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import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import gradio as gr
import json
import requests
# ============================================================
# ==================== MODEL + TOKENIZER =====================
# ============================================================
class RWKVMambaHybrid(nn.Module):
def __init__(self, d_model, d_state=64):
super().__init__()
self.d_model = d_model
self.d_state = d_state
self.w_mix = nn.Parameter(torch.ones(d_model) * 0.5)
self.A = nn.Parameter(torch.randn(d_state, d_state) * 0.01)
self.B = nn.Parameter(torch.randn(d_state, d_model) * 0.01)
self.C = nn.Parameter(torch.randn(d_model, d_state) * 0.01)
self.D = nn.Parameter(torch.ones(d_model) * 0.1)
def forward(self, x):
B, T, C = x.shape
h = torch.zeros(B, C, device=x.device)
s = torch.zeros(B, self.d_state, device=x.device)
outputs = []
for t in range(T):
x_t = x[:, t, :]
h = self.w_mix * h + (1 - self.w_mix) * x_t
s = s @ self.A.T + x_t @ self.B.T
y_t = s @ self.C.T + h * self.D
outputs.append(y_t)
return torch.stack(outputs, dim=1)
class FullAttention(nn.Module):
def __init__(self, d_model, n_heads=16):
super().__init__()
self.d_model = d_model
self.n_heads = n_heads
self.head_dim = d_model // n_heads
self.qkv = nn.Linear(d_model, d_model*3)
self.out_proj = nn.Linear(d_model, d_model)
def forward(self, x, mask=None):
B, T, C = x.shape
qkv = self.qkv(x)
q, k, v = qkv.chunk(3, dim=-1)
q = q.view(B, T, self.n_heads, self.head_dim).transpose(1,2)
k = k.view(B, T, self.n_heads, self.head_dim).transpose(1,2)
v = v.view(B, T, self.n_heads, self.head_dim).transpose(1,2)
attn = (q @ k.transpose(-2,-1)) / (self.head_dim**0.5)
if mask is not None:
mask = mask.expand(B, self.n_heads, T, T).bool()
attn = attn.masked_fill(mask==0, float('-inf'))
attn = F.softmax(attn, dim=-1)
out = attn @ v
out = out.transpose(1,2).contiguous().view(B,T,C)
return self.out_proj(out)
class i3HybridBlock(nn.Module):
def __init__(self, d_model, d_state=64, ffn_mult=4):
super().__init__()
self.ln1 = nn.LayerNorm(d_model)
self.hybrid = RWKVMambaHybrid(d_model, d_state)
self.ln2 = nn.LayerNorm(d_model)
d_ff = d_model * ffn_mult
self.ffn = nn.Sequential(nn.Linear(d_model,d_ff), nn.GELU(), nn.Linear(d_ff,d_model))
def forward(self, x, mask=None):
x = x + self.hybrid(self.ln1(x))
x = x + self.ffn(self.ln2(x))
return x
class i3AttentionBlock(nn.Module):
def __init__(self, d_model, n_heads=16, ffn_mult=4):
super().__init__()
self.ln1 = nn.LayerNorm(d_model)
self.attn = FullAttention(d_model,n_heads)
self.ln2 = nn.LayerNorm(d_model)
d_ff = d_model * ffn_mult
self.ffn = nn.Sequential(nn.Linear(d_model,d_ff), nn.GELU(), nn.Linear(d_ff,d_model))
def forward(self, x, mask=None):
x = x + self.attn(self.ln1(x), mask)
x = x + self.ffn(self.ln2(x))
return x
class i3Model(nn.Module):
def __init__(self, vocab_size, d_model=512, n_heads=16, max_seq_len=256, d_state=32):
super().__init__()
self.vocab_size = vocab_size
self.d_model = d_model
self.max_seq_len = max_seq_len
self.embed = nn.Embedding(vocab_size,d_model)
self.pos_embed = nn.Embedding(max_seq_len,d_model)
hybrid_layers = [i3HybridBlock(d_model,d_state) for _ in range(10)]
attention_layers = [i3AttentionBlock(d_model,n_heads) for _ in range(6)]
self.layers = nn.ModuleList(hybrid_layers + attention_layers)
self.ln_f = nn.LayerNorm(d_model)
self.head = nn.Linear(d_model,vocab_size)
self.apply(self._init_weights)
def _init_weights(self,module):
if isinstance(module,(nn.Linear,nn.Embedding)):
module.weight.data.normal_(0,0.02)
if isinstance(module,nn.Linear) and module.bias is not None:
module.bias.data.zero_()
def forward(self, idx, targets=None):
B,T = idx.shape
pos = torch.arange(0,T,device=idx.device).unsqueeze(0)
x = self.embed(idx)+self.pos_embed(pos)
mask = torch.tril(torch.ones(T,T,device=idx.device)).view(1,1,T,T)
for layer in self.layers:
x = layer(x,mask)
x = self.ln_f(x)
logits = self.head(x)
loss=None
