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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Fast transformer inference for Ruby
| Date | Stars |
|---|---|
| 2026-07-24 | 616 |
| 2026-07-25 | 616 |
| 2026-07-28 | 616 |
| 2026-07-30 | 616 |
| 2026-08-06 | 616 |
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growth rate 0.00%/day
# Informers
:fire: Fast [transformer](https://github.com/huggingface/transformers.js) inference for Ruby
For non-ONNX models, check out [Transformers.rb](https://github.com/ankane/transformers-ruby) :slightly_smiling_face:
[](https://github.com/ankane/informers/actions)
## Installation
Add this line to your application’s Gemfile:
```ruby
gem "informers"
```
## Getting Started
- [Models](#models)
- [Pipelines](#pipelines)
## Models
Embedding
- [sentence-transformers/all-MiniLM-L6-v2](#sentence-transformersall-MiniLM-L6-v2)
- [sentence-transformers/multi-qa-MiniLM-L6-cos-v1](#sentence-transformersmulti-qa-MiniLM-L6-cos-v1)
- [sentence-transformers/all-mpnet-base-v2](#sentence-transformersall-mpnet-base-v2)
- [sentence-transformers/paraphrase-MiniLM-L6-v2](#sentence-transformersparaphrase-minilm-l6-v2)
- [mixedbread-ai/mxbai-embed-large-v1](#mixedbread-aimxbai-embed-large-v1)
- [Supabase/gte-small](#supabasegte-small)
- [intfloat/e5-base-v2](#intfloate5-base-v2)
- [nomic-ai/nomic-embed-text-v1](#nomic-ainomic-embed-text-v1)
- [BAAI/bge-base-en-v1.5](#baaibge-base-en-v15)
- [jinaai/jina-embeddings-v2-base-en](#jinaaijina-embeddings-v2-base-en)
- [Snowflake/snowflake-arctic-embed-m-v1.5](#snowflakesnowflake-arctic-embed-m-v15)
Reranking
- [mixedbread-ai/mxbai-rerank-base-v1](#mixedbread-aimxbai-rerank-base-v1)
- [jinaai/jina-reranker-v1-turbo-en](#jinaaijina-reranker-v1-turbo-en)
- [BAAI/bge-reranker-base](#baaibge-reranker-base)
- [Xenova/ms-marco-MiniLM-L-6-v2](#xenovams-marco-minilm-l-6-v2)
### sentence-transformers/all-MiniLM-L6-v2
[Docs](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
```ruby
sentences = ["This is an example sentence", "Each sentence is converted"]
model = Informers.pipeline("embedding", "sentence-transformers/all-MiniLM-L6-v2")
embeddings = model.(sentences)
```
### sentence-transformers/multi-qa-MiniLM-L6-cos-v1
[Docs](https://huggingface.co/Xenova/multi-qa-MiniLM-L6-cos-v1)
```ruby
query = "How many people live in London?"
docs = ["Around 9 Million people live in London", "London is known for its financial district"]
model = Informers.pipeline("embedding", "sentence-transformers/multi-qa-MiniLM-L6-cos-v1")
query_embedding = model.(query)
doc_embeddings = model.(docs)
scores = doc_embeddings.map { |e| e.zip(query_embedding).sum { |d, q| d * q } }
doc_score_pairs = docs.zip(scores).sort_by { |d, s| -s }
```
### sentence-transformers/all-mpnet-base-v2
[Docs](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
```ruby
sentences = ["This is an example sentence", "Each sentence is converted"]
model = Informers.pipeline("embedding", "sentence-transformers/all-mpnet-base-v2")
embeddings = model.(sentences)
```
### sentence-transformers/paraphrase-MiniLM-L6-v2
[Docs](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L6-v2)
```ruby
sentences = ["This is an example sentence", "Each sentence is converted"]
model = Informers.pipeline("embedding", "sentence-transformers/paraphrase-MiniLM-L6-v2")
embeddings = model.(sentences, normalize: false)
```
### mixedbread-ai/mxbai-embed-large-v1
[Docs](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1)
```ruby
query_prefix = "Represent this sentence for searching relevant passages: "
input = [
"The dog is barking",
"The cat is purring",
query_prefix + "puppy"
]
model = Informers.pipeline("embedding", "mixedbread-ai/mxbai-embed-large-v1")
embeddings = model.(input)
```
### Supabase/gte-small
[Docs](https://huggingface.co/Supabase/gte-small)
```ruby
sentences = ["That is a happy person", "That is a very happy person"]
model = Informers.pipeline("embedding", "Supabase/gte-small")
embeddings = model.(sentences)
```
### intfloat/e5-base-v2
[Docs](https://huggingface.co/intfloat/e5-base-v2)
```ruby
doc_prefix = "passage: "
query_prefix = "query: "
input = [
doc_prefix + "Ruby is a programming language Excerpt of 12,068 characters
Read on GitHub
Andrew Kane · United States
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:0ee60118ae08bd96, topic:named-entity-recognition, topic:sentiment-analysis
matched fp:0ee60118ae08bd96, topic:question-answering