.

app.py

@@ -8,7 +8,9 @@ from listener import RabbitListenerBase
 from rabbit_repo import RabbitRepo
 from oa_server import OpenAIServers
 #from vllm_backend import VLLMChatBackend, StubImagesBackend
-from transformers_backend import TransformersChatBackend, StubImagesBackend
+#from transformers_backend import TransformersChatBackend, StubImagesBackend
+from hf_backend import HFChatBackend, StubImagesBackend
+
 
 logging.basicConfig(
     level=logging.INFO,
@@ -35,7 +37,7 @@ base = RabbitBase(exchange_type_resolver=resolver)
 
 servers = OpenAIServers(
     publisher,
-    chat_backend=TransformersChatBackend(),
+    chat_backend=HFChatBackend(),
     images_backend=StubImagesBackend()
 )
 

hf_backend.py

@@ -0,0 +1,122 @@
+# hf_backend.py
+import time, logging, os, contextlib
+from typing import Any, Dict, AsyncIterable, List
+
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from backends_base import ChatBackend, ImagesBackend
+from config import settings
+
+try:
+    import spaces
+except ImportError:
+    spaces = None
+
+logger = logging.getLogger(__name__)
+
+# --- Load model/tokenizer on CPU at import time (ZeroGPU safe) ---
+MODEL_ID = settings.LlmHFModelID or "Qwen/Qwen2.5-1.5B-Instruct"
+logger.info(f"Loading {MODEL_ID} on CPU at startup (ZeroGPU safe)...")
+
+tokenizer = None
+model = None
+load_error = None
+try:
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True, use_fast=False)
+    model = AutoModelForCausalLM.from_pretrained(
+        MODEL_ID,
+        torch_dtype=torch.float32,  # CPU-safe default
+        trust_remote_code=True,
+    )
+    model.eval()
+except Exception as e:
+    load_error = f"Failed to load model/tokenizer: {e}"
+    logger.exception(load_error)
+
+
+# --- Device helpers ---
+def pick_device() -> str:
+    forced = os.getenv("FORCE_DEVICE", "").lower().strip()
+    if forced in {"cpu", "cuda", "mps"}:
+        return forced
+    if torch.cuda.is_available():
+        return "cuda"
+    if getattr(torch.backends, "mps", None) and torch.backends.mps.is_available():
+        return "mps"
+    return "cpu"
+
+def pick_dtype(device: str) -> torch.dtype:
+    if device == "cuda":
+        major, _ = torch.cuda.get_device_capability()
+        return torch.bfloat16 if major >= 8 else torch.float16
+    if device == "mps":
+        return torch.float16
+    return torch.float32
+
+
+# --- Backend class ---
+class HFChatBackend(ChatBackend):
+    async def stream(self, request: Dict[str, Any]) -> AsyncIterable[Dict[str, Any]]:
+        if load_error:
+            raise RuntimeError(load_error)
+
+        messages = request.get("messages", [])
+        prompt = messages[-1]["content"] if messages else "(empty)"
+        temperature = float(request.get("temperature", settings.LlmTemp or 0.7))
+        max_tokens = int(request.get("max_tokens", settings.LlmOpenAICtxSize or 512))
+
+        rid = f"chatcmpl-hf-{int(time.time())}"
+        now = int(time.time())
+
+        if spaces:
+            @spaces.GPU(duration=120)  # allow longer run
+            def run_once(prompt: str) -> str:
+                device = pick_device()
+                dtype = pick_dtype(device)
+
+                # Move model to GPU if needed
+                model.to(device=device, dtype=dtype).eval()
+
+                inputs = tokenizer(prompt, return_tensors="pt").to(device)
+                with torch.inference_mode(), torch.autocast(device_type=device, dtype=dtype):
+                    outputs = model.generate(
+                        **inputs,
+                        max_new_tokens=max_tokens,
+                        temperature=temperature,
+                        do_sample=True,
+                    )
+                return tokenizer.decode(outputs[0], skip_special_tokens=True)
+        else:
+            def run_once(prompt: str) -> str:
+                inputs = tokenizer(prompt, return_tensors="pt")
+                with torch.inference_mode():
+                    outputs = model.generate(
+                        **inputs,
+                        max_new_tokens=max_tokens,
+                        temperature=temperature,
+                        do_sample=True,
+                    )
+                return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+        try:
+            text = run_once(prompt)
+            yield {
+                "id": rid,
+                "object": "chat.completion.chunk",
+                "created": now,
+                "model": MODEL_ID,
+                "choices": [
+                    {"index": 0, "delta": {"content": text}, "finish_reason": "stop"}
+                ],
+            }
+        except Exception:
+            logger.exception("HF inference failed")
+            raise
+
+
+class StubImagesBackend(ImagesBackend):
+    async def generate_b64(self, request: Dict[str, Any]) -> str:
+        logger.warning("Image generation not supported in HF backend.")
+        return (
+            "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR4nGP4BwQACfsD/etCJH0AAAAASUVORK5CYII="
+        )