{"v":1,"id":"model:hf:nomic-ai/nomic-embed-code-GGUF","slug":"model-nomic-ai-nomic-embed-code-gguf","kind":"model","category":"embedding","title":"nomic-embed-code-GGUF","summary":"Llama.cpp Quantizations of Nomic Embed Code: A State-of-the-Art Code Retriever","source":{"provider":"hf","ref":"nomic-ai/nomic-embed-code-GGUF","url":"https://huggingface.co/nomic-ai/nomic-embed-code-GGUF","rev":"ff2ddedde976ea623178981f18e36af33c0c2a94","fetchedAt":"2026-10-02T21:01:05.612Z","etag":"W/\"132c-257wlQXnrcfrwwFnOKCOobuxQEw\""},"author":{"name":"nomic-ai","url":"https://huggingface.co/nomic-ai"},"license":{"spdx":"apache-2.0","raw":"apache-2.0","open":true},"metrics":{"downloads":2438,"downloadsWeek":4402710,"likes":16,"takenAt":"2026-10-02T21:01:05.612Z"},"tags":["gguf","sentence-similarity","feature-extraction","endpoints_compatible"],"pipeline":"sentence-similarity","links":{"github":"nomic-ai/nomic","npm":"@huggingface/transformers"},"updatedAt":"2025-05-01T20:06:19.000Z","collectedAt":"2026-10-02T21:01:05.612Z","review":{"numbers":["2,438 downloads on Hugging Face","16 likes","license apache-2.0","4.1 GB for nomic-embed-code.Q4_K_M.gguf","4,402,710 npm downloads a week for @huggingface/transformers","latest @huggingface/transformers@4.3.0"],"log":null},"trust":"unlabeled","health":{"status":"alive","checkedAt":"2026-10-02T21:01:05.612Z","http":200},"description":"# Llama.cpp Quantizations of Nomic Embed Code: A State-of-the-Art Code Retriever\n\n[Blog](https://www.nomic.ai/blog/posts/introducing-state-of-the-art-nomic-embed-code) | [Technical Report](https://arxiv.org/abs/2412.01007) | [AWS SageMaker](https://aws.amazon.com/marketplace/seller-profile?id=seller-tpqidcj54zawi) | [Atlas Embedding and Unstructured Data Analytics Platform](https://atlas.nomic.ai)\n\nUsing llama.cpp commit 11683f579 for quantization.\n\nOriginal model: [nomic-embed-code](https://huggingface.co/nomic-ai/nomic-embed-code)\n\n## Usage\n\nThis model can be used with the [llama.cpp server](https://github.com/ggml-org/llama.cpp/blob/master/examples/embedding#post-v1embeddings-openai-compatible-embeddings-api) and other software that supports llama.cpp embedding models.\n\nQueries embedded with `nomic-embed-code` must begin with the following prefix:\n```\nRepresent this query for searching relevant code:\n```\n\nFor example, the code below shows how to use the prefix to embed user questions, e.g. in a RAG application.\n\nStart a llama.cpp server:\n```\nllama-server -m nomic-embed-code.Q4_0.gguf --embeddings --pooling last\n```\n\nAnd run this code:\n```python\nimport requests\nfrom textwrap import dedent\n\ndef dot(va, vb):\n    return sum(a*b for a, b in zip(va, vb))\ndef embed(texts):\n    resp = requests.post('http://localhost:8080/v1/embeddings', json={'input': texts}).json()\n    return [d['embedding'] for d in resp['data']]\n\ndocs = [\n    dedent(\"\"\"\\\n    def fn(n):\n        if n < 0:\n            raise ValueError\n        return 1 if n == 0 else n * fn(n - 1)\n    \"\"\").strip(),\n    dedent(\"\"\"\\\n    def