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serve_ministral.py

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+#!/usr/bin/env python3
+"""
+Simple OpenAI-compatible API server for Ministral 14B using transformers
+Usage: python serve_ministral.py
+"""
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from fastapi import FastAPI
+from pydantic import BaseModel
+from typing import List, Optional
+import uvicorn
+import time
+
+app = FastAPI()
+
+# Global model and tokenizer
+model = None
+tokenizer = None
+
+class Message(BaseModel):
+    role: str
+    content: str
+
+class ChatRequest(BaseModel):
+    model: str = "ministral-14b"
+    messages: List[Message]
+    max_tokens: Optional[int] = 2048
+    temperature: Optional[float] = 0.7
+    top_p: Optional[float] = 0.9
+
+class ChatResponse(BaseModel):
+    id: str
+    object: str = "chat.completion"
+    created: int
+    model: str
+    choices: List[dict]
+    usage: dict
+
+@app.on_event("startup")
+async def load_model():
+    global model, tokenizer
+    print("Loading Ministral 14B...")
+
+    model_id = "RoleModel/ministral-14b-merged-official"
+
+    tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+
+    # Load just the text model weights
+    model = AutoModelForCausalLM.from_pretrained(
+        model_id,
+        torch_dtype=torch.bfloat16,
+        device_map="auto",
+        trust_remote_code=True,
+    )
+    model.eval()
+    print("Model loaded successfully!")
+
+@app.post("/v1/chat/completions")
+async def chat_completions(request: ChatRequest):
+    global model, tokenizer
+
+    # Format messages using chat template
+    chat_text = tokenizer.apply_chat_template(
+        [{"role": m.role, "content": m.content} for m in request.messages],
+        tokenize=False,
+        add_generation_prompt=True
+    )
+
+    inputs = tokenizer(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,
+            top_p=request.top_p,
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id,
+        )
+
+    # Decode only the new tokens
+    new_tokens = outputs[0][inputs["input_ids"].shape[1]:]
+    response_text = tokenizer.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": inputs["input_ids"].shape[1],
+            "completion_tokens": len(new_tokens),
+            "total_tokens": inputs["input_ids"].shape[1] + len(new_tokens)
+        }
+    )
+
+@app.get("/v1/models")
+async def list_models():
+    return {
+        "object": "list",
+        "data": [{"id": "ministral-14b", "object": "model", "owned_by": "rolemodel"}]
+    }
+
+@app.get("/health")
+async def health():
+    return {"status": "ok"}
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8000)