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| <link rel="modulepreload" href="/docs/text-embeddings-inference/pr_742/en/_app/immutable/chunks/MermaidChart.svelte_svelte_type_style_lang.bf9a6737.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{"title":"Supported models and hardware","local":"supported-models-and-hardware","sections":[{"title":"Supported embeddings models","local":"supported-embeddings-models","sections":[],"depth":2},{"title":"Supported re-rankers and sequence classification models","local":"supported-re-rankers-and-sequence-classification-models","sections":[],"depth":2},{"title":"Supported hardware","local":"supported-hardware","sections":[],"depth":2}],"depth":1}"><!-- HEAD_svelte-u9bgzb_END --> <p></p> <div class="items-center shrink-0 min-w-[100px] max-sm:min-w-[50px] justify-end ml-auto flex" style="float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"><div class="inline-flex rounded-md max-sm:rounded-sm"><button class="inline-flex items-center 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disabled:pointer-events-none text-sm text-gray-500 hover:text-gray-700 dark:hover:text-white rounded-r-md max-sm:rounded-r-sm border border-l transition border-gray-200 bg-white hover:shadow-inner dark:border-gray-850 dark:bg-gray-950 dark:text-gray-200 dark:hover:bg-gray-800" aria-haspopup="menu" aria-expanded="false" aria-label="Open copy menu"><svg class="transition-transform text-gray-400 overflow-visible sm:size-3.5 size-3 rotate-0" width="1em" height="1em" viewBox="0 0 12 7" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M1 1L6 6L11 1" stroke="currentColor"></path></svg></button></div> </div> <h1 class="relative group"><a id="supported-models-and-hardware" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#supported-models-and-hardware"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Supported models and hardware</span></h1> <p data-svelte-h="svelte-1k6m0ht">We are continually expanding our support for other model types and plan to include them in future updates.</p> <h2 class="relative group"><a id="supported-embeddings-models" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#supported-embeddings-models"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Supported embeddings models</span></h2> <p data-svelte-h="svelte-aoav01">Text Embeddings Inference currently supports Nomic, BERT, CamemBERT, XLM-RoBERTa models with absolute positions, JinaBERT | |
| model with Alibi positions and Mistral, Alibaba GTE, Qwen2 models with Rope positions, MPNet, ModernBERT, Qwen3, and Gemma3.</p> <p data-svelte-h="svelte-vskdni">Below are some examples of the currently supported models:</p> <table data-svelte-h="svelte-1sce89e"><thead><tr><th>MTEB Rank</th> <th>Model Size</th> <th>Model Type</th> <th>Model ID</th></tr></thead> <tbody><tr><td>2</td> <td>7.57B (Very Expensive)</td> <td>Qwen3</td> <td><a href="https://hf.co/Qwen/Qwen3-Embedding-8B" rel="nofollow">Qwen/Qwen3-Embedding-8B</a></td></tr> <tr><td>3</td> <td>4.02B (Very Expensive)</td> <td>Qwen3</td> <td><a href="https://hf.co/Qwen/Qwen3-Embedding-4B" rel="nofollow">Qwen/Qwen3-Embedding-4B</a></td></tr> <tr><td>4</td> <td>509M</td> <td>Qwen3</td> <td><a href="https://hf.co/Qwen/Qwen3-Embedding-0.6B" rel="nofollow">Qwen/Qwen3-Embedding-0.6B</a></td></tr> <tr><td>6</td> <td>7.61B (Very Expensive)</td> <td>Qwen2</td> <td><a href="https://hf.co/Alibaba-NLP/gte-Qwen2-7B-instruct" rel="nofollow">Alibaba-NLP/gte-Qwen2-7B-instruct</a></td></tr> <tr><td>7</td> <td>560M</td> <td>XLM-RoBERTa</td> <td><a href="https://hf.co/intfloat/multilingual-e5-large-instruct" rel="nofollow">intfloat/multilingual-e5-large-instruct</a></td></tr> <tr><td>8</td> <td>308M</td> <td>Gemma3</td> <td><a href="https://hf.co/google/embeddinggemma-300m" rel="nofollow">google/embeddinggemma-300m</a> (gated)</td></tr> <tr><td>15</td> <td>1.78B (Expensive)</td> <td>Qwen2</td> <td><a href="https://hf.