{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "mFjDH3xDjZxr" }, "source": [ "# Chapter 7: Deploying Models for Inference at Scale\n", "\n", "You have trained your VLM and now you want people to actually use it. This is where inference deployment comes in, and it is where many of us discover that \"the model works in my setup\" is far from \"the model serves 1000 users reliably\".\n", "\n", "Deployment is two challenges bundled together: first, there is the optimization challenge: how do you make inference fast and cheap enough to be practical? Second, there is the packaging and deploying your model where it needs to run: whether that's a cloud cluster, a browser, an edge device, or specific hardware.\n", "\n", "In this chapter, we tackle both problems: inference optimization, serving frameworks like [vLLM](https://github.com/vllm-project/vllm), and how to make models portable across runtimes and deploy them." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "EyELOLNXjZxv" }, "outputs": [], "source": [ "!pip install -Uq transformers==5.3.0\n", "!pip install -Uq accelerate bitsandbytes torchao num2words # flash-attn" ] }, { "cell_type": "markdown", "metadata": { "id": "Buin09Z-jZxw" }, "source": [ "## 7.1 Inference Optimization for VLMs\n", "\n", "You have trained your VLM and confirmed its accuracy through your tests. Now, you're ready to roll it out. However, when you start serving real users, a new question emerges: *why so little throughput?*\n", "\n", "This section provides you with the mental models and hands-on techniques to answer that question and address it. We will break down the VLM inference pipeline, learn how to measure what's important, and explore a complete set of optimizations: from quantizing weights to improving attention efficiency." ] }, { "cell_type": "markdown", "metadata": { "id": "tuRbiLnujZxy" }, "source": [ "### 7.1.1 Understanding VLM Inference\n", "\n", "When you deploy a text-only LLM, most of the inference time is spent on autoregressive decoding, generating one token at a time. Vision-Language Models introduce a new factor: while the vision encoder processes each image once, the visual tokens it generates remain in the context for the whole generation.\n", "\n", "There are two phases of transformer inference:\n", "\n", "**Prefill phase**: The model processes all input tokens in parallel, building up the key-value (KV) cache that will be reused during generation. This phase is *compute-bound* — you're doing a lot of matrix multiplications, and the GPU's tensor cores are busy.\n", "\n", "**Decode phase**: The model generates tokens one at a time. Each new token attends to all previous tokens (via the cached keys and values), but you're only computing the representation for a single new token. This phase is *memory-bound* — the bottleneck is loading the model weights and KV cache from GPU memory for each token.\n", "\n", "The prefill phase happens once. The decode phase happens N times, where N is the number of tokens you generate. Which phase dominates depends on your use case:\n", "\n", "- Prefill dominates when the visual context is large: high-resolution images, multiple images, or video (8K-32K+ tokens).\n", "- Decode dominates when the visual context is smaller but the output is lengthy.\n", "\n", "But how can we benchmark the generation speed for both? There are two core metrics to pay attention to:\n", "\n", "**Time to First Token (TTFT):** This measures the delay between receiving a request and producing the first output token. For interactive applications, aim for a TTFT of less than 500 ms.\n", "\n", "**Memory-Normalized Throughput (Tokens per Second per GB):** This is useful for comparing configurations. If you use less memory per request, you can batch more requests, which improves total throughput.\n", "\n", "There's one more thing to know: when profiling generations, the first run is always slow. CUDA kernel launches often have cold-start cost, so before we actually start the timers, we need to warm the GPU up by doing several generations.