File size: 3,310 Bytes
6f9f476 c94d86b 6f9f476 c94d86b 6f9f476 c94d86b 6f9f476 c94d86b 6f9f476 c94d86b 6f9f476 | 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 | import os, json, asyncio
from fastapi import FastAPI, Request, HTTPException
from fastapi.responses import StreamingResponse
from fastapi.middleware.cors import CORSMiddleware
from huggingface_hub import hf_hub_download
from llama_cpp import Llama
from prompts import build_system_prompt
from search_engine import search_web
app = FastAPI()
app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_methods=["*"], allow_headers=["*"])
MODEL_REPO = "bartowski/Qwen_Qwen3.6-35B-A3B-GGUF"
MODEL_FILE = "Qwen_Qwen3.6-35B-A3B-IQ3_M.gguf"
llm = None
def load_model():
global llm
if llm is None:
print("⬇️ جاري تحميل النموذج...")
model_path = hf_hub_download(repo_id=MODEL_REPO, filename=MODEL_FILE)
llm = Llama(
model_path=model_path,
n_ctx=2048, # متوازن مع 18GB RAM (يمكن رفعه لـ 3076 إذا توفرت رامات إضافية)
n_threads=4,
n_gpu_layers=0,
use_mmap=True,
verbose=False
)
print("✅ تم تحميل النموذج بنجاح.")
@app.on_event("startup")
def startup():
load_model()
def format_qwen_chat(messages: list, system_prompt: str) -> str:
"""بناء قالب محادثة Qwen3 الصحيح مع حفظ السياق"""
prompt = f"<|im_start|>system\n{system_prompt}<|im_end|>\n"
# نحتفظ بآخر 5 رسائل فقط لتوفير سياق الذاكرة على السيرفر المجاني
history = messages[-5:] if len(messages) > 5 else messages
for msg in history:
role = "user" if msg["role"] == "user" else "assistant"
prompt += f"<|im_start|>{role}\n{msg['content']}<|im_end|>\n"
prompt += "<|im_start|>assistant\n"
return prompt
async def generate_stream(messages: list, mode: str):
system_prompt = build_system_prompt(mode)
# وضع البحث: حقن النتائج بتعليمات واضحة
if mode == "search":
query = messages[-1]['content']
search_res = search_web(query)
# نضيف النتائج كرسالة نظام قبل آخر رسالة مستخدم
messages = messages.copy()
messages.insert(-1, {"role": "system", "content": f"[SEARCH RESULTS]\n{search_res}\n\nINSTRUCTION: Use the above results to answer accurately. If irrelevant, rely on your knowledge."})
prompt = format_qwen_chat(messages, system_prompt)
# إعدادات توليد محسنة لنماذج MoE الكبيرة
for token in llm(
prompt,
max_tokens=2048,
stop=["<|im_end|>", "<|user|>"],
stream=True,
temperature=0.7,
repeat_penalty=1.1, # منع التكرار
top_p=0.9
):
yield json.dumps({"token": token["choices"][0]["text"]}) + "\n"
await asyncio.sleep(0.01)
@app.post("/v1/chat/completions")
async def chat_completions(request: Request):
data = await request.json()
messages = data.get("messages", [])
mode = data.get("mode", "chat")
if not messages:
raise HTTPException(400, "No messages provided")
return StreamingResponse(generate_stream(messages, mode), media_type="application/json")
@app.get("/health")
def health():
return {"status": "ok", "model": MODEL_FILE} |