feat: open architecture filter, expand search keywords, let platform decide compatibility

Dockerfile

@@ -4,6 +4,8 @@ ENV PYTHONUNBUFFERED=1
 
 WORKDIR /app
 
+RUN mkdir -p /app/data
+
 COPY requirements.txt .
 RUN pip install --no-cache-dir -r requirements.txt
 

main.py

@@ -31,7 +31,7 @@ PORT = 8080
 STRATEGY_ID = os.getenv("STRATEGY_ID", "")
 
 # 目标GPU
-TARGET_GPU = "ppu_zw_810e"
+TARGET_GPU = "Iluvatar_bi-100"
 
 # 账号Token
 TARGET_TOKEN = "f45f1aae2c094426be237c88b1085015"
@@ -47,7 +47,7 @@ SUPPORTED_SPECIAL_ARCHS = ['Eagle3Speculator', 'LlamaForCausalLMEagle3']
 MODELHUB_API = "https://modelhub.org.cn/api"
 
 # 搜索关键词
-SEARCH_KEYWORDS = ['qwen', 'Qwen2', 'Qwen3', 'Qwen3.5', 'Qwen1.5', 'Qwen-']
+SEARCH_KEYWORDS = ['qwen', 'Qwen2', 'Qwen3', 'Qwen3.5', 'Llama-3', 'Llama-3.1', 'Mistral', 'DeepSeek']
 
 # ============================================================
 # 全局状态
@@ -81,16 +81,35 @@ db_conn = None
 
 def init_db():
     global db_conn
-    db_conn = sqlite3.connect(':memory:', check_same_thread=False)
-    db_conn.execute('''CREATE TABLE IF NOT EXISTS queue (
-        model_id TEXT, gpu TEXT, url TEXT, downloads INTEGER,
-        params TEXT, category TEXT, score REAL,
-        PRIMARY KEY(model_id, gpu)
-    )''')
+    os.makedirs('/app/data', exist_ok=True)
+    db_conn = sqlite3.connect('/app/data/submit_history.db', check_same_thread=False)
     db_conn.execute('''CREATE TABLE IF NOT EXISTS submitted (
-        model_id TEXT, gpu TEXT, task_id TEXT, submitted_at TEXT,
+        model_id TEXT, gpu TEXT, task_id TEXT, status TEXT,
+        submitted_at TEXT, checked_at TEXT,
         PRIMARY KEY(model_id, gpu)
     )''')
+    db_conn.execute('''CREATE TABLE IF NOT EXISTS failed (
+        model_id TEXT, gpu TEXT, reason TEXT, failed_at TEXT,
+        PRIMARY KEY(model_id, gpu)
+    )''')
+    db_conn.commit()
+def is_model_failed(model_id: str) -> bool:
+    """检查模型是否已知失败"""
+    if db_conn:
+        row = db_conn.execute(
+            'SELECT 1 FROM failed WHERE model_id=? AND gpu=?',
+            (model_id, TARGET_GPU)
+        ).fetchone()
+        return row is not None
+    return False
+
+def record_failed(model_id: str, reason: str):
+    """记录失败的模型"""
+    if db_conn:
+        db_conn.execute(
+            'INSERT OR REPLACE INTO failed VALUES (?,?,?,?)',
+            (model_id, TARGET_GPU, reason, datetime.now().isoformat())
+        )
         db_conn.commit()
 
 
@@ -99,15 +118,20 @@ def init_db():
 # ============================================================
 
 MODELSCOPE_API = "https://modelscope.cn/api/v1"
+DOWNLOAD_MIN = 50
+DOWNLOAD_MAX = 5000
+SEARCH_PAGES = 5  # 每个关键词搜5页（50*5=250个结果）
 
 
-def search_models(keyword: str, limit: int = 50) -> list:
-    """从 ModelScope 搜索模型"""
+def search_models(keyword: str) -> list:
+    """从 ModelScope 搜索模型（多页，筛选下载量50-5000的冷门模型）"""
     url = "https://modelscope.cn/openapi/v1/models"
+    models = []
+    for page in range(1, SEARCH_PAGES + 1):
         params = {
             'search': keyword,
-        'page_size': min(limit, 50),
-        'page_number': 1,
+            'page_size': 50,
+            'page_number': page,
             'sort': 'downloads',
         }
         try:
@@ -115,14 +139,21 @@ def search_models(keyword: str, limit: int = 50) -> list:
                                 headers={'User-Agent': 'Mozilla/5.0'})
             data = resp.json()
             if data.get('success'):
-            models = data.get('data', {}).get('models', [])
-            log(f"  [{keyword}]: {len(models)} 个结果")
-            return [{'id': m.get('id'), 'downloads': m.get('downloads', 0)} for m in models]
+                page_models = data.get('data', {}).get('models', [])
+                for m in page_models:
+                    dl = m.get('downloads', 0)
+                    if DOWNLOAD_MIN <= dl <= DOWNLOAD_MAX:
+                        models.append({'id': m.get('id'), 'downloads': dl})
+                if len(page_models) < 50:
+                    break  # 最后一页，不继续
             else:
-            log(f"  [{keyword}]: success=false")
+                break
         except Exception as e:
-        log(f"  [{keyword}]: 失败 {e}")
-    return []
+            log(f"  [{keyword}] page={page}: {e}")
+            break
+        time.sleep(0.3)
+    log(f"  [{keyword}]: {len(models)} 个 (50<={DOWNLOAD_MAX})")
+    return models
 