\n", "\n", "Let's do a VLM profiling ourselves and take a look at why warmup matters:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "V2ZmPeOUjZxz" }, "outputs": [], "source": [ "import time\n", "import torch\n", "import requests\n", "from io import BytesIO\n", "from PIL import Image\n", "\n", "def benchmark_vlm(model, processor, prompt=\"Describe.\", max_new_tokens=100, warmup=3):\n", "\n", " messages = [{\"role\": \"user\", \"content\": [{\"type\": \"image\", \"image\": \"http://images.cocodataset.org/val2017/000000039769.jpg\"}, {\"type\": \"text\", \"text\": prompt}]}]\n", " inputs = processor.apply_chat_template(messages, add_generation_prompt=True,\n", " tokenize=True, return_dict=True, return_tensors=\"pt\").to(\"cuda\")\n", "\n", " # Warmup runs (discard - includes JIT compilation, memory allocation)\n", " for _ in range(warmup):\n", " with torch.no_grad():\n", " model.generate(**inputs, max_new_tokens=1)\n", "\n", " torch.cuda.reset_peak_memory_stats()\n", "\n", " # Measure TTFT (time to first token)\n", " torch.cuda.synchronize()\n", " t0 = time.perf_counter()\n", " with torch.no_grad():\n", " model.generate(**inputs, max_new_tokens=1)\n", " torch.cuda.synchronize()\n", " ttft_ms = (time.perf_counter() - t0) * 1000\n", "\n", " # Measure throughput (tokens per second)\n", " torch.cuda.synchronize()\n", " t0 = time.perf_counter()\n", " with torch.no_grad():\n", " outputs = model.generate(**inputs, max_new_tokens=max_new_tokens)\n", " torch.cuda.synchronize()\n", " total_time = time.perf_counter() - t0\n", " tokens_generated = outputs.shape[1] - inputs[\"input_ids\"].shape[1]\n", " throughput = tokens_generated / total_time\n", "\n", " memory_gb = torch.cuda.max_memory_allocated() / 1e9\n", "\n", " return {\n", " \"ttft_ms\": ttft_ms,\n", " \"throughput\": throughput,\n", " \"memory_gb\": memory_gb,\n", " \"efficiency\": throughput / memory_gb\n", " }" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 85, "referenced_widgets": [ "a7814b5aaf6149c4bfded5a667629592", "22ce5141a10c45379c9ea6856b643186", "163f550a23c946569e848a7a332aee09", "80af6403ee6a43b4a06f4c4d4dbce17b", "e419bb167f214150a0101e4fad24b109", "302d2efe39314431b753569afc3163a8", "a1839cce8af94134a6e0d510229b1252", "27a9de5d45e246f4a9100279fe0b2619", "6a967c25316d417395d5eb0e7e75abf9", "b7d10a67441847dca222c90ff86ed88a", "33846d9e82034858b1f8e083ab42d562" ] }, "id": "ecCcbH2IjZx1", "outputId": "80381f7c-2539-45eb-ff24-9ad94d8ad996" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "`torch_dtype` is deprecated! Use `dtype` instead!\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/657 [00:00=0.15`.\n", "\n", "You can checkout [torchao transformers documentation](https://huggingface.co/docs/transformers/quantization/torchao) to try different quantization schemes, as the API is subject to changes." ] }, { "cell_type": "markdown", "metadata": { "id": "rkWH9KCIjZx7" }, "source": [ "## 7.2 Exporting Models to Different Runtimes\n", "\n", "Under the hood, every neural network is a directed graph: inputs flow in, pass through operations (matrix multiplies, attention computations, activations), and outputs flow out. When you train a model in PyTorch, you are working with a dynamic computational graph.\n", "\n", "When it comes to performance, this is far from optimal: if we know how the graph looks like, we can think of strategies to fuse adjacent operations, prune unused branches and allocate memory efficiently.\n", "\n", "### ONNX: the universal format\n", "\n", "ONNX stands for Open Neural Network Exchange and is an open format designed to be the common language between ML frameworks. You can export from PyTorch or JAX, and then run on ONNX Runtime, TensorRT, or any other compatible backend.