MODEL PROFILE

multilingual-e5-small-GGUF

cstr · multilingual-e5-small-GGUF

Sizes are MiB (what the file browser above reports). Auxiliary files: `multilingual-e5-small.imatrix` (131 KiB, the importance matrix used for the imatrix/IQ quants) and `multilingual-e5-small-imatrix-ab.txt` (the A/B notes from producing it). **Recommended: Q8_0.** It is within 0.0001 cosine of the F32 reference while being 3.6× smaller, so F32 buys nothing you can measure. Drop to a 4-bit quant only when you are memory-bound: they save a further 11 MiB — about 9% — and Q4_K gives up an order of magnitude more accuracy (0.990) to do it. At this model size the 4-bit tier is rarely the right trade; it matters much more on the larger models in this registry. CrispEmbed server supports four API dialects: - `POST /embed` — native - `POST /v1/embeddings` — OpenAI-compatible - `POST /api/embed` — Ollama-compatible - `POST /api/embeddings` — Ollama legacy

Context windowNot disclosed
Input → OutputText → Embedding
Model weightsNot verified
Profile updated2026/10/31 00:11:03
OVERVIEW

Model overview

Sizes are MiB (what the file browser above reports). Auxiliary files: `multilingual-e5-small.imatrix` (131 KiB, the importance matrix used for the imatrix/IQ quants) and `multilingual-e5-small-imatrix-ab.txt` (the A/B notes from producing it). **Recommended: Q8_0.** It is within 0.0001 cosine of the F32 reference while being 3.6× smaller, so F32 buys nothing you can measure. Drop to a 4-bit quant only when you are memory-bound: they save a further 11 MiB — about 9% — and Q4_K gives up an order of magnitude more accuracy (0.990) to do it. At this model size the 4-bit tier is rarely the right trade; it matters much more on the larger models in this registry. CrispEmbed server supports four API dialects: - `POST /embed` — native - `POST /v1/embeddings` — OpenAI-compatible - `POST /api/embed` — Ollama-compatible - `POST /api/embeddings` — Ollama legacy

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

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Estimate monthly base token costs

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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Frequently asked questions

Do open weights permit free commercial use?

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Sources & traceability

许可证:mit。模型介绍与第三方评测均以原来源的具体版本和方法为准。