fn(n):\n        print((\"Fizz\" * (n % 3 == 0) + \"Buzz\" * (n % 5 == 0)) or n)\n    \"\"\").strip(),\n]\ndocs_embed = embed(docs)\n\nquery = 'Calculate the n-th factorial'\nquery_embed = embed(['Represent this query for searching relevant code: ' + query])[0]\nprint(f'query: {query!r}')\nfor d, e in zip(docs, docs_embed):\n    print(f'\\nsim…\n\nSource: https://huggingface.co/nomic-ai/nomic-embed-code-GGUF","install":{"kind":"model","hfId":"nomic-ai/nomic-embed-code-GGUF","gated":false,"format":"gguf","files":[{"name":"nomic-embed-code.Q2_K.gguf","size":2837112128,"quant":"Q2_K","sha256":"afaf7fb764e3bbd850629291a52d3955b3376381a4662544b860d71f1e7338a9"},{"name":"nomic-embed-code.Q3_K_L.gguf","size":3854280000,"quant":"Q3_K_L","sha256":"848ea553eb9f788c32962ba336435b2ae60f1d449d572760ee42a55ce0141797"},{"name":"nomic-embed-code.Q3_K_M.gguf","size":3574211904,"quant":"Q3_K_M","sha256":"b9dbe007d13d967ed2135120b4c60350dce9d02af01dec29f6c26409c32f62b5"},{"name":"nomic-embed-code.Q3_K_S.gguf","size":3258189120,"quant":"Q3_K_S","sha256":"6d1d69385827e3cd28e44fb14cf56e4a4156aed72a38ea2f5dd96bebebb8b527"},{"name":"nomic-embed-code.Q4_0.gguf","size":4124828992,"quant":"Q4_0","sha256":"0999f5a97eb15363c4cad76cfa71c413db96f61e8a32a91166e9f45db30b895e"},{"name":"nomic-embed-code.Q4_1.gguf","size":4532659520,"quant":"Q4_1","sha256":"859e801467a37922e0e5a3157a6498a9d33cb2b0ee08daed42e6988b1d1f14de"},{"name":"nomic-embed-code.Q4_K_M.gguf","size":4376511808,"quant":"Q4_K_M","sha256":"4354a73ee9ff5d811efe552a515dfd518667ff25fdfc4ee9e10af3f617f96eec"},{"name":"nomic-embed-code.Q4_K_S.gguf","size":4151207232,"quant":"Q4_K_S","sha256":"be589a33ae8ad094e634adc05a7d186de41da6052a38b85608dc147b39ffab11"},{"name":"nomic-embed-code.Q5_K_M.gguf","size":5070144832,"quant":"Q5_K_M","sha256":"f234c58a5be4c5e89f71e3b7131150a568b9618d32a34ebb625e9c0f6e0be9fb"},{"name":"nomic-embed-code.Q5_K_S.gguf","size":4940490048,"quant":"Q5_K_S","sha256":"a75989528b69c854da8d3a9f2f6011c649dc190b7e7b7d81d1356feb395832fa"},{"name":"nomic-embed-code.Q6_K.gguf","size":5807129920,"quant":"Q6_K","sha256":"b179b7e381cb1c7d00a82f5c26858c94e19916257cbd6146c7e21c9240b3dd86"},{"name":"nomic-embed-code.Q8_0.gguf","size":7519464768,"quant":"Q8_0","sha256":"80bbd617cc55bad52ceb0f7dfad5d2f5b8080c61015cad7063ffc438b6561832"},{"name":"nomic-embed-code.bf16.gguf","size":14147857728,"quant":"BF16","sha256":"09340ae55bc36f8d5e8a423084253c6460be99fee91d855d086767f40638abcd"},{"name":"nomic-embed-code.f16.gguf","size":14147857728,"quant":"F16","sha256":"33d214c79b97308e82b84f97ebdbba58bc1d8a841ccaaa9053848a06a091b33e"},{"name":"nomic-embed-code.f32.gguf","size":28288429376,"quant":"F32","sha256":"99694fed45c5fd1badea488f7998bce7fa4c4d7e1e1b9e4f58b3d3e6892040e4"}],"totalBytes":110630375104,"suggestedFile":"nomic-embed-code.Q4_K_M.gguf","requirements":{"ramGb":6,"diskBytes":4376511808,"note":"estimate: suggested file size × 1.15 + 0.5 GB; a real measurement comes with lsh models"},"runWith":["llama.cpp"],"command":"lsh models install hf:nomic-ai/nomic-embed-code-GGUF"}}