lmstudio-community/granite-embedding-107m-multilingual-gguf - Free GGUF Download is indexed on GraySoft with repository links, GGUF quant files, and Hugging Face metadata. This page helps you pick a local model for guIDE or other runtimes. See related models in the same shard below.
Model Intelligence Sheet
lmstudio-community/granite-embedding-107m-multilingual-gguf overview
💫 Community Model> granite embedding 107m multilingual by Ibm-Granite 👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord. Model creator: ibm-granite Original model: granite-embedding-107m-multilingual GGUF quantization: provided by bartowski based on llama.cpp release b4381
Downloads
186
Likes
2
Pipeline
text-generation
Library
—
Visibility
Public
Access
Open
Repository Files & Downloads
4 files detected
Direct downloads for all repository files
| File | Type | Quantization | Size | Link |
|---|---|---|---|---|
| granite-embedding-107m-multilingual-Q3_K_L.gguf | GGUF | Q3_K_L | 111.09 MB | Download |
| granite-embedding-107m-multilingual-Q4_K_M.gguf | GGUF | Q4_K_M | 111.59 MB | Download |
| granite-embedding-107m-multilingual-Q6_K.gguf | GGUF | Q6_K | 114.60 MB | Download |
| granite-embedding-107m-multilingual-Q8_0.gguf | GGUF | — | 115.41 MB | Download |
Model Details Live
Metadata Inspector
Normalized metadata (stored in metadata_json)
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"cs",
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"es",
"fr",
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"ja",
"ko",
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"embeddings",
"multilingual"
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"license": "apache-2.0",
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"frontmatter": {
"quantized_by": "bartowski",
"pipeline_tag": "text-generation",
"language": [
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],
"tags": [
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]
},
"hero_image_url": "",
"summary": "## 💫 Community Model> granite embedding 107m multilingual by Ibm-Granite *👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord*. **Model creator:** ibm-granite **Original model**: granite-embedding-107m-multilingual **GGUF quantization:** provided by bartowski based on llama.cpp release b4381",
"quick_links": [],
"benchmark_table_html": "",
"readme_markdown": "---\nquantized_by: bartowski\npipeline_tag: text-generation\nlanguage:\n- en\n- ar\n- cs\n- de\n- es\n- fr\n- it\n- ja\n- ko\n- nl\n- pt\n- zh\ntags:\n- language\n- granite\n- embeddings\n- multilingual\nlicense: apache-2.0\nbase_model: ibm-granite/granite-embedding-107m-multilingual\nmodel-index:\n- name: ibm-granite/granite-embedding-107m-multilingual\n results:\n - task:\n type: Retrieval\n dataset:\n name: Miracl (en)\n type: miracl/mmteb-miracl\n config: en\n split: dev\n metrics:\n - type: ndcg_at_1\n value: 0.41176\n - type: ndcg_at_10\n value: 0.46682\n - type: ndcg_at_100\n value: 0.54326\n - type: ndcg_at_1000\n value: 0.56567\n - type: ndcg_at_20\n value: 0.50157\n - type: ndcg_at_3\n value: 0.41197\n - type: ndcg_at_5\n value: 0.42086\n - type: recall_at_1\n value: 0.19322\n - type: recall_at_10\n value: 0.57721\n - type: recall_at_100\n value: 0.83256\n - type: recall_at_1000\n value: 0.95511\n - type: recall_at_20\n value: 0.6757\n - type: recall_at_3\n value: 