if targets is not None:
loss = F.cross_entropy(logits.view(-1,logits.size(-1)), targets.view(-1))
return logits, loss
@torch.no_grad()
def generate(self, idx, max_new_tokens=100, temperature=1.0, top_k=None):
for _ in range(max_new_tokens):
idx_cond = idx if idx.size(1)<=self.max_seq_len else idx[:,-self.max_seq_len:]
logits,_ = self(idx_cond)
logits = logits[:,-1,:]/temperature
if top_k is not None:
v,_ = torch.topk(logits,min(top_k,logits.size(-1)))
logits[logits<v[:,[-1]]]=-float('Inf')
probs = F.softmax(logits,dim=-1)
idx_next = torch.multinomial(probs,1)
idx = torch.cat((idx,idx_next),dim=1)
return idx
class ChunkTokenizer:
def __init__(self, vocab_path=None):
self.chunk_to_idx={}
self.idx_to_chunk={}
self.unk_token='<UNK>'
self.unk_idx=0
if vocab_path and os.path.exists(vocab_path):
with open(vocab_path,'r') as f:
data=json.load(f)
self.chunk_to_idx=data['chunk_to_idx']
self.idx_to_chunk={int(k):v for k,v in data['idx_to_chunk'].items()}
self.vocab_size=data['vocab_size']
else:
# minimal fallback vocab
self.chunk_to_idx={'<UNK>':0,'a':1,'b':2,'c':3,'d':4,'e':5,'f':6,'g':7,'h':8,'i':9,'j':10,'k':11,'l':12,'m':13,'n':14,'o':15,'p':16,'q':17,'r':18,'s':19,'t':20,'u':21,'v':22,'w':23,'x':24,'y':25,'z':26,' ':27}
self.idx_to_chunk={v:k for k,v in self.chunk_to_idx.items()}
self.vocab_size=len(self.chunk_to_idx)
def encode(self,text):
text=text.lower()
idxs=[]
pos=0
while pos<len(text):
chunk=text[pos:pos+3] if pos+3<=len(text) else text[pos:]
if chunk in self.chunk_to_idx:
idxs.append(self.chunk_to_idx[chunk])
pos+=len(chunk)
else:
idxs.append(self.unk_idx)
pos+=1
return idxs
def decode(self,indices):
return ''.join([self.idx_to_chunk.get(int(i),self.unk_token) for i in indices])
# ============================================================
# ===================== LOAD MODEL ===========================
# ============================================================
MODEL_NAME = "your-hf-username/i3-80m" # Replace with HF repo ID
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
vocab_file = "chunk_vocab_combined.json"
tokenizer = ChunkTokenizer(vocab_file)
vocab_size = tokenizer.vocab_size
model = i3Model(vocab_size=vocab_size)
# load local safetensors or pytorch_model.bin if exists
if os.path.exists("model.safetensors"):
from safetensors.torch import load_file
state_dict = load_file("model.safetensors")
model.load_state_dict(state_dict)
elif os.path.exists("pytorch_model.bin"):
state_dict = torch.load("pytorch_model.bin", map_location=DEVICE)
model.load_state_dict(state_dict)
else:
# download from HF
url_bin = f"https://huggingface.co/{MODEL_NAME}/resolve/main/pytorch_model.bin"
r = requests.get(url_bin)
with open("pytorch_model.bin",'wb') as f:
f.write(r.content)
state_dict = torch.load("pytorch_model.bin", map_location=DEVICE)
model.load_state_dict(state_dict)
model.to(DEVICE)
model.eval()
# ============================================================
# ===================== GRADIO UI ============================
# ============================================================
def generate_text(prompt, max_tokens, temperature, top_k):
idx = torch.tensor([tokenizer.encode(prompt)],dtype=torch.long).to(DEVICE)
out_idx = model.generate(idx, max_new_tokens=max_tokens, temperature=temperature, top_k=top_k)
text = tokenizer.decode(out_idx[0].cpu())
return text
with gr.Blocks() as demo:
gr.Markdown("### i3-80M Model Demo")
with gr.Row():
with gr.Column(scale=3):
prompt = gr.Textbox(label="Prompt", lines=3)
generate_btn = gr.Button("Generate")
output = gr.Textbox(label="Generated Text", lines=10)
with gr.Column(scale=1):
gr.Markdown("#### Dev Panel")
max_tokens = gr.Slider(10,512,value=100,step=1,label="Max Tokens")
temperature = gr.Slider(0.1,2.0,value=0.8,step=0.05,label="Temperature")
top_k = gr.Slider(1,100,value=40,step=1,label="Top-k")
generate_btn.click(generate_text, inputs=[prompt,max_tokens,temperature,top_k], outputs=output)
demo.launch()
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