 
 def check_architecture(model_id: str) -> tuple:
@@ -223,25 +254,26 @@ def check_queue_available() -> int:
 
 
 def build_config_params() -> str:
-    """构建 YAML 配置 - 完全匹配平台自动生成的格式（只支持vllm）"""
+    """构建 YAML 配置 - llama.cpp (支持 GGUF)"""
     params = {
-        'framework': 'vllm',
-        'nv_framework': 'vllm',
+        'framework': 'llama.cpp',
+        'nv_framework': 'llama.cpp',
         'api': 'completion',
         'max_tokens': 1024,
         'temperature': 0.7,
         'repetition_penalty': 1.2,
         'top_p': 0.9,
         'lang': 'zh',
-        'max_model_len': 2048,
+        'max_model_len': 4096,
         'sut_config': {
             'gpu_num': 1,
             'values': {
                 'command': [
-                    'vllm', 'serve', '/model', '--port', '8000',
-                    '--served-model-name', 'llm', '--max-model-len', '2048',
-                    '--dtype', 'auto', '--gpu-memory-utilization', '0.95',
-                    '-tp', '1', '--enforce-eager', '--trust-remote-code',
+                    'llama-server', '--model', '/model', '--alias', 'llm',
+                    '--threads', '20', '--n-gpu-layers', '999', '--prio', '3',
+                    '--min_p', '0.01', '--ctx-size', '4096',
+                    '--host', '0.0.0.0', '--port', '8000',
+                    '--jinja', '--flash-attn', 'off',
                 ]
             }
         },
@@ -249,9 +281,9 @@ def build_config_params() -> str:
             'gpu_num': 1,
             'values': {
                 'command': [
-                    'vllm', 'serve', '/model', '--port', '80',
-                    '--served-model-name', 'llm', '--max-model-len', '4096',
-                    '--enforce-eager', '--trust-remote-code', '-tp', '1',
+                    'llama-server', '--model', '/model', '--alias', 'llm',
+                    '--threads', '20', '--n-gpu-layers', '999',
+                    '--ctx-size', '4096', '--host', '0.0.0.0', '--port', '8000',
                 ]
             }
         },
@@ -271,7 +303,7 @@ def submit_model(model_url: str) -> tuple:
         'modelAddress': normalize_model_url(model_url),
         'taskType': 'text-generation',
         'targetGpu': TARGET_GPU,
-        'framework': 'vllm',
+        'framework': 'llama.cpp',
         'strategyId': STRATEGY_ID,
         'configParams': build_config_params(),
     }
@@ -290,7 +322,7 @@ def submit_model(model_url: str) -> tuple:
 # 主流程
 # ============================================================
 
-def run_pipeline(submit_limit: int = 30):
+def run_pipeline(submit_limit: int = 2):
     """完整流程：搜索→筛选→提交（只针对目标GPU）"""
     init_db()
     log("=" * 50)
@@ -303,47 +335,45 @@ def run_pipeline(submit_limit: int = 30):
     seen = set()
     all_models = []
     for kw in SEARCH_KEYWORDS:
-        models = search_models(kw, limit=100)
+        models = search_models(kw)
         for m in models:
             mid = m.get('id', '')
             if mid and mid not in seen:
                 seen.add(mid)
-                downloads = m.get('downloads', 0)
-                if downloads >= 50:
                 all_models.append({
                     'model_id': mid,
                     'url': f"https://modelscope.cn/{mid}",
-                        'downloads': downloads,
+                    'downloads': m.get('downloads', 0),
                 })
         time.sleep(0.3)
-    log(f"搜索完成: {len(seen)} 个唯一模型, {len(all_models)} 个下载量>=50")
+    log(f"搜索完成: {len(seen)} 个唯一模型, {len(all_models)} 个下载量{DOWNLOAD_MIN}-{DOWNLOAD_MAX}")
 