0.37171\n - type: recall_at_5\n value: 0.44695\n - task:\n type: Retrieval\n dataset:\n name: Miracl (ar)\n type: miracl/mmteb-miracl\n config: ar\n split: dev\n metrics:\n - type: ndcg_at_1\n value: 0.55559\n - type: ndcg_at_10\n value: 0.62541\n - type: ndcg_at_100\n value: 0.67101\n - type: ndcg_at_1000\n value: 0.6805\n - type: ndcg_at_20\n value: 0.64739\n - type: ndcg_at_3\n value: 0.56439\n - type: ndcg_at_5\n value: 0.59347\n - type: recall_at_1\n value: 0.37009\n - type: recall_at_10\n value: 0.73317\n - type: recall_at_100\n value: 0.90066\n - type: recall_at_1000\n value: 0.96272\n - type: recall_at_20\n value: 0.80205\n - type: recall_at_3\n value: 0.56903\n - type: recall_at_5\n value: 0.6518\n - task:\n type: Retrieval\n dataset:\n name: Miracl (bn)\n type: miracl/mmteb-miracl\n config: bn\n split: dev\n metrics:\n - type: ndcg_at_1\n value: 0.56691\n - type: ndcg_at_10\n value: 0.65484\n - type: ndcg_at_100\n value: 0.70142\n - type: ndcg_at_1000\n value: 0.70994\n - type: ndcg_at_20\n value: 0.67838\n - type: ndcg_at_3\n value: 0.5988\n - type: ndcg_at_5\n value: 0.62718\n - type: recall_at_1\n value: 0.3605\n - type: recall_at_10\n value: 0.76854\n - type: recall_at_100\n value: 0.9285\n - type: recall_at_1000\n value: 0.97928\n - type: recall_at_20\n value: 0.83667\n - type: recall_at_3\n value: 0.61596\n - type: recall_at_5\n value: 0.69766\n - task:\n type: Retrieval\n dataset:\n name: Miracl (de)\n type: miracl/mmteb-miracl\n config: de\n split: dev\n metrics:\n - type: ndcg_at_1\n value: 0.41967\n - type: ndcg_at_10\n value: 0.45141\n - type: ndcg_at_100\n value: 0.53461\n - type: ndcg_at_1000\n value: 0.55463\n - type: ndcg_at_20\n value: 0.49012\n - type: ndcg_at_3\n value: 0.39486\n - type: ndcg_at_5\n value: 0.41496\n - type: recall_at_1\n value: 0.19494\n - type: recall_at_10\n value: 0.53774\n - type: recall_at_100\n value: 0.83314\n - type: recall_at_1000\n value: 0.95045\n - type: recall_at_20\n value: 0.65659\n - type: recall_at_3\n value: 0.3556\n - type: recall_at_5\n value: 0.44448\n - task:\n type: Retrieval\n dataset:\n name: Miracl (es)\n type: miracl/mmteb-miracl\n config: es\n split: dev\n metrics:\n - type: ndcg_at_1\n value: 0.54475\n - type: ndcg_at_10\n value: 0.46593\n - type: ndcg_at_100\n value: 0.58079\n - type: ndcg_at_1000\n value: 0.60656\n - type: ndcg_at_20\n value: 0.51858\n - type: ndcg_at_3\n value: 0.4578\n - type: ndcg_at_5\n value: 0.44321\n - type: recall_at_1\n value: 0.15966\n - type: recall_at_10\n value: 0.49343\n - type: recall_at_100\n value: 0.82684\n - type: recall_at_1000\n value: 0.95299\n - type: recall_at_20\n value: 0.62367\n - type: recall_at_3\n value: 0.2949\n - type: recall_at_5\n value: 0.37983\n - task:\n type: Retrieval\n dataset:\n name: Miracl (fa)\n type: miracl/mmteb-miracl\n config: fa\n split: dev\n metrics:\n - type: ndcg_at_1\n value: 0.36709\n - type: ndcg_at_10\n value: 0.46961\n - type: ndcg_at_100\n value: 0.53262\n - type: ndcg_at_1000\n value: 0.55024\n - type: ndcg_at_20\n value: 0.49892\n - type: ndcg_at_3\n value: 0.40235\n - type: ndcg_at_5\n value: 0.42866\n - type: recall_at_1\n value: 0.22735\n - type: recall_at_10\n value: 0.59949\n - type: recall_at_100\n value: 0.83867\n - type: recall_at_1000\n value: 0.95007\n - type: recall_at_20\n value: 0.68947\n - type: recall_at_3\n value: 0.41781\n - type: recall_at_5\n value: 0.49374\n - task:\n type: Retrieval\n dataset:\n name: Miracl (fi)\n type: miracl/mmteb-miracl\n config: fi\n split: dev\n metrics:\n - type: ndcg_at_1\n value: 0.59245\n - type: ndcg_at_10\n value: 0.65551\n - type: ndcg_at_100\n value: 0.6967\n - type: ndcg_at_1000\n value: 0.70521\n - type: ndcg_at_20\n value: 0.67552\n - type: ndcg_at_3\n value: 0.58876\n - type: ndcg_at_5\n value: 0.61779\n - type: recall_at_1\n value: 0.37669\n - type: recall_at_10\n value: 0.76529\n - type: recall_at_100\n value: 0.9156\n - type: recall_at_1000\n value: 0.96977\n - type: recall_at_20\n value: 0.82685\n - type: recall_at_3\n value: 0.60234\n - type: recall_at_5\n value: 0.67135\n - task:\n type: Retrieval\n dataset:\n name: Miracl (fr)\n type: miracl/mmteb-miracl\n config: fr\n split: dev\n metrics:\n - type: ndcg_at_1\n value: 0.38776\n - type: ndcg_at_10\n value: 0.47589\n - type: ndcg_at_100\n value: 0.54641\n - type: ndcg_at_1000\n value: 0.5629\n - type: ndcg_at_20\n value: 0.51203\n - type: ndcg_at_3\n value: 0.38924\n - type: ndcg_at_5\n value: 0.42572\n - type: recall_at_1\n value: 0.22082\n - type: recall_at_10\n value: 0.61619\n - type: recall_at_100\n value: 0.87237\n - type: recall_at_1000\n value: 0.97449\n - type: recall_at_20\n value: 0.72689\n - type: recall_at_3\n value: 0.39527\n - type: recall_at_5\n value: 0.48983\n - task:\n type: Retrieval\n dataset:\n name: Miracl (hi)\n type: miracl/mmteb-miracl\n config: hi\n split: dev\n metrics:\n - type: ndcg_at_1\n value: 0.33143\n - type: ndcg_at_10\n value: 0.42084\n - type: ndcg_at_100\n value: 0.48647\n - type: ndcg_at_1000\n value: 0.50712\n - type: ndcg_at_20\n value: 0.45399\n - type: ndcg_at_3\n value: 0.34988\n - type: ndcg_at_5\n value: 0.37938\n - type: recall_at_1\n value: 0.17852\n - type: recall_at_10\n value: 0.55217\n - type: recall_at_100\n value: 0.79929\n - type: recall_at_1000\n value: 0.93434\n - type: recall_at_20\n value: 0.65231\n - type: recall_at_3\n value: 0.33765\n - type: recall_at_5\n value: 0.43828\n - task:\n type: Retrieval\n dataset:\n name: Miracl (id)\n type: miracl/mmteb-miracl\n config: id\n split: dev\n metrics:\n - type: ndcg_at_1\n value: 0.43854\n - type: ndcg_at_10\n value: 0.45459\n - type: ndcg_at_100\n value: 0.53643\n - type: ndcg_at_1000\n value: 0.56052\n - type: ndcg_at_20\n value: 0.48795\n - type: ndcg_at_3\n value: 0.41041\n - type: ndcg_at_5\n value: 0.42235\n - type: recall_at_1\n value: 0.19193\n - type: recall_at_10\n value: 0.5289\n - type: recall_at_100\n value: 0.79649\n - type: recall_at_1000\n value: 0.92937\n - type: recall_at_20\n value: 0.61813\n - type: recall_at_3\n value: 0.35431\n - type: recall_at_5\n value: 0.43348\n - task:\n type: Retrieval\n dataset:\n name: Miracl (ja)\n type: miracl/mmteb-miracl\n config: ja\n split: dev\n metrics:\n - type: ndcg_at_1\n value: 0.53256\n - type: ndcg_at_10\n value: 0.59922\n - type: ndcg_at_100\n value: 0.65407\n - type: ndcg_at_1000\n value: 0.66484\n - type: ndcg_at_20\n value: 0.62596\n - type: ndcg_at_3\n value: 0.53717\n - type: ndcg_at_5\n value: 0.56523\n - type: recall_at_1\n value: 0.34555\n - type: recall_at_10\n value: 0.71476\n - type: recall_at_100\n value: 0.91152\n - type: recall_at_1000\n value: 0.97728\n - type: recall_at_20\n value: 0.79811\n - type: recall_at_3\n value: 0.53482\n - type: recall_at_5\n value: 0.62327\n - task:\n type: Retrieval\n dataset:\n name: Miracl (ko)\n type: miracl/mmteb-miracl\n config: ko\n split: dev\n metrics:\n - type: ndcg_at_1\n value: 0.5493\n - type: ndcg_at_10\n value: 0.58413\n - type: ndcg_at_100\n value: 0.64374\n - type: ndcg_at_1000\n value: 0.65655\n - type: ndcg_at_20\n value: 0.61732\n - type: ndcg_at_3\n value: 0.53068\n - type: ndcg_at_5\n value: 0.55202\n - type: recall_at_1\n value: 0.32602\n - type: recall_at_10\n value: 0.68647\n - type: recall_at_100\n value: 0.87746\n - type: recall_at_1000\n value: 0.95524\n - type: recall_at_20\n value: 0.78089\n - type: recall_at_3\n value: 0.49173\n - type: recall_at_5\n value: 0.5827\n - task:\n type: Retrieval\n dataset:\n name: Miracl (ru)\n type: miracl/mmteb-miracl\n config: ru\n split: dev\n metrics:\n - type: ndcg_at_1\n value: 0.43131\n - type: ndcg_at_10\n value: 0.48262\n - type: ndcg_at_100\n value: 0.56158\n - type: ndcg_at_1000\n value: 0.57929\n - type: ndcg_at_20\n value: 0.52023\n - type: ndcg_at_3\n value: 0.42808\n - type: ndcg_at_5\n value: 0.44373\n - type: recall_at_1\n value: 0.22018\n - type: recall_at_10\n value: 0.58034\n - type: recall_at_100\n value: 0.84074\n - type: recall_at_1000\n value: 0.93938\n - type: recall_at_20\n value: 0.68603\n - type: recall_at_3\n value: 0.39307\n - type: recall_at_5\n value: 0.47077\n - task:\n type: Retrieval\n dataset:\n name: Miracl (sw)\n type: miracl/mmteb-miracl\n config: sw\n split: dev\n metrics:\n - type: ndcg_at_1\n value: 0.50415\n - type: ndcg_at_10\n value: 0.59111\n - type: ndcg_at_100\n value: 0.64312\n - type: ndcg_at_1000\n value: 0.65089\n - type: ndcg_at_20\n value: 0.61651\n - type: ndcg_at_3\n value: 0.5304\n - type: ndcg_at_5\n value: 0.56139\n - type: recall_at_1\n value: 0.33267\n - type: recall_at_10\n value: 0.72082\n - type: recall_at_100\n value: 0.91377\n - type: recall_at_1000\n value: 0.96152\n - type: recall_at_20\n value: 0.79943\n - type: recall_at_3\n value: 0.5548\n - type: recall_at_5\n value: 0.64302\n - task:\n type: Retrieval\n dataset:\n name: Miracl (te)\n type: miracl/mmteb-miracl\n config: te\n split: dev\n metrics:\n - type: ndcg_at_1\n value: 0.64372\n - type: ndcg_at_10\n value: 0.78175\n - type: ndcg_at_100\n value: 0.79523\n - type: ndcg_at_1000\n value: 0.79774\n - type: ndcg_at_20\n value: 0.78826\n - type: ndcg_at_3\n value: 0.74856\n - type: ndcg_at_5\n value: 0.77128\n - type: recall_at_1\n value: 0.63688\n - type: recall_at_10\n value: 0.90358\n - type: recall_at_100\n value: 0.96558\n - type: recall_at_1000\n value: 0.9847\n - type: recall_at_20\n value: 0.92834\n - type: recall_at_3\n value: 0.81804\n - type: recall_at_5\n value: 0.87198\n - task:\n type: Retrieval\n dataset:\n name: Miracl (th)\n type: miracl/mmteb-miracl\n config: th\n split: dev\n metrics:\n - type: ndcg_at_1\n value: 0.65484\n - type: ndcg_at_10\n value: 0.71774\n - type: ndcg_at_100\n value: 0.75362\n - type: ndcg_at_1000\n value: 0.75898\n - type: ndcg_at_20\n value: 0.73709\n - type: ndcg_at_3\n value: 0.66199\n - type: ndcg_at_5\n value: 0.68451\n - type: recall_at_1\n value: 0.45911\n - type: recall_at_10\n value: 0.82619\n - type: recall_at_100\n value: 0.95515\n - type: recall_at_1000\n value: 0.98854\n - type: recall_at_20\n value: 0.88447\n - type: recall_at_3\n value: 0.67437\n - type: recall_at_5\n