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+# ── Base ──────────────────────────────────────────────────────────────────────
+# SmolVLM is small enough to run on CPU, but a GPU Space is faster.
+# HuggingFace Spaces requires the app to listen on port 7860.
+FROM python:3.11-slim
+
+# ── System deps ───────────────────────────────────────────────────────────────
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        git \
+        libgl1 \
+        libglib2.0-0 \
+    && rm -rf /var/lib/apt/lists/*
+
+# ── Non-root user (HF Spaces runs as UID 1000) ────────────────────────────────
+RUN useradd -m -u 1000 appuser
+WORKDIR /app
+
+# ── Install Python deps ───────────────────────────────────────────────────────
+COPY requirements.txt .
+RUN pip install --no-cache-dir --upgrade pip \
+    && pip install --no-cache-dir -r requirements.txt
+
+# ── Copy app source ───────────────────────────────────────────────────────────
+COPY app.py .
+
+# ── HuggingFace cache (model weights downloaded at first startup) ─────────────
+ENV HF_HOME=/app/.cache/huggingface
+RUN mkdir -p /app/.cache/huggingface && chown -R appuser:appuser /app
+
+USER appuser
+
+# ── Port ──────────────────────────────────────────────────────────────────────
+EXPOSE 7860
+
+# ── Start ─────────────────────────────────────────────────────────────────────
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+"""
+FastAPI app for HuggingFaceTB/SmolVLM-Instruct
+Supports: text-only prompts, single image, and multi-image inputs.
+Port: 7860 (HuggingFace Spaces default)
+"""
+
+import io
+import base64
+import logging
+from contextlib import asynccontextmanager
+from typing import Optional
+
+import torch
+from PIL import Image
+from fastapi import FastAPI, File, Form, UploadFile, HTTPException
+from pydantic import BaseModel
+from transformers import AutoProcessor, AutoModelForVision2Seq
+
+# ── Logging ───────────────────────────────────────────────────────────────────
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# ── Config ────────────────────────────────────────────────────────────────────
+MODEL_ID = "HuggingFaceTB/SmolVLM-Instruct"
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+DTYPE = torch.bfloat16 if DEVICE == "cuda" else torch.float32
+
+# ── Globals ───────────────────────────────────────────────────────────────────
+model = None
+processor = None
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    global model, processor
+    logger.info(f"Loading {MODEL_ID} on {DEVICE} …")
+    processor = AutoProcessor.from_pretrained(MODEL_ID)
+    model = AutoModelForVision2Seq.from_pretrained(
+        MODEL_ID,
+        torch_dtype=DTYPE,
+        _attn_implementation="eager",  # swap to "flash_attention_2" on supported GPUs
+    ).to(DEVICE)
+    model.eval()
+    logger.info("SmolVLM ready ✓")
+    yield
+    del model, processor
+    if DEVICE == "cuda":
+        torch.cuda.empty_cache()
+
+
+# ── App ───────────────────────────────────────────────────────────────────────
+app = FastAPI(
+    title="SmolVLM API",
+    description="Multimodal inference with HuggingFaceTB/SmolVLM-Instruct",
+    version="1.0.0",
+    lifespan=lifespan,
+)
+
+
+# ── Helpers ───────────────────────────────────────────────────────────────────
+
+def run_inference(
+    prompt: str,
+    images: Optional[list[Image.Image]] = None,
+    max_new_tokens: int = 512,
+    temperature: float = 0.0,
+) -> str:
+    images = images or []
+
+    # Build chat message — SmolVLM uses the standard messages format
+    content = []
+    for img in images:
+        content.append({"type": "image"})
+    content.append({"type": "text", "text": prompt})
+
+    messages = [{"role": "user", "content": content}]
+
+    # Apply chat template
+    text_input = processor.apply_chat_template(messages, add_generation_prompt=True)
+
+    inputs = processor(
+        text=text_input,
+        images=images if images else None,
+        return_tensors="pt",
+    ).to(DEVICE)
+
+    generate_kwargs = dict(
+        **inputs,
+        max_new_tokens=max_new_tokens,
+        do_sample=temperature > 0,
+    )
+    if temperature > 0:
+        generate_kwargs["temperature"] = temperature
+
+    with torch.no_grad():
+        output_ids = model.generate(**generate_kwargs)
+
+    # Decode only the new tokens
+    input_len = inputs["input_ids"].shape[1]
+    generated = output_ids[0][input_len:]
+    return processor.decode(generated, skip_special_tokens=True).strip()
+
+
+# ── Routes ────────────────────────────────────────────────────────────────────
+
+@app.get("/", tags=["Health"])
+def root():
+    return {"status": "ok", "model": MODEL_ID, "device": DEVICE}
+
+
+@app.get("/health", tags=["Health"])
+def health():
+    return {"model_loaded": model is not None}
+
+
+# ── 1. Text-only ──────────────────────────────────────────────────────────────
+
+class TextRequest(BaseModel):
+    prompt: str
+    max_new_tokens: int = 512
+    temperature: float = 0.0
+
+
+@app.post("/generate/text", tags=["Inference"])
+def generate_text(req: TextRequest):
+    """Plain text prompt — no image required."""
