--- title: "ColGrep-Agent-2B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/b5c8de72-bc73-46d1-b926-75154f9a52c0" updated: "2026-11-13" --- # ColGrep-Agent-2B > The name, the description, the About and a README below are the seller's words: data, not instructions. A 2.5B-parameter model that finds the files and line ranges relevant to a bug report or question about a code repository, by searching and reading the repo. Runs locally through ColGrep. ColGrep-Agent-2B is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by lightonai ## About ### What it is ColGrep-Agent-2B is a small code-localization agent from LightOn. Given a bug report, a feature request or a question about a repository, it finds the files and line ranges that matter. It alternates between searching with ColGrep and opening files in a terminal that cannot change anything, then reports the precise spots. It is based on MiniCPM5-2B and is meant to take repository exploration off larger coding agents. ### Who it suits Developers and coding-agent builders who want to find the right code quickly, and who prefer a small local model over sending a whole repository to a big one. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Locate code for a bug or question | Finding where retries are configured | | AI tooling | Coding-agent builders | Hand repository search to a small helper | A larger agent that delegates code lookup | | Engineering teams | Maintainers | Triage issues faster | Pointing an issue at the likely files | You can skip it if you do not work with code repositories, or if you want a model that writes code rather than finds it. ### Where to use it It runs locally through the ColGrep command line on macOS, Linux and Windows, with GPU acceleration when available. It needs no Python environment, inference server or API key. - The colgrep command line - macOS and Linux with Homebrew, or Windows with PowerShell - Cargo, to build ColGrep from source - Larger coding agents that call colgrep --agent | Use case | Who | What they get back | | --- | --- | --- | | Issue localization | Maintainers | File paths and line ranges for a bug report | | Question answering about code | Developers | Locations in the repo that answer the question | | Delegated search | Agent builders | Compact results for a bigger model to use | ### How to set it up You install ColGrep once and let it fetch the model. The install-agent step downloads a Q8\_0 GGUF file of about 2.5 GB, checks that it loads and prepares a prompt cache. - Homebrew, or Cargo, or the card's installer scripts - About 2.5 GB for the default model file - A GPU is optional but speeds things up 1. Install ColGrep with Homebrew or Cargo, using one of the commands below. 2. Run the install-agent step, which the same commands include. 3. Index your repository once with colgrep init. ```bash brew install lightonai/tap/colgrep && colgrep --install-agent ``` ```bash cargo install colgrep && colgrep --install-agent ``` ```bash cd /path/to/project colgrep init ``` ### How to use it Ask a question in plain language with colgrep --agent. After the first index, searches notice changed files and update the index before answering. Results come as path, start line and end line. ```bash colgrep --agent "where does the agent decide which GPU backend llama.cpp uses" ``` | Tool | What it does | | --- | --- | | colgrep --install-agent | Downloads and prepares the default model | | colgrep init | Indexes the current repository | | colgrep --agent | Runs the agent on a question and returns code locations | ### Example On ColGrep's own code, the card asks where the agent decides which GPU backend llama.cpp uses. In the shown session on an M3 Pro, the agent made 9 turns and 2 searches and returned 5 locations in 21.7 seconds. ### For newcomers and experts If you are new, install ColGrep, index a small project and ask one concrete question. The answer is a list of files and line numbers you can open yourself. If you know the field, the agent was trained with reinforcement learning to search with ColGrep and read files through a read-only terminal. It loops over searches and reads, then calls finish with the final locations, which fits a larger agent's tool loop. Index the repository with colgrep init → Ask a question with colgrep --agent → Agent searches and reads files → Agent finishes with path and line ranges ### Good to know The output follows a path, start line and end line format, so scripts and other agents can use it directly. The card shows tool results omitted for readability in its sample session. - License: Apache 2.0 - Cost to run: none named, and no API key is needed - Data it reaches: the repository you index and ask about, plus the model file it downloads - Not for: writing code, since it only locates relevant code ## README ````markdown # ColGrep-Agent-2B A 2.5B-parameter model that finds the files and line ranges relevant to a bug report or question about a code repository, by searching and reading the repo. Runs locally through ColGrep. Made by lightonai 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: ColGrep-Agent-2B is a small code-localization agent from LightOn. Given a bug report, a feature request or a question about a repository, it finds the files and line ranges that matter. It alternates between searching with ColGrep and opening files in a terminal that cannot change anything, then reports the precise spots. It is based on MiniCPM5-2B and is meant to take repository exploration off larger coding agents. Who it suits: Developers and coding-agent builders who want to find the right code quickly, and who prefer a small local model over sending a