Buckets:
| <meta charset="utf-8" /><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}"> | |
| <link href="/docs/text-embeddings-inference/main/en/_app/immutable/assets/0.e3b0c442.css" rel="modulepreload"> | |
| <link rel="modulepreload" href="/docs/text-embeddings-inference/main/en/_app/immutable/entry/start.5929ce5e.js"> | |
| <link rel="modulepreload" href="/docs/text-embeddings-inference/main/en/_app/immutable/chunks/scheduler.b108d059.js"> | |
| <link rel="modulepreload" href="/docs/text-embeddings-inference/main/en/_app/immutable/chunks/singletons.8ba50d2e.js"> | |
| <link rel="modulepreload" href="/docs/text-embeddings-inference/main/en/_app/immutable/chunks/paths.0433c982.js"> | |
| <link rel="modulepreload" href="/docs/text-embeddings-inference/main/en/_app/immutable/entry/app.99d3b526.js"> | |
| <link rel="modulepreload" href="/docs/text-embeddings-inference/main/en/_app/immutable/chunks/index.008de539.js"> | |
| <link rel="modulepreload" href="/docs/text-embeddings-inference/main/en/_app/immutable/nodes/0.edd78360.js"> | |
| <link rel="modulepreload" href="/docs/text-embeddings-inference/main/en/_app/immutable/nodes/12.efbbb03f.js"> | |
| <link rel="modulepreload" href="/docs/text-embeddings-inference/main/en/_app/immutable/chunks/EditOnGithub.d1c48e3d.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> <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-wffkmp">Text Embeddings Inference currently supports BERT, CamemBERT, XLM-RoBERTa models with absolute positions and JinaBERT | |
| model with Alibi positions.</p> <p data-svelte-h="svelte-vskdni">Below are some examples of the currently supported models:</p> <table data-svelte-h="svelte-aq53hm"><thead><tr><th>MTEB Rank</th> <th>Model Type</th> <th>Model ID</th></tr></thead> <tbody><tr><td>6</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>10</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>N/A</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>N/A</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>N/A</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>JinaBERT</td> <td><a href="https://hf.co/jniaai/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-ze9dp3"><thead><tr><th>Task</th> <th>Model Type</th> <th>Model ID</th> <th>Revision</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> <td><code>refs/pr/4</code></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> <td><code>refs/pr/5</code></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> <td></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-58o6ad">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, …), and Hopper (H100) architectures.</p> <p data-svelte-h="svelte-1q49upw">The library does <strong>not</strong> support CUDA compute capabilities < 7.5, which means V100, Titan V, GTX 1000 series, etc. are not supported. | |
| 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-f5zzzv"><thead><tr><th>Architecture</th> <th>Image</th></tr></thead> <tbody><tr><td>CPU</td> <td>ghcr.io/huggingface/text-embeddings-inference:cpu-1.2</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-1.2 (experimental)</td></tr> <tr><td>Ampere 80 (A100, A30)</td> <td>ghcr.io/huggingface/text-embeddings-inference:1.2</td></tr> <tr><td>Ampere 86 (A10, A40, …)</td> <td>ghcr.io/huggingface/text-embeddings-inference:86-1.2</td></tr> <tr><td>Ada Lovelace (RTX 4000 series, …)</td> <td>ghcr.io/huggingface/text-embeddings-inference:89-1.2</td></tr> <tr><td>Hopper (H100)</td> <td>ghcr.io/huggingface/text-embeddings-inference:hopper-1.2 (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"><span data-svelte-h="svelte-1kd6by1"><</span> <span data-svelte-h="svelte-x0xyl0">></span> <span data-svelte-h="svelte-1dajgef"><span class="underline ml-1.5">Update</span> on GitHub</span></a> <p></p> | |
| <script> | |
| { | |
| __sveltekit_1a9ukzg = { | |
| assets: "/docs/text-embeddings-inference/main/en", | |
| base: "/docs/text-embeddings-inference/main/en", | |
| env: {} | |
| }; | |
| const element = document.currentScript.parentElement; | |
| const data = [null,null]; | |
| Promise.all([ | |
| import("/docs/text-embeddings-inference/main/en/_app/immutable/entry/start.5929ce5e.js"), | |
| import("/docs/text-embeddings-inference/main/en/_app/immutable/entry/app.99d3b526.js") | |
| ]).then(([kit, app]) => { | |
| kit.start(app, element, { | |
| node_ids: [0, 12], | |
| data, | |
| form: null, | |
| error: null | |
| }); | |
| }); | |
| } | |
| </script> | |
Xet Storage Details
- Size:
- 12.8 kB
- Xet hash:
- 9c88f60b12a81ff7a816038396920f819fe77fb07612a7cf0e5c11bb4660770e
·
Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.