# bge-micro-v2 by Taylor: Open-Weight Model
Source: https://savrn.com/models/bge-micro-v2
Markdown alternate of the page above; the site index is https://savrn.com/llms.txt

---

## Runs On

What it takes to serve bge-micro-v2 (17M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
| --- | --- | --- | --- | --- | --- |
| 16-bit | 0.0 GB | 0.0 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 |
| 8-bit | 0.0 GB | 0.0 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 |
| 4-bit | 0.0 GB | 0.0 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 |

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the [SAVRN Index](https://savrn.com/ai-index/pricing/gpus), read Oct 23, 2026.

[bge-micro-v2 on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/bge-micro-v2/gpus)

## SAVRN's Notes on bge-micro-v2

Three transformer layers, 17 million parameters, and 156 MB of float32 weights: the publisher distilled bge-micro-v2 in two steps from BAAI/bge-small-en-v1.5, and what remains maps sentences and paragraphs into a 384-dimensional space. At 16-bit our memory line rounds to 0.0 GB. We price the cheapest slot at one MI300X with 192 GB for $1.85 an hour because that is the lowest line we carry, not because the model needs it; it rides on a card you already pay for.

MIT permits commercial use, modification and redistribution with the notices kept, so shipping it inside a product is paperwork only. Two checks before committing: the 512-token context caps each passage you embed, and the file lists no relation records, so the lineage to bge-small-en-v1.5 rests on the publisher's description alone. Downloads run 1,058,661 monthly, the last update was 2024-06-06, and no host price sits on the Index yet.

SAVRN Research, 2026-09-18

## Model Card

By Taylor, published under mit, revision 3edf6d7de0fa.

This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.

Distilled in a 2-step training process (bge-micro was step 1) from BAAI/bge-small-en-v1.5.

### Usage (Sentence-Transformers)

Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

```
pip install -U sentence-transformers
```