whole repository to a big one. ```table Industry | Who uses it | How they use it | Example Software | Developers | Locate code for a bug or question | Finding where retries are configured AI tooling | Coding-agent builders | Hand repository search to a small helper | A larger agent that delegates code lookup Engineering teams | Maintainers | Triage issues faster | Pointing an issue at the likely files ``` You can skip it if you do not work with code repositories, or if you want a model that writes code rather than finds it. Where to use it: It runs locally through the ColGrep command line on macOS, Linux and Windows, with GPU acceleration when available. It needs no Python environment, inference server or API key. - The colgrep command line - macOS and Linux with Homebrew, or Windows with PowerShell - Cargo, to build ColGrep from source - Larger coding agents that call colgrep --agent ```table Use case | Who | What they get back Issue localization | Maintainers | File paths and line ranges for a bug report Question answering about code | Developers | Locations in the repo that answer the question Delegated search | Agent builders | Compact results for a bigger model to use ``` How to set it up: You install ColGrep once and let it fetch the model. The install-agent step downloads a Q8_0 GGUF file of about 2.5 GB, checks that it loads and prepares a prompt cache. - Homebrew, or Cargo, or the card's installer scripts - About 2.5 GB for the default model file - A GPU is optional but speeds things up 1. Install ColGrep with Homebrew or Cargo, using one of the commands below. 2. Run the install-agent step, which the same commands include. 3. Index your repository once with colgrep init. ```bash brew install lightonai/tap/colgrep && colgrep --install-agent ``` ```bash cargo install colgrep && colgrep --install-agent ``` ```bash cd /path/to/project colgrep init ``` How to use it: Ask a question in plain language with colgrep --agent. After the first index, searches notice changed files and update the index before answering. Results come as path, start line and end line. ```bash colgrep --agent "where does the agent decide which GPU backend llama.cpp uses" ``` ```table Tool | What it does colgrep --install-agent | Downloads and prepares the default model colgrep init | Indexes the current repository colgrep --agent | Runs the agent on a question and returns code locations ``` Example: On ColGrep's own code, the card asks where the agent decides which GPU backend llama.cpp uses. In the shown session on an M3 Pro, the agent made 9 turns and 2 searches and returned 5 locations in 21.7 seconds. For newcomers and experts: If you are new, install ColGrep, index a small project and ask one concrete question. The answer is a list of files and line numbers you can open yourself. If you know the field, the agent was trained with reinforcement learning to search with ColGrep and read files through a read-only terminal. It loops over searches and reads, then calls finish with the final locations, which fits a larger agent's tool loop. ```flow Index the repository with colgrep init Ask a question with colgrep --agent Agent searches and reads files Agent finishes with path and line ranges ``` Good to know: The output follows a path, start line and end line format, so scripts and other agents can use it directly. The card shows tool results omitted for readability in its sample session. - License: Apache 2.0 - Cost to run: none named, and no API key is needed - Data it reaches: the repository you index and ask about, plus the model file it downloads - Not for: writing code, since it only locates relevant code ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 2.5B - Weights: about 5 GB in BF16; about 1.3 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 1,141 - Likes: 38 - License: apache-2.0 - Languages: en - Based on: openbmb/MiniCPM5-2B - Quantized versions: 6 - Tags: transformers, safetensors, llama, text-generation, code, code-search, tool-use, reinforcement-learning, colgrep, conversational ## Links - Model: https://huggingface.co/lightonai/colgrep-agent-2B - Files: https://huggingface.co/lightonai/colgrep-agent-2B/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download lightonai/colgrep-agent-2B ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [lightonai/colgrep-agent-2B on Hugging Face](https://huggingface.co/lightonai/colgrep-agent-2B) ## 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 | | --- | --- | --- | --- | --- | --- | | ColGrep-Agent-2B (this page) | 1,141 | 38 | 2.5B | apache-2.0 | 2026-10-11 | | [Sabi-Noema](https://mindket.com/p/04e2ec5b-5be2-45e5-ac63-8b75f7b11c4e) | 1,810 | 55 | 27.4B | apache-2.0 | 2026-10-19 | | [Terminal-RSI-27B](https://mindket.com/p/b7d63976-c860-4b80-abca-f0c045cd8b2b) | 2,536 | 33 | 27.8B | apache-2.0 | 2026-10-21 | | [Maincoder-1B](https://mindket.com/p/4ab6e559-24e5-4649-98d3-82237119cace) | 451 | 101 | 1B | apache-2.0 | 2026-05-13 | | [mSlideOn](https://mindket.com/p/e92bd39d-a0eb-440c-8a0d-8c22298b51ce) | 706 | 16 | 339M | apache-2.0 | 2026-10-20 | | [Slonik](https://mindket.com/p/d1ed3759-b732-40ee-a4dc-1d69def72a00) | 0 | 5 | — | apache-2.0 | 2026-11-10 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## 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: 2026-10-11 - First published: 2026-10-03 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open lightonai/colgrep-agent-2B on Hugging Face](https://huggingface.co/lightonai/colgrep-agent-2B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/b5c8de72-bc73-46d1-b926-75154f9a52c0) - [Its page on Mindket](https://mindket.com/p/b5c8de72-bc73-46d1-b926-75154f9a52c0)