\n", "\n", "See below in some pseudo-code how we export a model to ONNX:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "54yn2KzojZx7" }, "outputs": [], "source": [ "# This is pseudo-code — requires a model with a simple forward signature\n", "import torch\n", "\n", "# Your trained model\n", "# model = load_my_vlm()\n", "# model.eval()\n", "\n", "# Create dummy inputs matching your model's expected shapes\n", "# dummy_image = torch.randn(1, 3, 384, 384)\n", "# dummy_input_ids = torch.randint(0, 32000, (1, 128))\n", "\n", "# Export to ONNX\n", "# torch.onnx.export(\n", "# model,\n", "# (dummy_image, dummy_input_ids),\n", "# \"model.onnx\",\n", "# input_names=[\"pixel_values\", \"input_ids\"],\n", "# output_names=[\"logits\"],\n", "# dynamic_axes={\n", "# \"pixel_values\": {0: \"batch\"},\n", "# \"input_ids\": {0: \"batch\", 1: \"sequence\"},\n", "# \"logits\": {0: \"batch\", 1: \"sequence\"}\n", "# },\n", "# opset_version=17\n", "# )" ] }, { "cell_type": "markdown", "metadata": { "id": "ATqu7Hi7jZx8" }, "source": [ "The `dynamic_axes` parameter is crucial: it tells ONNX which dimensions can vary at runtime. Without it, your model would be locked to exactly the batch size and sequence length you used during export.\n", "\n", "What can go wrong?\n", "\n", "- **Unsupported operations:** If your model uses an operation that doesn't have an ONNX equivalent, export fails. Custom CUDA kernels, some dynamic control flow, and certain newer PyTorch features can all cause problems.\n", "- **Dynamic control flow:** If your model's computation depends on input values (not just shapes), tracing may not capture all paths.\n", "\n", "Specifically on VLMs, preprocessing for resizing, normalization or patch extraction often stays in Python rather than getting exported, which means you'll need to handle it separately in your deployment pipeline.\n", "\n", "A common practice in VLMs is to export the vision encoder, projection layer, and language model separately because each have different computational patterns." ] }, { "cell_type": "markdown", "metadata": { "id": "1qQ_GIJNjZx8" }, "source": [ "### Browser Deployment with Transformers.js\n", "\n", "Running models in the browser has gone from impossible to surprisingly practical. Transformers.js wraps ONNX Runtime Web and provides a familiar API for running Hugging Face models directly in JavaScript.\n", "\n", "```javascript\n", "import { pipeline } from '@huggingface/transformers';\n", "\n", "// Load a VLM for image-to-text\n", "const pipe = await pipeline(\n", " 'image-to-text',\n", " 'onnx-community/SmolVLM-256M-Instruct',\n", " { device: 'webgpu' }\n", ");\n", "\n", "const result = await pipe(imageUrl, {\n", " max_new_tokens: 100\n", "});\n", "```\n", "\n", "Browsers have extra constraints: memory is limited (typically 2-4GB for WebAssembly), download sizes matter (users will wait for a 256M model, not a 7B model). Quantization becomes essential — 4-bit models are the norm for browser deployment." ] }, { "cell_type": "markdown", "metadata": { "id": "dmGRAKlMjZx8" }, "source": [ "## 7.3 Packaging & Deploying in Real Environments\n", "\n", "We have seen how to optimize your model and convert it to a faster runtime. Now you need to actually serve it, meaning expose an API that handles real traffic, stays up under load, and all this while not getting bankrupted paying GPUs.\n", "\n", "### 7.3.1 It Runs\n", "\n", "Most teams start here. You containerize a small web server that loads a VLM, accepts image URLs and prompts, calls `model.generate()`, and returns text. Something like this:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ogHSTzN_jZx9" }, "outputs": [], "source": [ "# server.py — A minimal VLM serving endpoint\n", "# This is meant to be saved as a file and run with: uvicorn server:app --host 0.0.0.0 --port 8000\n", "\n", "# from fastapi import FastAPI\n", "# from transformers import AutoProcessor, LlavaForConditionalGeneration\n", "# from pydantic import BaseModel\n", "# from PIL import Image\n", "# import requests\n", "# import torch\n", "\n", "# app = FastAPI()\n", "# device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "# model = LlavaForConditionalGeneration.from_pretrained(\n", "# \"llava-hf/llava-1.5-7b-hf\"\n", "# ).to(device).eval()\n", "# processor = AutoProcessor.from_pretrained(\"llava-hf/llava-1.5-7b-hf\")\n", "\n", "# class GenerateRequest(BaseModel):\n", "# image_url: str\n", "# prompt: str\n", "\n", "# @app.post(\"/generate\")\n", "# async def generate(req: GenerateRequest):\n", "# image = Image.open(requests.get(req.image_url, stream=True).raw)\n", "# inputs = processor(text=req.prompt, images=image, return_tensors=\"pt\").to(device)\n", "# with torch.inference_mode():\n", "# output = model.generate(**inputs, max_new_tokens=200)\n", "# generated_tokens = output[0, inputs[\"input_ids\"].shape[1]:]\n", "# return {\"text\": processor.decode(generated_tokens, skip_special_tokens=True)}" ] }, { "cell_type": "markdown", "metadata": { "id": "UK2987kKjZx9" }, "source": [ "This works. You can build it, run it, send requests, get responses. But it falls apart quickly under real conditions. Why?\n", "\n", "- Each request gets its own `generate()` call creating tiny spiky workloads: GPUs are optimized for large, steady batches of work.\n", "- Concurrency causes OOM: two simultaneous requests with long outputs can blow up the KV cache and crash your server.\n", "- No streaming: Users wait for the entire response before seeing anything.\n", "\n", "Text generation has two distinct phases with different computational profiles:\n", "\n", "- **Prefill** processes the entire input prompt in one forward pass — many matrix multiplications, but done once per request.\n", "- **Decode** generates tokens one at a time, each requiring a forward pass that reads from the KV cache — memory-bandwidth-heavy.\n", "\n", "You could implement a scheduler that handles all this but this is exactly what serving engines like vLLM already do, with years of optimization behind them." ] }, { "cell_type": "markdown", "metadata": { "id": "7sKr_rCtjZx9" }, "source": [ "### 7.3.2 Efficient Deployment with vLLM\n", "\n", "vLLM is the answer to \"how do I group requests and run inference efficiently?\" It provides an OpenAI-compatible server that handles batching, scheduling, and memory management automatically, including full support for VLMs with image inputs.\n", "\n", "Three concepts matter for understanding why vLLM is fast:\n", "\n", "- **Chunked prefill with smart scheduling:** Instead of processing an entire long prompt in one shot, vLLM breaks prefills into chunks and interleaves them with decode steps.\n", "- **PagedAttention for memory management:** The KV cache is managed in blocks (like virtual memory pages), eliminating fragmentation and enabling much larger effective batch sizes.\n", "- **Continuous batching:** Requests enter and exit the batch dynamically. When one sequence finishes, its slot immediately becomes available for a new request.\n", "\n", "#### Running vLLM with a VLM\n", "\n", "vLLM provides an official Docker image that makes deployment straightforward:\n", "\n", "```bash\n", "export HF_TOKEN=...\n", "\n", "docker run --runtime nvidia --gpus all \\\n", " -v ~/.cache/huggingface:/root/.cache/huggingface \\\n", " --env \"HF_TOKEN=$HF_TOKEN\" \\\n", " -p 8000:8000 \\\n", " --ipc=host \\\n", " vllm/vllm-openai:latest \\\n", " --model microsoft/Phi-3.5-vision-instruct \\\n", " --trust-remote-code \\\n", " --max-model-len 4096 \\\n", " --limit-mm-per-prompt '{\"image\":2}'\n", "```\n", "\n", "#### Client Code\n", "\n", "The server exposes an OpenAI-compatible API, so you can use the standard OpenAI client:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Aodu9eixjZx-" }, "outputs": [], "source": [ "# This requires a running vLLM server (see Docker command above)\n", "# pip install openai\n", "\n", "from openai import OpenAI\n", "client = OpenAI(api_key=\"EMPTY\", base_url=\"http://localhost:8000/v1\")\n", "response = client.chat.completions.create(\n", " model=\"microsoft/Phi-3.5-vision-instruct\",\n", " messages=[{\n", " \"role\": \"user\",\n", " \"content\": [\n", " {\"type\": \"text\", \"text\": \"What's in this image?