app.py

import os
import jax
import jax.numpy as jnp
import flax.linen as nn
import pickle
import numpy as np
import gradio as gr
from huggingface_hub import hf_hub_download

HF_REPO = "MoEprometheus/Prometheus-base"

print("📥 Загружаем Prometheus...")
path = hf_hub_download(HF_REPO, "expert1.pkl")
with open(path, "rb") as f:
    ckpt = pickle.load(f)

itos   = ckpt["vocab"]
stoi   = {v: k for k, v in itos.items()}
CONFIG = ckpt["config"]
encode = lambda s: [stoi.get(c, 0) for c in s]
decode = lambda l: "".join([itos.get(i, "") for i in l])
print(f"✅ Загружено — шаг {ckpt['step']}")

class PrometheusAttention(nn.Module):
    n_heads: int
    n_embed: int
    block_size: int
    dropout: float
    @nn.compact
    def __call__(self, x, deterministic=True):
        B, T, C = x.shape
        head_size = self.n_embed // self.n_heads
        qkv = nn.Dense(3 * self.n_embed, use_bias=False)(x)
        q, k, v = jnp.split(qkv, 3, axis=-1)
        q = q.reshape(B, T, self.n_heads, head_size).transpose(0, 2, 1, 3)
        k = k.reshape(B, T, self.n_heads, head_size).transpose(0, 2, 1, 3)
        v = v.reshape(B, T, self.n_heads, head_size).transpose(0, 2, 1, 3)
        att = (q @ k.transpose(0, 1, 3, 2)) * (head_size ** -0.5)
        mask = jnp.tril(jnp.ones((T, T)))
        att = jnp.where(mask == 0, -1e9, att)
        att = jax.nn.softmax(att, axis=-1)
        att = nn.Dropout(self.dropout)(att, deterministic=deterministic)
        out = (att @ v).transpose(0, 2, 1, 3).reshape(B, T, C)
        out = nn.Dense(self.n_embed)(out)
        return nn.Dropout(self.dropout)(out, deterministic=deterministic)

class PrometheusMLP(nn.Module):
    n_embed: int
    dropout: float
    @nn.compact
    def __call__(self, x, deterministic=True):
        x = nn.Dense(4 * self.n_embed)(x)
        x = nn.gelu(x)
        x = nn.Dense(self.n_embed)(x)
        return nn.Dropout(self.dropout)(x, deterministic=deterministic)

class PrometheusBlock(nn.Module):
    n_embed: int
    n_heads: int
    block_size: int
    dropout: float
    @nn.compact
    def __call__(self, x, deterministic=True):
        x = x + PrometheusAttention(
            self.n_heads, self.n_embed,
            self.block_size, self.dropout
        )(nn.LayerNorm()(x), deterministic)
        x = x + PrometheusMLP(
            self.n_embed, self.dropout
        )(nn.LayerNorm()(x), deterministic)
        return x

class Prometheus(nn.Module):
    vocab_size: int
    n_embed: int
    n_heads: int
    n_layers: int
    block_size: int
    dropout: float
    @nn.compact
    def __call__(self, idx, training=False):
        B, T = idx.shape
        tok = nn.Embed(self.vocab_size, self.n_embed)(idx)
        pos = nn.Embed(self.block_size, self.n_embed)(jnp.arange(T))
        x = nn.Dropout(self.dropout)(tok + pos, deterministic=True)
        BlockRemat = nn.remat(PrometheusBlock, static_argnums=(2,))
        for _ in range(self.n_layers):
            x = BlockRemat(
                self.n_embed, self.n_heads,
                self.block_size, self.dropout
            )(x, True)
        return nn.Dense(self.vocab_size)(nn.LayerNorm()(x))

model = Prometheus(
    vocab_size = CONFIG["vocab_size"],
    n_embed    = CONFIG["n_embed"],
    n_heads    = CONFIG["n_heads"],
    n_layers   = CONFIG["n_layers"],
    block_size = CONFIG["block_size"],
    dropout    = CONFIG["dropout"],
)

params = ckpt["params"]

def generate(prompt, max_new_tokens=80, temperature=1.1):
    tokens = encode(prompt)
    tokens = tokens[-(CONFIG["block_size"]-1):]
    for _ in range(max_new_tokens):
        x = jnp.array([tokens])
        logits = model.apply(params, x, training=False)
        logits = logits[0, -1, :] / temperature
        top_k = 40
        top_k_logits, top_k_indices = jax.lax.top_k(logits, top_k)
        probs = jax.nn.softmax(top_k_logits)
        chosen = int(jax.random.categorical(
            jax.random.PRNGKey(np.random.randint(0, 99999)),
            jnp.log(probs)
        ))
        next_token = int(top_k_indices[chosen])
        tokens.append(next_token)
    return decode(tokens)

def chat(message, history):
    result = generate(message, max_new_tokens=80, temperature=1.1)
    # Убираем промпт из ответа
    if len(result) > len(message):
        answer = result[len(message):]
    else:
        answer = result
    return answer.strip()

demo = gr.ChatInterface(
    fn=chat,
    title="🔥 Prometheus AI",
    description="Языковая модель 1.2B параметров. Создана с нуля одним человеком.",
    examples=[
        "Москва —",
        "Россия — это",
        "Нейронная сеть — это",
        "Python — язык",
    ],
)

demo.launch()