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a.py

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+from qwisp import Wisp
+
+w = Wisp().load(device="cpu")
+
+print(w.chat("Hi",temperature=0.7))

qwisp/__init__.py

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+import argparse
+import json
+import math
+from pathlib import Path
+from contextlib import nullcontext
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import sentencepiece as spm
+
+torch.set_num_threads(12)
+torch.set_num_interop_threads(12)
+
+# ---------------------------------------------------------------------------
+# Model definition — must match the training script exactly, or the
+# checkpoint's state_dict won't line up with the module structure.
+# ---------------------------------------------------------------------------
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim, eps=1e-6):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(dim))
+        self.eps = eps
+    def forward(self, x):
+        return x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps) * self.weight
+
+def rope_cache(seq_len, head_dim, device, dtype):
+    inv_freq = 1.0 / (10000 ** (torch.arange(0, head_dim, 2, device=device, dtype=torch.float32) / head_dim))
+    pos = torch.arange(seq_len, device=device, dtype=torch.float32)
+    freqs = torch.outer(pos, inv_freq)
+    cos = freqs.cos().to(dtype=dtype)[None, None, :, :]
+    sin = freqs.sin().to(dtype=dtype)[None, None, :, :]
+    return cos, sin
+
+def apply_rope(x, cos, sin):
+    x1 = x[..., ::2]
+    x2 = x[..., 1::2]
+    out = torch.empty_like(x)
+    out[..., ::2] = x1 * cos - x2 * sin
+    out[..., 1::2] = x1 * sin + x2 * cos
+    return out
+
+class CausalSelfAttention(nn.Module):
+    def __init__(self, dim, n_head, dropout):
+        super().__init__()
+        assert dim % n_head == 0
+        self.n_head = n_head
+        self.head_dim = dim // n_head
+        assert self.head_dim % 2 == 0
+        self.qkv = nn.Linear(dim, 3 * dim)
+        self.proj = nn.Linear(dim, dim)
+        self.dropout = dropout
+    def forward(self, x):
+        B, T, C = x.shape
+        q, k, v = self.qkv(x).chunk(3, dim=-1)
+        q = q.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
+        k = k.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
+        v = v.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
+        cos, sin = rope_cache(T, self.head_dim, x.device, x.dtype)
+        q = apply_rope(q, cos, sin)
+        k = apply_rope(k, cos, sin)
+        if hasattr(F, "scaled_dot_product_attention"):
+            a = F.scaled_dot_product_attention(q, k, v, dropout_p=0.0, is_causal=True)
+        else:
+            att = (q @ k.transpose(-2, -1)) / math.sqrt(self.head_dim)
+            mask = torch.triu(torch.ones(T, T, device=x.device, dtype=torch.bool), diagonal=1)
+            att = att.masked_fill(mask, float("-inf"))
+            att = F.softmax(att, dim=-1)
+            a = att @ v
+        a = a.transpose(1, 2).contiguous().view(B, T, C)
+        return self.proj(a)
+
+class SwiGLU(nn.Module):
+    def __init__(self, dim, dropout):
+        super().__init__()
+        hidden = 4 * dim
+        self.fc = nn.Linear(dim, hidden * 2)
+        self.proj = nn.Linear(hidden, dim)
+        self.drop = nn.Dropout(dropout)
+    def forward(self, x):
+        x1, x2 = self.fc(x).chunk(2, dim=-1)
+        return self.drop(self.proj(F.silu(x1) * x2))
+
+class Block(nn.Module):
+    def __init__(self, dim, n_head, dropout):
+        super().__init__()
+        self.n1 = RMSNorm(dim)
+        self.attn = CausalSelfAttention(dim, n_head, dropout)
+        self.n2 = RMSNorm(dim)
+        self.mlp = SwiGLU(dim, dropout)
+    def forward(self, x):
+        x = x + self.attn(self.n1(x))
+        x = x + self.mlp(self.n2(x))
+        return x
+
+class GPT(nn.Module):
+    def __init__(self, vocab_size, block_size, n_layer, n_head, n_embd, dropout=0.0):
+        super().__init__()
+        self.block_size = block_size
+        self.tok_emb = nn.Embedding(vocab_size, n_embd)
+        self.drop = nn.Dropout(dropout)
+        self.blocks = nn.ModuleList([Block(n_embd, n_head, dropout) for _ in range(n_layer)])
+        self.norm_f = RMSNorm(n_embd)
+        self.lm_head = nn.Linear(n_embd, vocab_size, bias=False)
+        self.lm_head.weight = self.tok_emb.weight
+    def forward(self, idx):
+        B, T = idx.shape
+        if T > self.block_size:
+            idx = idx[:, -self.block_size:]
+        x = self.tok_emb(idx)
+        x = self.drop(x)
+        for block in self.blocks:
+            x = block(x)
+        x = self.norm_f(x)
+        logits = self.lm_head(x)
+        return logits
+
+def get_stop_ids(sp):
+    ids = set()
+    for piece in ("<|user|>", "<|system|>"):
+        pid = sp.piece_to_id(piece)
+        if pid != sp.unk_id():
+            ids.add(pid)
+    return ids
+