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct" rel="nofollow">Alibaba-NLP/gte-Qwen2-1.5B-instruct</a></td></tr> <tr><td>18</td> <td>7.11B (Very Expensive)</td> <td>Mistral</td> <td><a href="https://hf.co/Salesforce/SFR-Embedding-2_R" rel="nofollow">Salesforce/SFR-Embedding-2_R</a></td></tr> <tr><td>35</td> <td>568M</td> <td>XLM-RoBERTa</td> <td><a href="https://hf.co/Snowflake/snowflake-arctic-embed-l-v2.0" rel="nofollow">Snowflake/snowflake-arctic-embed-l-v2.0</a></td></tr> <tr><td>41</td> <td>305M</td> <td>Alibaba GTE</td> <td><a href="https://hf.co/Snowflake/snowflake-arctic-embed-m-v2.0" rel="nofollow">Snowflake/snowflake-arctic-embed-m-v2.0</a></td></tr> <tr><td>52</td> <td>335M</td> <td>BERT</td> <td><a href="https://hf.co/WhereIsAI/UAE-Large-V1" rel="nofollow">WhereIsAI/UAE-Large-V1</a></td></tr> <tr><td>58</td> <td>137M</td> <td>NomicBERT</td> <td><a href="https://hf.co/nomic-ai/nomic-embed-text-v1" rel="nofollow">nomic-ai/nomic-embed-text-v1</a></td></tr> <tr><td>79</td> <td>137M</td> <td>NomicBERT</td> <td><a href="https://hf.co/nomic-ai/nomic-embed-text-v1.5" rel="nofollow">nomic-ai/nomic-embed-text-v1.5</a></td></tr> <tr><td>103</td> <td>109M</td> <td>MPNet</td> <td><a href="https://hf.co/sentence-transformers/all-mpnet-base-v2" rel="nofollow">sentence-transformers/all-mpnet-base-v2</a></td></tr> <tr><td>N/A</td> <td>475M-A305M</td> <td>NomicBERT</td> <td><a href="https://hf.co/nomic-ai/nomic-embed-text-v2-moe" rel="nofollow">nomic-ai/nomic-embed-text-v2-moe</a></td></tr> <tr><td>N/A</td> <td>434M</td> <td>Alibaba GTE</td> <td><a href="https://hf.co/Alibaba-NLP/gte-large-en-v1.5" rel="nofollow">Alibaba-NLP/gte-large-en-v1.5</a></td></tr> <tr><td>N/A</td> <td>396M</td> <td>ModernBERT</td> <td><a href="https://hf.co/answerdotai/ModernBERT-large" rel="nofollow">answerdotai/ModernBERT-large</a></td></tr> <tr><td>N/A</td> <td>340M</td> <td>Qwen3</td> <td><a href="https://hf.co/voyageai/voyage-4-nano" rel="nofollow">voyageai/voyage-4-nano</a></td></tr> <tr><td>N/A</td> <td>137M</td> <td>JinaBERT</td> <td><a href="https://hf.co/jinaai/jina-embeddings-v2-base-en" rel="nofollow">jinaai/jina-embeddings-v2-base-en</a></td></tr> <tr><td>N/A</td> <td>137M</td> <td>JinaBERT</td> <td><a href="https://hf.co/jinaai/jina-embeddings-v2-base-code" rel="nofollow">jinaai/jina-embeddings-v2-base-code</a></td></tr></tbody></table> <p data-svelte-h="svelte-1fz6qfa">To explore the list of best performing text embeddings models, visit the | |
| <a href="https://huggingface.co/spaces/mteb/leaderboard" rel="nofollow">Massive Text Embedding Benchmark (MTEB) Leaderboard</a>.</p> <h2 class="relative group"><a id="supported-re-rankers-and-sequence-classification-models" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#supported-re-rankers-and-sequence-classification-models"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Supported re-rankers and sequence classification models</span></h2> <p data-svelte-h="svelte-17enjvk">Text Embeddings Inference currently supports CamemBERT, and XLM-RoBERTa Sequence Classification models with absolute positions.