if user_input.lower() in ("quit", "exit", "q"):
|
| print("Bye!")
|
| break
|
|
|
| prompt = format_prompt(user_input)
|
| ids = tokenizer.encode(prompt, return_tensors="pt")
|
|
|
| tokens = generate(
|
| model, ids,
|
| max_new=args.max_new,
|
| temperature=args.temp,
|
| top_p=args.top_p,
|
| top_k=args.top_k,
|
| repetition_penalty=args.rep_pen,
|
| device=device,
|
| )
|
|
|
| response = tokenizer.decode(tokens, skip_special_tokens=True).strip()
|
|
|
| if "### " in response:
|
| response = response.split("### ")[0].strip()
|
|
|
| print(f"\nLUNA: {response}\n")
|
|
|
|
|
| if __name__ == "__main__":
|
| main()
|
|
|