value: 0.73786\n - task:\n type: Retrieval\n dataset:\n name: Miracl (yo)\n type: miracl/mmteb-miracl\n config: yo\n split: dev\n metrics:\n - type: ndcg_at_1\n value: 0.46218\n - type: ndcg_at_10\n value: 0.64685\n - type: ndcg_at_100\n value: 0.66941\n - type: ndcg_at_1000\n value: 0.67361\n - type: ndcg_at_20\n value: 0.65548\n - type: ndcg_at_3\n value: 0.57609\n - type: ndcg_at_5\n value: 0.62021\n - type: recall_at_1\n value: 0.42787\n - type: recall_at_10\n value: 0.82913\n - type: recall_at_100\n value: 0.93277\n - type: recall_at_1000\n value: 0.96499\n - type: recall_at_20\n value: 0.85994\n - type: recall_at_3\n value: 0.65406\n - type: recall_at_5\n value: 0.7542\n - task:\n type: Retrieval\n dataset:\n name: Miracl (zh)\n type: miracl/mmteb-miracl\n config: zh\n split: dev\n metrics:\n - type: ndcg_at_1\n value: 0.41985\n - type: ndcg_at_10\n value: 0.4837\n - type: ndcg_at_100\n value: 0.55961\n - type: ndcg_at_1000\n value: 0.5762\n - type: ndcg_at_20\n value: 0.51595\n - type: ndcg_at_3\n value: 0.42094\n - type: ndcg_at_5\n value: 0.44273\n - type: recall_at_1\n value: 0.21446\n - type: recall_at_10\n value: 0.59695\n - type: recall_at_100\n value: 0.87388\n - type: recall_at_1000\n value: 0.96833\n - type: recall_at_20\n value: 0.69252\n - type: recall_at_3\n value: 0.40377\n - type: recall_at_5\n value: 0.4903\n---\n## 💫 Community Model> granite embedding 107m multilingual by Ibm-Granite\n\n*👾 [LM Studio](https://lmstudio.ai) Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on [Discord](https://discord.gg/aPQfnNkxGC)*.\n\n**Model creator:** [ibm-granite](https://huggingface.co/ibm-granite)<br>\n**Original model**: [granite-embedding-107m-multilingual](https://huggingface.co/ibm-granite/granite-embedding-107m-multilingual)<br>\n**GGUF quantization:** provided by [bartowski](https://huggingface.co/bartowski) based on `llama.cpp` release [b4381](https://github.com/ggerganov/llama.cpp/releases/tag/b4381)<br>\n\n## Technical Details\n\nEmbedding model with vector size 384\n\nMax length of 512 tokens\n\n107 million parameters for high quality fast embedding\n\nSupported Languages: English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese.\n\n## Special thanks\n\n🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.\n\n## Disclaimers\n\nLM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.\n",
"related_quantizations": []
},
"tags": [
"gguf",
"language",
"granite",
"embeddings",
"multilingual",
"text-generation",
"en",
"ar",
"cs",
"de",
"es",
"fr",
"it",
"ja",
"ko",
"nl",
"pt",
"zh",
"base_model:ibm-granite/granite-embedding-107m-multilingual",
"base_model:quantized:ibm-granite/granite-embedding-107m-multilingual",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us",
"feature-extraction"
],
"likes": 2,
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"last_modified": "2024-12-23T04:59:41.000Z",
"created_at": "2024-12-18T16:53:53.000Z",
"pipeline_tag": "text-generation",
"library_name": ""
}
Source payload excerpt (from Hugging Face API)
{
"_id": "6762fe21cec46b2b62222814",
"id": "lmstudio-community/granite-embedding-107m-multilingual-GGUF",
"modelId": "lmstudio-community/granite-embedding-107m-multilingual-GGUF",
"sha": "fe02f2818f3aefb661ec656cab3be19024e40acd",
"createdAt": "2024-12-18T16:53:53.000Z",
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