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Dockerfile

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+FROM python:3.10-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 7860
+
+CMD ["python", "app.py"]

README.md

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----
-title: Mpt Space
-emoji: 🌖
-colorFrom: green
-colorTo: green
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Qa Python Api
+emoji: 💻
+colorFrom: red
+colorTo: green
+sdk: docker
+pinned: false
+license: apache-2.0
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+app = FastAPI()
+
+# ---- Load your HF model repo ----
+MODEL_REPO = "hello-ram/mpt-model"
+
+print("Loading tokenizer...")
+tokenizer = AutoTokenizer.from_pretrained(MODEL_REPO)
+
+print("Loading model...")
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_REPO,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+# ---------- ROUTES -------------
+
+@app.get("/")
+async def root():
+    return {
+        "message": "🚀 FastAPI MPT Model Running on Hugging Face Spaces",
+        "endpoints": ["/", "/status", "/generate"]
+    }
+
+@app.get("/status")
+async def status():
+    return {
+        "status": "ok",
+        "model": MODEL_REPO,
+        "device": str(model.device),
+        "torch_dtype": str(model.dtype)
+    }
+
+
+class InputText(BaseModel):
+    text: str
+
+
+@app.post("/generate")
+async def generate_text(data: InputText):
+    inputs = tokenizer(data.text, return_tensors="pt").to(model.device)
+
+    output = model.generate(
+        **inputs,
+        max_new_tokens=200,
+        temperature=0.7
+    )
+
+    generated = tokenizer.decode(output[0], skip_special_tokens=True)
+    return {"response": generated}

requirements.txt

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+fastapi
+uvicorn
+transformers
+accelerate
+torch