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 license: mit
 short_description: Top Open-Source Small Language Models for Generative AI Appl
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-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

 license: mit
 short_description: Top Open-Source Small Language Models for Generative AI Appl
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-<!doctype html>
-<html>
-	<head>
-		<meta charset="utf-8" />
-		<meta name="viewport" content="width=device-width" />
-		<title>My static Space</title>
-		<link rel="stylesheet" href="style.css" />
-	</head>
-	<body>
-		<div class="card">
-			<h1>Welcome to your static Space!</h1>
-			<p>You can modify this app directly by editing <i>index.html</i> in the Files and versions tab.</p>
-			<p>
-				Also don't forget to check the
-				<a href="https://huggingface.co/docs/hub/spaces" target="_blank">Spaces documentation</a>.
-			</p>
-		</div>
-	</body>
 </html>

+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1">
+    <title>Top Open-Source Small Language Models</title>
+    <link rel="stylesheet" href="styles.css"/>
+</head>
+<body>
+<h1>Top Open-Source Small Language Models for Generative AI Applications</h1>
+<p>
+    Small Language Models (SLMs) are language models that contain, at most, a few billion parameters—significantly fewer
+    than Large Language Models (LLMs), which can have tens, hundreds of billions, or even trillions, of parameters. SLMs
+    are well-suited for resource-constrained environments, as well as on-device and real-time generative AI
+    applications. Many of them can run locally on a laptop using tools like LM Studio or Ollama . These models are
+    typically derived from larger models using techniques such as quantization and distillation. In the following, some
+    well developed SLMs are introduced.
+</p>
+<p>
+    Note: All the models mentioned here are open source. However, for details regarding experimental use, commercial
+    use, redistribution, and other terms, please refer to the license documentation.
+</p>
+<h2>Phi 4 Collection by Microsoft</h2>
+<p>
+    This Collection features a range of small language models, including reasoning models, ONNX- and GGUF-compatible
+    formats, and multimodal models. The base model in the collection has 14 billion parameters, while the smallest
+    models have 3.84 billion. Strategic use of synthetic data during training has led to improved performance compared
+    to its mother model (primarily GPT-4). Currently, the collection includes three versions of reasoning-focused SLMs,
+    making it one of the best solutions for reasoning tasks.
+</p>
+<p>
+    👉 Licence: <a href="https://choosealicense.com/licenses/mit/" target="_blank">MIT</a><br>
+    👉 <a href="https://huggingface.co/collections/microsoft/phi-4-677e9380e514feb5577a40e4" target="_blank">Collection
+    on Hugging Face</a><br>
+    👉 <a href="https://arxiv.org/abs/2412.08905" target="_blank">Technical Report</a>
+</p>
+<h2>Gemma 3 Collection by Google</h2>
+<p>
+    This collection features multiple versions, including Image-to-Text, Text-to-Text, and Image-and-Text-to-Text
+    models, available in both quantized and GGUF formats. The models vary in size, with 1, 4.3, 12.2, and 27.4 billion
+    parameters. Two specialized variants have been developed for specific applications: TxGemma, optimized for
+    therapeutic development, and ShieldGemma, designed for moderating text and image content.
+</p>
+<p>
+    👉 Licence: <a href="https://ai.google.dev/gemma/terms" target="_blank">Gemma</a><br>
+    👉 <a href="https://huggingface.co/collections/google/gemma-3-release-67c6c6f89c4f76621268bb6d" target="_blank">Collection
+    on Hugging Face</a><br>
+    👉 <a href="https://storage.googleapis.com/deepmind-media/gemma/Gemma3Report.pdf" target="_blank">Technical
+    Report</a><br>
+    👉 <a href="https://huggingface.co/collections/google/shieldgemma-67d130ef8da6af884072a789" target="_blank">ShieldGemma
+    on Hugging Face</a><br>
+    👉 <a href="https://huggingface.co/collections/google/txgemma-release-67dd92e931c857d15e4d1e87" target="_blank">TxGemma
+    on Hugging Face</a>
+</p>
+<h2>Mistral Models</h2>
+<p>
+    Mistral AI is a France-based AI startup and one of the pioneers in releasing open-source language models. Its
+    current product lineup includes three compact models: Mistral Small 3.1, Pixtral 12B, and Mistral NEMO. All of them
+    are released under <a href="https://www.apache.org/licenses/LICENSE-2.0" target="_blank">Apache 2.0 license</a>.
+</p>
+<p>
+    <b>Mistral 3.1</b> is a multimodal and multilingual SLM having 24 billion parameters and 128K context window.