Then you can use the model like this:

```
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```

### Usage (HuggingFace Transformers)

Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

[Read the full model card (273 words)](https://savrn.com/models/bge-micro-v2/card)

## Configuration

Architecture

BertModel

Context length (tokens)

512

Layers

3

Hidden size

384

Feed-forward size

1,536

Attention heads

12

Vocabulary size

30,522

Stored precision

float32

Model type

bert

## Identity and Version

Repository

TaylorAI/bge-micro-v2

Publisher

Taylor

Task

Sentence similarity

Modality

Text

Library

sentence-transformers

Parameters

17M parameters

Languages

Not stated by the source

Revision

3edf6d7de0faa426b09780416fe61009f26ae589

First published

2023-10-11

Last updated

2024-06-06

## Files and Weights

17 files, 157.0 MB in total. The weights are 4 files totalling 156.0 MB in bin, onnx, safetensors.

Weights4 files · 156.0 MB

Configuration7 files · 1.7 KB

Tokenizer3 files · 944.7 KB

Documentation2 files · 66.6 KB

Repository1 file · 1.5 KB

Every file

| File | Type | Size | SHA-256 |
| --- | --- | --- | --- |
| model.safetensors | Weights | 34.8 MB | 792472b64c3c |
| onnx/model.onnx | Weights | 69.0 MB | 9f705befe60d |
| onnx/model_quantized.onnx | Weights | 17.4 MB | ed65e36025aa |
| pytorch_model.bin | Weights | 34.8 MB | f154c669357b |
| 1_Pooling/config.json | Configuration | 270 B | — |
| added_tokens.json | Configuration | 82 B | — |
| config.json | Configuration | 745 B | — |
| config_sentence_transformers.json | Configuration | 123 B | — |
| modules.json | Configuration | 229 B | — |
| sentence_bert_config.json | Configuration | 53 B | — |
| special_tokens_map.json | Configuration | 228 B | — |
| LICENSE | Documentation | 1.1 KB | — |
| README.md | Documentation | 65.5 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
| tokenizer.json | Tokenizer | 711.7 KB | — |
| tokenizer_config.json | Tokenizer | 1.6 KB | — |
| vocab.txt | Tokenizer | 231.5 KB | — |

## License and Download

License

mit

Access

Open weights, no gate

Download size

156.0 MB

[Download from Taylor](https://huggingface.co/TaylorAI/bge-micro-v2)

Released by Taylor through its official repository on Hugging Face. [Read the license](https://opensource.org/license/mit).

## Evaluations

Each result is shown as reported, with the conditions its reporter stated. None is a SAVRN measurement. A comparison lines two results up only when their configuration, unit and setup are all stated and identical.

| Benchmark | Conditions | Result | Reported by | Revision | Date |
| --- | --- | --- | --- | --- | --- |
| MTEB AmazonCounterfactualClassification (en) | Configuration enTask ClassificationMetric accuracyComparison conditions not established | 67.7612 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB AmazonCounterfactualClassification (en) | Configuration enTask ClassificationMetric apComparison conditions not established | 29.6378 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB AmazonCounterfactualClassification (en) | Configuration enTask ClassificationMetric f1Comparison conditions not established | 61.3118 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB AmazonPolarityClassification | Configuration defaultTask ClassificationMetric accuracyComparison conditions not established | 79.7547 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB AmazonPolarityClassification | Configuration defaultTask ClassificationMetric apComparison conditions not established | 74.214 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB AmazonPolarityClassification | Configuration defaultTask ClassificationMetric f1Comparison conditions not established | 79.6532 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB AmazonReviewsClassification (en) | Configuration enTask ClassificationMetric accuracyComparison conditions not established | 37.452 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB AmazonReviewsClassification (en) | Configuration enTask ClassificationMetric f1Comparison conditions not established | 37.0245 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric map_at_1Comparison conditions not established | 31.152 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric map_at_10Comparison conditions not established | 46.702 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric map_at_100Comparison conditions not established | 47.563 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric map_at_1000Comparison conditions not established | 47.567 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric map_at_3Comparison conditions not established | 42.058 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric map_at_5Comparison conditions not established | 44.608 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric mrr_at_1Comparison conditions not established | 32.006 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric mrr_at_10Comparison conditions not established | 47.064 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric mrr_at_100Comparison conditions not established | 47.91 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric mrr_at_1000Comparison conditions not established | 47.915 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric mrr_at_3Comparison conditions not established | 42.283 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric mrr_at_5Comparison conditions not established | 44.968 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric ndcg_at_1Comparison conditions not established | 31.152 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric ndcg_at_10Comparison conditions not established | 55.308 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric ndcg_at_100Comparison conditions not established | 58.965 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric ndcg_at_1000Comparison conditions not established | 59.067 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric ndcg_at_3Comparison conditions not established | 45.698 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric ndcg_at_5Comparison conditions not established | 50.296 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric precision_at_1Comparison conditions not established | 31.152 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric precision_at_10Comparison conditions not established | 8.279 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric precision_at_100Comparison conditions not established | 0.987 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric precision_at_1000Comparison