\"},\n", " {\"type\": \"image_url\", \"image_url\": {\"url\": \"https://example.com/photo.jpg\"}},\n", " ],\n", " }],\n", ")\n", "print(response.choices[0].message.content)" ] }, { "cell_type": "markdown", "metadata": { "id": "XCon859ujZx-" }, "source": [ "You don't need to manually insert `` placeholder tokens in your text — vLLM handles chat template application and image token placement automatically. Just interleave text and image content blocks as shown, and the server figures out the rest.\n", "\n", "#### Common Friction Points\n", "\n", "- **Chat template problems:** The server needs to know how to format messages for your specific model. If the model repo doesn't include a proper `chat_template` in its tokenizer config, you'll get errors. Fix with `--chat-template path/to/template.jinja`.\n", "- **Remote code requirements:** Newer models often need `--trust-remote-code`, and sometimes require installing a development version of Transformers.\n", "- Different VLMs expect different image formats, normalization, or tiling strategies. vLLM handles most common cases, but novel architectures may need updates.\n", "\n", "### 7.3.3 Production Optimizations\n", "\n", "#### Prefix Caching\n", "\n", "If many requests share a common prefix (a system prompt, few-shot examples, or standard instructions) you're recomputing the same KV cache entries over and over. vLLM's automatic prefix caching detects shared prefixes and reuses their cached attention states across requests.\n", "\n", "For VLMs specifically, you can cache processed image embeddings across requests by providing a stable UUID for each image:\n", "\n", "```python\n", "{\n", " \"type\": \"image_url\",\n", " \"image_url\": {\"url\": \"https://example.com/photo.jpg\"},\n", " \"uuid\": \"photo-abc123\"\n", "}\n", "```\n", "\n", "If the same UUID appears in subsequent requests, vLLM reuses the cached vision encoder output instead of re-processing the image." ] }, { "cell_type": "markdown", "metadata": { "id": "JTiYJtYEjZx_" }, "source": [ "## 7.4 On-device / Edge Deployment\n", "\n", "Two years ago, running a vision-language model on a phone was essentially impossible. Today, sub-500M models run at interactive speeds on flagship devices.\n", "\n", "### 7.4.1 The Edge Landscape\n", "\n", "By \"edge,\" we mean running inference on user-adjacent hardware: laptops, phones, or embedded devices, anywhere memory and power limits dominate design decisions:\n", "\n", "- **Laptops and desktops:** An M2 MacBook with 16GB unified memory runs a 2B parameter VLM comfortably.\n", "- **Mobile devices:** Phones have NPUs and GPUs, but thermal limits, battery life, and memory (4-8GB shared with the OS) restrict you to sub-500M models.\n", "- **Embedded:** Jetson Orin, Raspberry Pi, custom hardware. Limited compute, typically used for narrow tasks.\n", "\n", "In all of them memory is the primary constraint, not compute." ] }, { "cell_type": "markdown", "metadata": { "id": "Ed_BoB4RjZyA" }, "source": [ "### 7.4.2 MLX on Apple Silicon\n", "\n", "MLX is a NumPy-like array library for Apple Silicon backend. It has access to Silicon's unified memory (CPU & GPU use the same memory pool). The API is almost the same as numpy and torch — here's how a neural network can be defined:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "CS7xGhJnjZyA" }, "outputs": [], "source": [ "# This requires Apple Silicon and the mlx library: pip install mlx\n", "\n", "import mlx.core as mx\n", "import mlx.nn as nn\n", "\n", "class MLP(nn.Module):\n", " def __init__(self, in_dims: int, out_dims: int):\n", " super().__init__()\n", " self.layers = [\n", " nn.Linear(in_dims, 128),\n", " nn.Linear(128, 128),\n", " nn.Linear(128, out_dims),\n", " ]\n", " def __call__(self, x):\n", " for i, l in enumerate(self.layers):\n", " x = mx.maximum(x, 0) if i > 0 else x\n", " x = l(x)\n", " return x" ] }, { "cell_type": "markdown", "metadata": { "id": "vIkJme6wjZyC" }, "source": [ "MLX-VLM is a wrapper around MLX for vision language and any-to-any (Omni) models. It is very easy to get started with the library.