+    Currently there are two versions: Base and Instruct.<br>
+    👉 <a href="https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503" target="_blank">Base Version on Hugging
+    Face</a><br>
+    👉 <a href="https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503" target="_blank">Instruct Version on
+    Hugging Face</a><br>
+    👉 <a href="https://mistral.ai/news/mistral-small-3-1" target="_blank">Technical Report</a><br>
+</p>
+<p>
+    <b>Pixtral 12B</b> is a natively multimodal model trained on interleaved image and text data, delivering strong
+    performance on multimodal tasks and instruction following while maintaining state-of-the-art results on text-only
+    benchmarks. It features a newly developed 400M parameter vision encoder and a 12B parameter multimodal decoder based
+    on Mistral NEMO. The model supports variable image sizes, aspect ratios, and multiple images within a long context
+    window of up to 128k tokens.<br>
+    👉 <a href="https://huggingface.co/mistralai/Pixtral-12B-Base-2409" target="_blank">Pixtral-12B-Base-2409 on Hugging
+    Face</a><br>
+    👉 <a href="https://huggingface.co/mistralai/Pixtral-12B-2409" target="_blank">Pixtral-12B-2409 on Hugging
+    Face</a><br>
+    👉 <a href="https://mistral.ai/news/pixtral-12b" target="_blank">Technical Report</a><br>
+</p>
+<p>
+    <b>Mistral NeMo</b> is a 12B model developed in collaboration with NVIDIA, featuring a large 128k-token context
+    window and state-of-the-art reasoning, knowledge, and coding accuracy for its size.<br>
+    👉 <a href="https://huggingface.co/mistralai/Mistral-Nemo-Instruct-FP8-2407" target="_blank">Model on Hugging
+    Face</a><br>
+    👉 <a href="https://mistral.ai/news/mistral-nemo" target="_blank">Technical Report</a>
+</p>
+<h2>Llama Models by Meta</h2>
+<p>
+    Meta is one of the leading contributors to open-source AI. In recent years, it has released several versions of its
+    Llama models. The latest series is Llama 4, although all models in this collection are currently quite large.
+    Smaller models may be introduced in the future or in upcoming sub-versions of Llama 4, but for now, that hasn’t
+    happened. The most recent collection that includes smaller models is Llama 3.2. It features models with 1.24 billion
+    and 3.21 billion parameters with 128k context windows. Additionally, there is a 10.6 billion-parameter multimodal
+    version designed for Image-and-Text-to-Text tasks.
+    This collection includes small variants of Llama Guard — fine-tuned language models designed for prompt and response
+    classification. They can detect unsafe prompts and responses, making them useful for implementing safety measures in
+    LLM-based applications.
+</p>
+<p>
+    👉 License: <a href="https://www.llama.com/llama3_2/license/" target="_blank">LLAMA 3.2 COMMUNITY LICENSE
+    AGREEMENT</a><br>
+    👉 <a href="https://huggingface.co/collections/meta-llama/llama-32-66f448ffc8c32f949b04c8cf" target="_blank">Collection
+    on Hugging Face</a><br>
+    👉 <a href="https://ai.meta.com/blog/llama-3-2-connect-2024-vision-edge-mobile-devices/" target="_blank">Technical
+    Paper</a>
+</p>
+<h2>Qwen 3 Collection by Alibaba</h2>
+<p>
+    The Chinese tech giant Alibaba is another major player in open-source AI. It releases its language models under the
+    Qwen name. The latest version is Qwen 3, which includes both small and large models. The smaller models range in
+    size, with parameter counts of 14.8 billion, 8.19 billion, 4.02 billion, 2.03 billion, and even 752 million. This
+    collection also includes quantized and GGUF formats.
+</p>
+<p>
+    👉 Licence: <a href="https://www.apache.org/licenses/LICENSE-2.0" target="_blank">Apache 2.0</a><br>
+    👉 <a href="https://huggingface.co/collections/Qwen/qwen3-67dd247413f0e2e4f653967f" target="_blank">Collection on
+    Hugging Face</a><br>
+    👉 <a href="https://github.com/QwenLM/Qwen3/blob/main/Qwen3_Technical_Report.pdf" target="_blank">Technical
+    Report</a>
+</p>
+<hr style="border: none; height: 1px; background-color: #ccc;">
+<p>This list is not limited to these five. You can explore more open-source models at:</p>
+<ul>
+    <li><a href="https://huggingface.co/databricks" target="_blank">Databricks</a></li>
+    <li><a href="https://huggingface.co/Cohere" target="_blank">Cohere</a></li>
+    <li><a href="https://huggingface.co/deepseek-ai" target="_blank">Deepseek</a></li>
+    <li><a href="https://huggingface.co/collections/HuggingFaceTB/smollm-6695016cad7167254ce15966" target="_blank">SmolLM</a>
+    </li>
+    <li><a href="https://huggingface.co/stabilityai" target="_blank">Stability AI</a></li>
+    <li><a href="https://huggingface.co/ibm-granite" target="_blank">IBM Granite</a></li>
+</ul>
+<hr style="border: none; height: 1px; background-color: #ccc;">
+<p>* Cover photo generated with <a href="https://ideogram.ai/" target="_blank">Ideogram</a></p>
+<p>* All models are available on Hugging Face.</p>
+</body>
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