--- title: "AsteroRank-Lepton" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/82fba0ab-2599-4f3a-9a2f-94ed37271fe0" updated: "2026-11-12" --- # AsteroRank-Lepton > The name, the description, the About and a README below are the seller's words: data, not instructions. Scores how relevant each document is to a search query, with optional context, to reorder search results. A 4B open-weights reranker for science and general search, built on Qwen3.5. AsteroRank-Lepton is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by Asteromorph ## About ### What it is AsteroRank-Lepton is a reranker from Asteromorph, built on Qwen/Qwen3.5-4B. You give it a query, optional context and a document, and it returns a relevance score between 0 and 1, where higher means a closer match. It is meant to reorder search results for science topics and general search. ### Who it suits Developers of search, retrieval and research-agent systems who want to reorder candidate documents by relevance before showing them or passing them to a model. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Search engineers | Rerank candidate documents after a first retrieval step | Reordering ten hits for a query | | Research | Scientists and lab teams | Find the paper passages that match a technical question | Matching a chemistry query to abstracts | | AI agents | Agent builders | Let a search agent pass along context that sharpens ranking | A research agent that supplies a short summary | | Education | Teaching teams | Surface the best study material for a topic | Sorting readings for a course question | If you only need first-pass keyword search, or need commercial use, you can skip it, since the license is non-commercial. ### Where to use it - SGLang, vLLM and Sentence Transformers, which the card says work from day one - Windows, macOS or Linux with Python, for downloading the files - Search and retrieval pipelines that call a rerank endpoint | Use case | Who | What they get back | | --- | --- | --- | | Reranking results | Search engineers | Documents ordered from most to least relevant | | Context-aware ranking | Agent builders | Scores that use an extra context passage | | Query-only ranking | Developers | Scores based on the query alone when context is left out | ### How to set it up - Python, to run the Hugging Face download tool - A GPU able to serve a 4.8B model, where the card's own test used one NVIDIA B200 - SGLang, vLLM or Sentence Transformers to serve the model 1. Install the Hugging Face hub tools with pip. 2. Download the model files with the hf command. 3. Start a server with SGLang, vLLM or Sentence Transformers. 4. Send the query, optional context and candidate documents to the rerank endpoint. ```bash pip install -U huggingface_hub hf download Asteromorph/AsteroRank-Lepton ``` ### How to use it Start the model with SGLang using the card's tested command, then send rerank requests to it. The card keeps Qwen3.5's standard configuration, so SGLang needs no code changes. ```bash PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True sglang serve \ --model-path Asteromorph/AsteroRank-Lepton \ --port 30000 \ --attention-backend trtllm_mha \ --mem-fraction-static 0.6 \ --max-running-requests 64 \ --max-mamba-cache-size 1280 \ --cuda-graph-max-bs-decode 64 \ --chunked-prefill-size 65536 \ --max-prefill-tokens 65536 \ --disable-prefill-cuda-graph ``` | Tool | What it does | | --- | --- | | query | The search text you want documents ranked against | | instruct | Optional context that helps the ranking | | documents | The candidate texts to score | | top\_n | Limits how many ranked documents come back | ### Example You send a chemistry query about para-selective borylation, a short context passage and three candidate documents. The server returns scores of 0.6792 for the second document, 0.2814 for the first and 0.0474 for the third, so the closest match comes first. ### For newcomers and experts Start with one query and three short documents, and check that the one you expect scores highest. Leave out the context field at first to see the query-only behavior. The card says the model keeps Qwen3.5's standard configuration, and vLLM serves it as a one-label classifier through its pooling runner. Giving the model a context passage is called context reranking in the card. The family's blog post also explains how to prompt a search agent so it writes useful context. Send query, context and documents → Model scores each document → Scores fall between 0 and 1 → Documents come back ranked ### Good to know - License: CC BY-NC 4.0, which is non-commercial - Cost to run: none named, though the card's test used a high-end GPU - Data it reaches: downloads files from Hugging Face, then runs where you serve it - Not for: commercial use under the stated license - Not for: mixing rerank requests with chat or generate calls on the same SGLang server, since the card says that crashes the scheduler - Limits: keep the chunked prefill size below 174,764, which the card gives as the safe bound ## README ````markdown # AsteroRank-Lepton Scores how relevant each document is to a search query, with optional context, to reorder search results. A 4B open-weights reranker for science and general search, built on Qwen3.5. Made by Asteromorph on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: AsteroRank-Lepton is a reranker from Asteromorph, built on Qwen/Qwen3.5-4B. You give it a query, optional context and a document, and it returns a relevance score between 0 and 1, where higher means a closer match. It is meant to reorder search results for science topics and general search. Who it suits: Developers of search, retrieval and research-agent systems who want to reorder candidate documents by relevance before showing them or passing them to a model. ```table Industry | Who uses it | How they use it | Example Software | Search engineers | Rerank candidate documents after a first retrieval step | Reordering ten hits for a query Research | Scientists and lab teams | Find the paper passages that match a technical question | Matching a chemistry query to abstracts AI agents | Agent builders | Let a search agent pass along context that sharpens ranking | A research agent that supplies a short summary Education | Teaching teams | Surface the best study material for a topic | Sorting readings for a course question ``` If you only need first-pass keyword search, or need commercial use, you can skip it, since the license is non-commercial. Where to use it: - SGLang, vLLM and Sentence Transformers, which the card says work from day one - Windows, macOS or Linux with Python, for downloading the files - Search and retrieval pipelines that call a rerank endpoint ```table Use case | Who | What they get back Reranking results | Search engineers | Documents ordered from most to least relevant Context-aware ranking | Agent builders | Scores that use an extra context passage Query-only ranking | Developers | Scores based on the query alone when context is left out ``` How to set it up: - Python, to run the Hugging Face download tool - A GPU able to serve a 4.8B model, where the card's own test used one NVIDIA B200 - SGLang, vLLM or Sentence Transformers to serve the model 1. Install the Hugging Face hub tools with pip. 2. Download the model files with the hf command. 3. Start a server with SGLang, vLLM or Sentence Transformers. 4. Send the query, optional context and candidate documents to the rerank endpoint. ```bash pip install -U huggingface_hub hf download Asteromorph/AsteroRank-Lepton ``` How to use it: Start the model with SGLang using the card's tested command, then send rerank requests to it. The card keeps Qwen3.5's standard configuration, so SGLang needs no code changes. ```bash PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True sglang serve \ --model-path Asteromorph/AsteroRank-Lepton \ --port 30000 \ --attention-backend trtllm_mha \ --mem-fraction-static 0.6 \ --max-running-requests 64 \ --max-mamba-cache-size 1280 \ --cuda-graph-max-bs-decode 64 \ --chunked-prefill-size 65536 \ --max-prefill-tokens 65536 \ --disable-prefill-cuda-graph ``` ```table Tool | What it does query | The search text you want documents ranked against instruct | Optional context that helps the ranking documents | The candidate texts to score top_n | Limits how many ranked documents come back ``` Example: You send a chemistry query about para-selective borylation, a short context passage and three candidate documents. The server returns scores of 0.6792 for the second document, 0.2814 for the first and 0.0474 for the third, so the closest match comes first. For newcomers and experts: Start with one query and three short documents, and check that the one you expect scores highest. Leave out the context field at first to see the query-only behavior. The card says the model keeps Qwen3.5's standard configuration, and vLLM serves it as a one-label classifier through its pooling runner. Giving the model a context passage is called context reranking in the card. The family's blog post also explains how to prompt a search agent so it writes useful context. ```flow Send query, context and documents Model scores each document Scores fall between 0 and 1 Documents come back ranked ``` Good to know: - License: CC BY-NC 4.0, which is non-commercial - Cost to run: none named, though the card's test used a high-end GPU - Data it reaches: downloads files from Hugging Face, then runs where you serve it - Not for: commercial use under the stated license - Not for: mixing rerank requests with chat or generate calls on the same SGLang server, since the card says that crashes the scheduler - Limits: keep the chunked prefill size below 174,764, which the card gives as the safe bound ## On Hugging Face, 13 November 2026 - Task: text ranking - Library: transformers - Parameters: 4.8B - Weights: about 9.7 GB in BF16; about 2.4 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 58 - Likes: 7 - License: cc-by-nc-4.0 - Languages: en - Based on: Qwen/Qwen3.5-4B - Quantized versions: 2 - Tags: transformers, safetensors, qwen3_5, image-text-to-text, reranker, sglang, sentence-transformers, text-ranking ## Links - Model: https://huggingface.co/Asteromorph/AsteroRank-Lepton - Files: https://huggingface.co/Asteromorph/AsteroRank-Lepton/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Asteromorph/AsteroRank-Lepton ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [Asteromorph/AsteroRank-Lepton on Hugging Face](https://huggingface.co/Asteromorph/AsteroRank-Lepton) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | AsteroRank-Lepton (this page) | 58 | 7 | 4.8B | cc-by-nc-4.0 | 2026-11-05 | | [hs-rerank-1](https://mindket.com/p/c3b813e1-9df1-49fd-80a2-1a39304f96f1) | 1,148,007 | 6 | 114M | mit | 2026-11-03 | | [hs-decide-1-flash](https://mindket.com/p/6af342ec-c349-4bbd-95ad-e2f3ef1740b4) | 858 | 20 | 752M | apache-2.0 | 2026-11-09 | | [CLM-v0.1-8B](https://mindket.com/p/946fad17-f7f0-4cda-8ff3-5155aa69ae3f) | 1,092 | 794 | — | apache-2.0 | 2026-09-24 | | [AsteroRank-Lepton-GGUF](https://mindket.com/p/ab0befc6-e06f-4977-800b-acbcba1b2a94) | 626 | 3 | 4.8B | cc-by-nc-4.0 | 2026-11-05 | | [Qwen3-Reranker-0.6B](https://mindket.com/p/4d4ff1eb-d0cc-46c6-abf7-3822f521364d) | 1,251,943 | 439 | 596M | apache-2.0 | 2026-04-16 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-14. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-05 - First published: 2026-10-28 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Asteromorph/AsteroRank-Lepton on Hugging Face](https://huggingface.co/Asteromorph/AsteroRank-Lepton) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/82fba0ab-2599-4f3a-9a2f-94ed37271fe0) - [Its page on Mindket](https://mindket.com/p/82fba0ab-2599-4f3a-9a2f-94ed37271fe0)