+    if model is None:
+        raise HTTPException(503, "Model not loaded yet")
+    try:
+        return {"prompt": req.prompt, "response": run_inference(
+            req.prompt,
+            max_new_tokens=req.max_new_tokens,
+            temperature=req.temperature,
+        )}
+    except Exception as e:
+        logger.exception("Inference error")
+        raise HTTPException(500, str(e))
+
+
+# ── 2. Image upload (multipart/form-data) ─────────────────────────────────────
+
+@app.post("/generate/vision", tags=["Inference"])
+async def generate_vision(
+    prompt: str = Form("Describe the image(s) in detail."),
+    max_new_tokens: int = Form(512),
+    temperature: float = Form(0.0),
+    images: list[UploadFile] = File(default=[]),
+):
+    """Upload one or more images with an optional text prompt."""
+    if model is None:
+        raise HTTPException(503, "Model not loaded yet")
+
+    pil_images: list[Image.Image] = []
+    for upload in images:
+        raw = await upload.read()
+        try:
+            pil_images.append(Image.open(io.BytesIO(raw)).convert("RGB"))
+        except Exception:
+            raise HTTPException(400, f"Could not decode image: {upload.filename}")
+
+    try:
+        response = run_inference(
+            prompt,
+            images=pil_images or None,
+            max_new_tokens=max_new_tokens,
+            temperature=temperature,
+        )
+        return {"prompt": prompt, "num_images": len(pil_images), "response": response}
+    except Exception as e:
+        logger.exception("Inference error")
+        raise HTTPException(500, str(e))
+
+
+# ── 3. Base64 images via JSON ─────────────────────────────────────────────────
+
+class VisionB64Request(BaseModel):
+    prompt: str = "Describe the image(s) in detail."
+    images_b64: list[str] = []
+    max_new_tokens: int = 512
+    temperature: float = 0.0
+
+
+@app.post("/generate/vision/base64", tags=["Inference"])
+def generate_vision_b64(req: VisionB64Request):
+    """Send base64-encoded images inside a JSON body."""
+    if model is None:
+        raise HTTPException(503, "Model not loaded yet")
+
+    pil_images: list[Image.Image] = []
+    for idx, b64str in enumerate(req.images_b64):
+        if "," in b64str:
+            b64str = b64str.split(",", 1)[1]
+        try:
+            raw = base64.b64decode(b64str)
+            pil_images.append(Image.open(io.BytesIO(raw)).convert("RGB"))
+        except Exception:
+            raise HTTPException(400, f"Could not decode base64 image at index {idx}")
+
+    try:
+        response = run_inference(
+            req.prompt,
+            images=pil_images or None,
+            max_new_tokens=req.max_new_tokens,
+            temperature=req.temperature,
+        )
+        return {"prompt": req.prompt, "num_images": len(pil_images), "response": response}
+    except Exception as e:
+        logger.exception("Inference error")
+        raise HTTPException(500, str(e))
+
+
+# ── Entry point ───────────────────────────────────────────────────────────────
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run("app:app", host="0.0.0.0", port=7860, reload=False)

requirements.txt

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+fastapi==0.115.0
+uvicorn[standard]==0.30.6
+python-multipart==0.0.9
+pillow==10.4.0
+torch==2.4.1
+torchvision==0.19.1
+transformers==4.45.0
+accelerate==0.34.2