+DEFAULT_CONFIG = {
+    "vocab_size": 32000,
+    "block_size": 512,
+    "n_layer": 10,
+    "n_head": 8,
+    "n_embd": 576,
+}
+
+
+def pick_device():
+    if torch.cuda.is_available():
+        return torch.device("cuda")
+    if torch.backends.mps.is_available():
+        return torch.device("mps")
+    return torch.device("cpu")
+
+_MODULE_DIR = Path(__file__).resolve().parent
+
+DEFAULT_CKPT = _MODULE_DIR / "qWisp-base-v1.pt"
+DEFAULT_TOKENIZER = _MODULE_DIR / "qWisp.model"
+
+class Wisp:
+    def __init__(self):
+        self.device = pick_device()
+        self.model = None
+        self.tokenizer = None
+        self.stop_ids = None
+        self.config = DEFAULT_CONFIG.copy()
+
+    def load(
+        self,
+        ckpt=str(DEFAULT_CKPT),
+        tokenizer=str(DEFAULT_TOKENIZER),
+        device=None,
+    ):
+        if device is not None:
+            self.device = torch.device(device)
+
+        self.tokenizer = spm.SentencePieceProcessor()
+        self.tokenizer.load(tokenizer)
+
+        self.model = GPT(
+            vocab_size=self.config["vocab_size"],
+            block_size=self.config["block_size"],
+            n_layer=self.config["n_layer"],
+            n_head=self.config["n_head"],
+            n_embd=self.config["n_embd"],
+            dropout=0.0,
+        )
+
+        obj = torch.load(ckpt, map_location=self.device)
+
+        state_dict = obj["model"] if isinstance(obj, dict) and "model" in obj else obj
+
+        self.model.load_state_dict(state_dict, strict=True)
+        self.model.to(self.device)
+        self.model.eval()
+
+        self.stop_ids = get_stop_ids(self.tokenizer)
+
+        return self
+
+    def unload(self):
+        self.model = None
+        self.tokenizer = None
+        self.stop_ids = None
+
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+
+    def encode(self, text):
+        if self.tokenizer is None:
+            raise RuntimeError("Model not loaded.")
+        return self.tokenizer.encode(text, out_type=int)
+
+    def decode(self, ids):
+        if self.tokenizer is None:
+            raise RuntimeError("Model not loaded.")
+        return self.tokenizer.decode(ids)
+
+    @torch.no_grad()
+    def generate(
+        self,
+        prompt,
+        max_new_tokens=200,
+        temperature=0.8,
+        top_k=50,
+    ):
+        if self.model is None:
+            raise RuntimeError("Model not loaded.")
+
+        ids = self.tokenizer.encode(prompt, out_type=int)
+
+        if len(ids) == 0:
+            ids = [self.tokenizer.bos_id()]
+
+        x = torch.tensor([ids], dtype=torch.long, device=self.device)
+
+        self.model.eval()
+
+        for _ in range(max_new_tokens):
+            x_cond = x[:, -self.model.block_size :]
+            logits = self.model(x_cond)
+            logits = logits[:, -1] / max(temperature, 1e-6)
+
+            if top_k is not None and top_k > 0:
+                values, _ = torch.topk(logits, min(top_k, logits.size(-1)))
+                logits = torch.where(
+                    logits < values[:, [-1]],
+                    torch.full_like(logits, float("-inf")),
+                    logits,
+                )
+
+            probs = F.softmax(logits, dim=-1)
+
+            next_token = torch.multinomial(probs, 1)
+            token = int(next_token.item())
+
+            if token == self.tokenizer.eos_id() or token in self.stop_ids:
+                break
+
+            x = torch.cat((x, next_token), dim=1)
+
+        return self.tokenizer.decode(x[0].tolist())
+
+    def chat(
+        self,
+        message,
+        max_new_tokens=200,
+        temperature=0.8,
+        top_k=50,
+        syspr="Answer to what user have said."
+    ):
+        prompt = f"<|system|> {syspr}\n<|user|> {message}\n<|assistant|>"
+        return self.generate(
+            prompt,
+            max_new_tokens=max_new_tokens,
+            temperature=temperature,
+            top_k=top_k,
+        )
+
+    def __call__(
+        self,
+        message,
+        max_new_tokens=200,
+        temperature=0.8,
+        top_k=50,
+    ):
+        return self.chat(
+            message,
+            max_new_tokens=max_new_tokens,
+            temperature=temperature,
+            top_k=top_k,
+        )
+
+    def __repr__(self):
+        loaded = self.model is not None
+        return (
+            f"Wisp("
+            f"loaded={loaded}, "
+            f"device='{self.device}', "
+            f"layers={self.config['n_layer']}, "
+            f"hidden={self.config['n_embd']}, "
+            f"vocab={self.config['vocab_size']}"
+            f")"
+        )

qwisp/qWisp-base-v1.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:478b9f78c6c210aa9bcf765135adc414d84ceec32f0b3ce5644024e221a3cf4f
+size 286450027

qwisp/qWisp.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:777da9dd588e01e213044aa2382a078996cfd0c03aa2fa34e219a27af87f9986
+size 768897