Upload serve_ministral.py with huggingface_hub

serve_ministral.py

@@ -11,6 +11,7 @@ from pydantic import BaseModel
 from typing import List, Optional
 import uvicorn
 import time
+import traceback
 
 app = FastAPI()
 
@@ -59,47 +60,72 @@ async def load_model():
 async def chat_completions(request: ChatRequest):
     global model, processor
 
-    # Format messages using chat template
-    messages = [{"role": m.role, "content": m.content} for m in request.messages]
-
-    chat_text = processor.apply_chat_template(
-        messages,
-        tokenize=False,
-        add_generation_prompt=True
-    )
-
-    inputs = processor(text=chat_text, return_tensors="pt").to(model.device)
-
-    with torch.no_grad():
-        outputs = model.generate(
-            **inputs,
-            max_new_tokens=request.max_tokens,
-            temperature=request.temperature if request.temperature > 0 else None,
-            top_p=request.top_p,
-            do_sample=request.temperature > 0,
-            pad_token_id=processor.tokenizer.eos_token_id,
+    try:
+        # Format messages
+        messages = [{"role": m.role, "content": m.content} for m in request.messages]
+        print(f"Processing {len(messages)} messages...")
+
+        # Try to apply chat template
+        try:
+            chat_text = processor.apply_chat_template(
+                messages,
+                tokenize=False,
+                add_generation_prompt=True
+            )
+        except Exception as e:
+            print(f"Chat template error: {e}")
+            # Fallback: manual formatting
+            chat_text = ""
+            for m in messages:
+                if m["role"] == "user":
+                    chat_text += f"[INST] {m['content']} [/INST]"
+                elif m["role"] == "assistant":
+                    chat_text += f" {m['content']}</s>"
+                elif m["role"] == "system":
+                    chat_text = f"<<SYS>>\n{m['content']}\n<</SYS>>\n\n" + chat_text
+
+        print(f"Input length: {len(chat_text)} chars")
+
+        # Tokenize
+        inputs = processor.tokenizer(chat_text, return_tensors="pt").to(model.device)
+        input_len = inputs["input_ids"].shape[1]
+        print(f"Input tokens: {input_len}")
+
+        # Generate
+        with torch.no_grad():
+            outputs = model.generate(
+                **inputs,
+                max_new_tokens=request.max_tokens,
+                temperature=request.temperature if request.temperature and request.temperature > 0 else 1.0,
+                top_p=request.top_p if request.top_p else 0.9,
+                do_sample=request.temperature is not None and request.temperature > 0,
+                pad_token_id=processor.tokenizer.eos_token_id,
+            )
+
+        # Decode only the new tokens
+        new_tokens = outputs[0][input_len:]
+        response_text = processor.tokenizer.decode(new_tokens, skip_special_tokens=True)
+        print(f"Generated {len(new_tokens)} tokens")
+
+        return ChatResponse(
+            id=f"chatcmpl-{int(time.time())}",
+            created=int(time.time()),
+            model=request.model,
+            choices=[{
+                "index": 0,
+                "message": {"role": "assistant", "content": response_text},
+                "finish_reason": "stop"
+            }],
+            usage={
+                "prompt_tokens": input_len,
+                "completion_tokens": len(new_tokens),
+                "total_tokens": input_len + len(new_tokens)
+            }
         )
-
-    # Decode only the new tokens
-    input_len = inputs["input_ids"].shape[1]
-    new_tokens = outputs[0][input_len:]
-    response_text = processor.decode(new_tokens, skip_special_tokens=True)
-
-    return ChatResponse(
-        id=f"chatcmpl-{int(time.time())}",
-        created=int(time.time()),
-        model=request.model,
-        choices=[{
-            "index": 0,
-            "message": {"role": "assistant", "content": response_text},
-            "finish_reason": "stop"
-        }],
-        usage={
-            "prompt_tokens": input_len,
-            "completion_tokens": len(new_tokens),
-            "total_tokens": input_len + len(new_tokens)
-        }
-    )
+    except Exception as e:
+        print(f"Error: {e}")
+        traceback.print_exc()
+        raise
 
 @app.get("/v1/models")
 async def list_models():