conditions not established | 0.1 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric precision_at_3Comparison conditions not established | 18.753 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric precision_at_5Comparison conditions not established | 13.485 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric recall_at_1Comparison conditions not established | 31.152 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric recall_at_10Comparison conditions not established | 82.788 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric recall_at_100Comparison conditions not established | 98.72 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric recall_at_1000Comparison conditions not established | 99.502 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric recall_at_3Comparison conditions not established | 56.259 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric recall_at_5Comparison conditions not established | 67.425 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArxivClusteringP2P | Configuration defaultTask ClusteringMetric v_measureComparison conditions not established | 44.5269 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArxivClusteringS2S | Configuration defaultTask ClusteringMetric v_measureComparison conditions not established | 33.2457 | [TaylorAI](https://huggingface.co/TaylorAI/bge-micro-v2) Publisher reported | Evaluated revision not stated | — |

## Memory Requirements

| Precision | Weights in memory |
| --- | --- |
| As published | 156.0 MB |
| 16-bit | 0.0 GB |
| 8-bit | 0.0 GB |
| 4-bit | 0.0 GB |

Weights only, from the published parameter count; the key-value cache and runtime add to this.

## Questions About bge-micro-v2

### How much GPU memory does bge-micro-v2 need?

About 0 GB at 16-bit and 0 GB at 4-bit: the weights (17M parameters) plus a working margin. A long context needs more.

### What is the cheapest GPU to run bge-micro-v2 on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

### Can I use bge-micro-v2 commercially?

Yes. bge-micro-v2 is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.

### What is bge-micro-v2's context length?

512 tokens, from the maximum position embeddings in its published configuration.

## Similar Models

Model · Sentence similarity

### [paraphrase-MiniLM-L3-v2](https://savrn.com/models/paraphrase-minilm-l3-v2)

[Sentence Transformers](https://savrn.com/model-publishers/sentence-transformers)

This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. This model was trained by sentence-transformers. If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Open weights apache-2.0 17M parameters 512 tokens sentence-transformers

[View model](https://savrn.com/models/paraphrase-minilm-l3-v2)

Model · Sentence similarity

### [all-MiniLM-L6-v2](https://savrn.com/models/all-minilm-l6-v2)

[Sentence Transformers](https://savrn.com/model-publishers/sentence-transformers)

This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised contrastive learning objective. We used the pretrained…

Open weights apache-2.0 23M parameters 512 tokens sentence-transformers

[View model](https://savrn.com/models/all-minilm-l6-v2)

Model · Sentence similarity

### [paraphrase-MiniLM-L6-v2](https://savrn.com/models/paraphrase-minilm-l6-v2)

[Sentence Transformers](https://savrn.com/model-publishers/sentence-transformers)

This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. This model was trained by sentence-transformers. If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Open weights apache-2.0 23M parameters 512 tokens sentence-transformers

[View model](https://savrn.com/models/paraphrase-minilm-l6-v2)

Model · Sentence similarity

### [multi-qa-MiniLM-L6-cos-v1](https://savrn.com/models/multi-qa-minilm-l6-cos-v1)

[Sentence Transformers](https://savrn.com/model-publishers/sentence-transformers)

This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for semantic search. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search, have a look at: SBERT.net - Semantic Search Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the correct pooling-operation on-top of the contextualized word embeddings. Similarly to the PyTorch example above, to use the model…

Open weights 23M parameters 512 tokens sentence-transformers

[View model](https://savrn.com/models/multi-qa-minilm-l6-cos-v1)

Model · Sentence similarity

### [paraphrase-albert-small-v2](https://savrn.com/models/paraphrase-albert-small-v2)

[Sentence Transformers](https://savrn.com/model-publishers/sentence-transformers)

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. This model was trained by sentence-transformers. If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Open weights apache-2.0 12M parameters 512 tokens sentence-transformers

[View model](https://savrn.com/models/paraphrase-albert-small-v2)

Model · Sentence similarity

### [rubert-tiny2](https://savrn.com/models/rubert-tiny2)

[David Dale](https://savrn.com/model-publishers/cointegrated)

This is an updated version of cointegrated/rubert-tiny: a small Russian BERT-based encoder with high-quality sentence embeddings. This post in Russian gives more details. The differences from the previous version include: - sentence embeddings approximate LaBSE closer than before; - meaningful segment embeddings (tuned on the NLI task) - the model is focused only on Russian. The model should be used as is to produce sentence embeddings (e.g. for KNN classification of short texts) or fine-tuned for a downstream task. Sentence embeddings can be produced as follows: Alternatively, you can use the model with sentencetransformers: For those who want to run the inference with VLLM, there is a…

Open weights mit 29M parameters 2,048 tokens sentence-transformers

[View model](https://savrn.com/models/rubert-tiny2)

## Taylor

[All models and datasets](https://savrn.com/model-publishers/taylorai)

## Versions

- [3edf6d7de0fa](https://savrn.com/models/bge-micro-v2/versions/3edf6d7de0fa) · current 2026-09-18

## Explore More

- [All sentence similarity models](https://savrn.com/models/tasks/sentence-similarity)
- [All models under mit](https://savrn.com/models/licenses/mit)
- [Model comparisons](https://savrn.com/models/comparisons)
- [The model directory](https://savrn.com/models)
- [Open model prices by host](https://savrn.com/ai-index/pricing/open-models)

## Source

- Repository metadata, read 2026-10-19.
- [Hugging Face record](https://huggingface.co/TaylorAI/bge-micro-v2)
- [How the hub is built](https://savrn.com/model-hub/methodology)