</p> <p data-svelte-h="svelte-vskdni">Below are some examples of the currently supported models:</p> <table data-svelte-h="svelte-1lqpwl"><thead><tr><th>Task</th> <th>Model Type</th> <th>Model ID</th></tr></thead> <tbody><tr><td>Re-Ranking</td> <td>XLM-RoBERTa</td> <td><a href="https://huggingface.co/BAAI/bge-reranker-large" rel="nofollow">BAAI/bge-reranker-large</a></td></tr> <tr><td>Re-Ranking</td> <td>XLM-RoBERTa</td> <td><a href="https://huggingface.co/BAAI/bge-reranker-base" rel="nofollow">BAAI/bge-reranker-base</a></td></tr> <tr><td>Re-Ranking</td> <td>GTE</td> <td><a href="https://huggingface.co/Alibaba-NLP/gte-multilingual-reranker-base" rel="nofollow">Alibaba-NLP/gte-multilingual-reranker-base</a></td></tr> <tr><td>Re-Ranking</td> <td>ModernBert</td> <td><a href="https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base" rel="nofollow">Alibaba-NLP/gte-reranker-modernbert-base</a></td></tr> <tr><td>Sentiment Analysis</td> <td>RoBERTa</td> <td><a href="https://huggingface.co/SamLowe/roberta-base-go_emotions" rel="nofollow">SamLowe/roberta-base-go_emotions</a></td></tr></tbody></table> <h2 class="relative group"><a id="supported-hardware" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#supported-hardware"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Supported hardware</span></h2> <p data-svelte-h="svelte-v5c6kt">Text Embeddings Inference supports can be used on CPU, Turing (T4, RTX 2000 series, …), Ampere 80 (A100, A30), | |
| Ampere 86 (A10, A40, …), Ada Lovelace (RTX 4000 series, …), Hopper (H100), and Blackwell (B200, …) architectures.</p> <p data-svelte-h="svelte-1l2nroq">The library does <strong>not</strong> support CUDA compute capabilities < 7.5, which means V100, Titan V, GTX 1000 series, etc. are not supported.</p> <p data-svelte-h="svelte-j76380">To leverage your GPUs, make sure to install the | |
| <a href="https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html" rel="nofollow">NVIDIA Container Toolkit</a>, and use | |
| NVIDIA drivers with CUDA version 12.2 or higher.</p> <p data-svelte-h="svelte-16dfo8x">Find the appropriate Docker image for your hardware in the following table:</p> <table data-svelte-h="svelte-1r6m5vr"><thead><tr><th>Architecture</th> <th>Image</th></tr></thead> <tbody><tr><td>CPU</td> <td>ghcr.io/huggingface/text-embeddings-inference:cpu-1.9</td></tr> <tr><td>Volta</td> <td>NOT SUPPORTED</td></tr> <tr><td>Turing (T4, RTX 2000 series, …)</td> <td>ghcr.io/huggingface/text-embeddings-inference:turing-1.9 (experimental)</td></tr> <tr><td>Ampere 8.0 (A100, A30)</td> <td>ghcr.io/huggingface/text-embeddings-inference:1.9</td></tr> <tr><td>Ampere 8.6 (A10, A40, …)</td> <td>ghcr.io/huggingface/text-embeddings-inference:86-1.9</td></tr> <tr><td>Ada Lovelace (RTX 4000 series, …)</td> <td>ghcr.io/huggingface/text-embeddings-inference:89-1.9</td></tr> <tr><td>Hopper (H100)</td> <td>ghcr.io/huggingface/text-embeddings-inference:hopper-1.9</td></tr> <tr><td>Blackwell 10.0 (B200, GB200, …)</td> <td>ghcr.io/huggingface/text-embeddings-inference:100-1.9 (experimental)</td></tr> <tr><td>Blackwell 12.0 (GeForce RTX 50X0, …)</td> <td>ghcr.io/huggingface/text-embeddings-inference:120-1.9 (experimental)</td></tr></tbody></table> <p data-svelte-h="svelte-173bv05"><strong>Warning</strong>: Flash Attention is turned off by default for the Turing image as it suffers from precision issues. | |
| You can turn Flash Attention v1 ON by using the <code>USE_FLASH_ATTENTION=True</code> environment variable.</p> <a class="!text-gray-400 !no-underline text-sm flex items-center not-prose mt-4" href="https://github.com/huggingface/text-embeddings-inference/blob/main/docs/source/en/supported_models.md" target="_blank"><svg class="mr-1" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M31,16l-7,7l-1.41-1.41L28.17,16l-5.58-5.59L24,9l7,7z"></path><path d="M1,16l7-7l1.41,1.41L3.83,16l5.58,5.59L8,23l-7-7z"></path><path d="M12.419,25.484L17.639,6.552l1.932,0.518L14.351,26.002z"></path></svg> <span data-svelte-h="svelte-zjs2n5"><span class="underline">Update</span> on GitHub</span></a> <p></p> | |
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