\n", "\n", "```bash\n", "pip install -U mlx-vlm\n", "```\n", "\n", "To serve a model with MLX-VLM, the architecture has to be implemented within the library. You can check out supported architectures [here](https://github.com/Blaizzy/mlx-vlm/tree/main/mlx_vlm/models). You can convert a Hugging Face transformers model as follows. You can quantize the model, save it in different precision, and push the converted model to Hugging Face Hub.\n", "\n", "```bash\n", "mlx_vlm.convert --hf-path Qwen/Qwen3-VL-8B-Instruct --mlx-path your_model_path --upload-repo your-username/Qwen3-VL-8B --quantize --dtype bfloat16\n", "```\n", "\n", "Hugging Face Hub hosts hundreds of vision language models converted to MLX-VLM format, as well as their quantized versions. When you visit the Hugging Face Hub models page, filter for `image-text-to-text` from tasks and `MLX` from libraries.\n", "\n", "Once you found the model to serve, you can use CLI commands MLX-VLM offers:\n", "\n", "```bash\n", "mlx_vlm.generate --model lmstudio-community/Qwen3-VL-2B-Thinking-MLX-8bit --max-tokens 100 --temperature 0.0 --prompt \"Describe this image.\" --image https://huggingface.co/datasets/vlmbook/images/resolve/main/venice.png\n", "```\n", "\n", "You can also infer with Gemma-3n, that takes in image, audio and text prompt, all at once:\n", "\n", "```bash\n", "mlx_vlm.generate --model mlx-community/gemma-3n-E2B-it-4bit --max-tokens 100 --prompt \"Describe what you see and hear\" --image https://huggingface.co/datasets/vlmbook/images/resolve/main/venice.png --audio /path/to/audio.wav\n", "```\n", "\n", "MLX-VLM also has a command for video understanding inference:\n", "\n", "```bash\n", "mlx_vlm.video_generate --model mlx-community/Qwen2-VL-2B-Instruct-4bit --max-tokens 100 --prompt \"Describe this video\" --video https://huggingface.co/datasets/vlmbook/images/resolve/main/IMG_8137.mp4 --fps 1.0\n", "```\n", "\n", "You can also use it through Python. Load the model as follows:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "_MU2cQhijZyC" }, "outputs": [], "source": [ "# Requires: pip install -U mlx-vlm (Apple Silicon only)\n", "\n", "from mlx_vlm import load, generate\n", "from mlx_vlm.utils import load_config\n", "\n", "model_path = \"lmstudio-community/Qwen3-VL-2B-Thinking-MLX-8bit\"\n", "model, processor = load(model_path)\n", "config = load_config(model_path)" ] }, { "cell_type": "markdown", "metadata": { "id": "eqZuRW5QjZyD" }, "source": [ "Preprocess the inputs and infer:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "00-3Y8cIjZyD" }, "outputs": [], "source": [ "import mlx.core as mx\n", "from mlx_vlm.prompt_utils import apply_chat_template\n", "\n", "image = [\"https://huggingface.co/datasets/vlmbook/images/resolve/main/venice.png\"]\n", "\n", "prompt = \"Describe this image.\"\n", "\n", "formatted_prompt = apply_chat_template(\n", " processor, config, prompt, num_images=len(image)\n", ")\n", "\n", "output = generate(model, processor, formatted_prompt, image, verbose=False)\n", "print(output)" ] }, { "cell_type": "markdown", "metadata": { "id": "jiZTLcFtjZyD" }, "source": [ "### 7.4.3 Llama.cpp\n", "\n", "Llama.cpp is a library for optimized LLM and VLM inference with C++. It supports various backends, from Metal (Apple Silicon) to CUDA. To use it, you need to export the model to the GGUF format and then you can serve it using different APIs.\n", "\n", "#### Installation and build\n", "\n", "```bash\n", "python3 -m pip install --user cmake\n", "export PATH=\"$HOME/.local/bin:$PATH\"\n", "cmake --version\n", "\n", "git clone https://github.com/ggml-org/llama.cpp\n", "cd llama.cpp\n", "cmake -B build\n", "cmake --build build --config Release\n", "```\n", "\n", "#### Converting to GGUF\n", "\n", "Because multimodal models come in different patterns with vision encoder, projection and LLM connected separately, you need to convert the vision encoder and projector first, and then LLM separately.\n", "\n", "```bash\n", "cd gemma-3-4b-it\n", "python ../llama.cpp/convert_hf_to_gguf.py --outfile model.gguf --outtype f16 --mmproj .