-    # 2. 格式筛选（只保留HuggingFace格式，排除GGUF）
+    # 2. 格式筛选（排除 GPTQ/AWQ，保留 GGUF 和 HuggingFace）
     log("\n--- 阶段2: 格式筛选 ---")
     hf_models = []
-    gguf_skipped = 0
+    format_skipped = 0
+    SKIP_FORMATS = ['GPTQ', 'AWQ']
     for m in all_models:
-        mid = m['model_id'].upper()
-        if 'GGUF' in mid:
-            gguf_skipped += 1
-            log(f"  ✗ {m['model_id']}: GGUF格式，跳过")
-        else:
+        mid_upper = m['model_id'].upper()
+        skip = False
+        for fmt in SKIP_FORMATS:
+            if fmt in mid_upper:
+                format_skipped += 1
+                log(f"  x {m['model_id']}: {fmt}格式，跳过")
+                skip = True
+                break
+        if not skip:
             hf_models.append(m)
-    log(f"格式筛选: {len(hf_models)} 通过 (HuggingFace), {gguf_skipped} 跳过 (GGUF)")
+    log(f"格式筛选: {len(hf_models)} 通过, {format_skipped} 跳过 (GPTQ/AWQ)")
 
-    # 3. 架构筛选
-    log("\n--- 阶段3: 架构筛选 ---")
-    arch_passed = []
-    arch_rejected = 0
+    # 3. 架构筛选（参考检查，不做严格过滤，让平台决定兼容性）
+    log("\n--- 阶段3: 架构检查 ---")
     for m in hf_models:
         ok, reason = check_architecture(m['model_id'])
-        if ok:
-            arch_passed.append(m)
-        else:
-            arch_rejected += 1
-            log(f"  ✗ {m['model_id']}: {reason}")
-        time.sleep(0.15)
-    log(f"架构筛选: {len(arch_passed)} 通过, {arch_rejected} 拒绝")
+        if not ok:
+            log(f"  ! {m['model_id']}: {reason} (仍保留)")
+        time.sleep(0.1)
+    log(f"架构检查: {len(hf_models)} 个模型进入下一阶段")
 
     # 4. 筛选并提交（只针对目标GPU）
     log(f"\n--- 阶段4: 筛选并提交 [{TARGET_GPU}] ---")
@@ -357,7 +387,7 @@ def run_pipeline(submit_limit: int = 30):
 
     # 筛选
     to_submit = []
-    for m in arch_passed:
+    for m in hf_models:
         model_id = m['model_id']
 
         # 检查全平台验证状态
@@ -369,6 +399,10 @@ def run_pipeline(submit_limit: int = 30):
         if check_my_submitted(model_id):
             continue
 
+        # 检查是否已知失败（避免重复提交）
+        if is_model_failed(model_id):
+            continue
+
         to_submit.append(m)
         if len(to_submit) >= min(submit_limit, available):
             break
@@ -385,10 +419,13 @@ def run_pipeline(submit_limit: int = 30):
             log(f"  ✅ {m['model_id']}")
             db_conn.execute(
                 'INSERT OR REPLACE INTO submitted VALUES (?,?,?,?)',
-                (m['model_id'], gpu, str(task_id), datetime.now().isoformat())
+                (m['model_id'], TARGET_GPU, str(task_id), datetime.now().isoformat())
             )
         else:
             log(f"  ❌ {m['model_id']}: {msg}")
+            # 永久失败类型记录到 failed 表
+            if any(kw in str(msg) for kw in ['保护期', '白名单', '唯一性']):
+                record_failed(m['model_id'], msg)
         time.sleep(0.5)
 
     log(f"  提交完成: {submitted}/{len(to_submit)}")
@@ -449,7 +486,7 @@ class AgentHandler(BaseHTTPRequestHandler):
             if content_len > 0:
                 body = json.loads(self.rfile.read(content_len))
 
-            limit = body.get('limit', 30)
+            limit = body.get('limit', 2)
 
             self._json({'status': 'started', 'gpu': TARGET_GPU, 'limit': limit})
 
@@ -530,7 +567,7 @@ class AgentHandler(BaseHTTPRequestHandler):
                     'modelAddress': 'https://www.modelscope.cn/models/Qwen/Qwen3-8B',
                     'taskType': 'text-generation',
                     'targetGpu': TARGET_GPU,
-                    'framework': 'vllm',
+                    'framework': 'llama.cpp',
                     'strategyId': STRATEGY_ID,
                     'configParams': build_config_params(),
                 },
@@ -648,7 +685,7 @@ def main():
                 json={
                     'modelAddress': 'https://www.modelscope.cn/models/Qwen/Qwen3-8B',
                     'taskType': 'text-generation', 'targetGpu': TARGET_GPU,
-                    'framework': 'vllm', 'strategyId': STRATEGY_ID,
+                    'framework': 'llama.cpp', 'strategyId': STRATEGY_ID,
                     'configParams': build_config_params(),
                 }, timeout=10
             )
@@ -666,7 +703,7 @@ def main():
         try:
             state['running'] = True
             state['last_run'] = datetime.now().isoformat()
-            count = run_pipeline(submit_limit=30)
+            count = run_pipeline(submit_limit=2)
             state['last_result'] = {'submitted': count, 'success': True}
         except Exception as e:
             log(f"流程异常: {traceback.format_exc()}")