\n", "```\n", "\n", "#### Quantization scheme\n", "\n", "Let's break down the naming convention, for example `Q4_K_M`:\n", "\n", "- **Q4**: 4-bit quantization, 4x smaller than fp16\n", "- **_K**: K-Quant — groups weights into blocks, stores quantization scale per block\n", "- **_M**: Quality preset — S (smaller & faster, more loss), M (balanced), L (larger & higher quality)\n", "\n", "```bash\n", "./build/bin/llama-quantize \\\n", " ../gemma-3-4b-it/model-f16.gguf \\\n", " ../gemma-3-4b-it/model-Q4_K_M.gguf \\\n", " Q4_K_M\n", "```\n", "\n", "#### Serving models\n", "\n", "llama.cpp supports multimodal input via libmtmd. Two ways of serving:\n", "\n", "```bash\n", "# CLI command\n", "llama-mtmd-cli -hf ggml-org/gemma-3-4b-it-GGUF -m gemma-3-4b-it-GGUF --image path/to/img.jpg -p \"Describe this image in detail.\"\n", "\n", "# OpenAI-compatible server\n", "llama-server -hf ggml-org/gemma-3-4b-it-GGUF\n", "```\n", "\n", "Using models on Hugging Face is the easiest way to serve GGUF models because otherwise you need to indicate two separate models, multimodal projector and LLM." ] }, { "cell_type": "markdown", "metadata": { "id": "z1IS9hmgjZyE" }, "source": [ "### 7.4.4 PEFT Adapters for Edge Customization\n", "\n", "LoRA allows you to ship a base VLM with your app (250MB at 4-bit) and then, like a screwdriver kit with interchangeable bits, ship small adapters for each use case:\n", "\n", "```\n", "Base model: 250 MB (ships with app)\n", "Adapter A: 8 MB (detailed descriptions)\n", "Adapter B: 8 MB (medical terminology)\n", "```\n", "\n", "Users download only what they need. MLX-VLM supports this directly:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "VcjPMKMQjZyE" }, "outputs": [], "source": [ "# Requires: pip install -U mlx-vlm (Apple Silicon only)\n", "\n", "from mlx_vlm import load, generate\n", "\n", "model, processor = load(\n", " \"mlx-community/SmolVLM2-500M-Video-Instruct-mlx\",\n", " adapter_path=\"path/to/medical-adapter\"\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "fagApLv8jZyG" }, "source": [ "### 7.4.5 Hybrid Patterns\n", "\n", "What about sharing the load between edge and cloud? There are several approaches:\n", "\n", "- **Local-first with cloud fallback:** Run a compact VLM locally for simple queries. When confidence is low or the query is complex, escalate to a larger cloud model. The local model handles 70-80% of requests instantly.\n", "- **Vision on-device, language in cloud:** Run just the vision encoder locally (fast, keeps images private), send embedding vectors to a cloud LLM. You're uploading kilobytes instead of megabytes.\n", "- **Local cache:** Cache responses for common queries. Hit the cloud only for novel inputs." ] }, { "cell_type": "markdown", "metadata": { "id": "_u1gKdMpjZyH" }, "source": [ "## 7.5 Summary\n", "\n", "This chapter equipped you with the complete toolkit for taking VLMs from training to production deployment:\n", "\n", "- VLM inference differs fundamentally from text-only models because visual tokens dominate the context and persist across conversation turns. The KV cache trades memory for compute during autoregressive generation, and modern models use Grouped Query Attention to shrink cache requirements.\n", "- FlashAttention exploits the bandwidth gap between fast on-chip SRAM and slower main GPU memory through tiled computation, while quantization attacks the same memory-bound bottleneck by shrinking weights.\n", "- For model portability, ONNX serves as the universal interchange format, TensorRT provides maximum NVIDIA performance, and Transformers.js enables browser deployment.\n", "- Naive web server deployments fail under concurrent load — vLLM solves this through PagedAttention, continuous batching, and chunked prefill scheduling.\n", "- Edge deployment reveals that aggressive quantization, efficient architectures like FastVLM, and LoRA adapters enable VLMs on phones and laptops." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python", "version": "3.10.0" }, "colab": 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