--- title: "Terminal-RSI-27B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/b7d63976-c860-4b80-abca-f0c045cd8b2b" updated: "2026-11-12" --- # Terminal-RSI-27B > The name, the description, the About and a README below are the seller's words: data, not instructions. Works through multi-step terminal tasks such as inspecting a repository, editing code and repairing dependencies. A 27.8B-parameter agent model built on Qwen3.8-27B for researchers and agent builders. Terminal-RSI-27B 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 Teriminal-RSI ## About ### What it is Terminal-RSI-27B is a terminal-agent model from the Terminal-RSI project, built on Qwen3.8-27B. It is meant for tasks that need a running environment and a check of the result, such as repository inspection, code edits, dependency repair and rebuilding scientific workflows. Its training alternates two steps the card calls Recursive Self-Rewrite and Recursive Policy-Refine. ### Who it suits Researchers and developers who build or test agents that work in a terminal and check their own results. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software development | Agent builders | Run multi-step coding tasks in a terminal | Fixing a broken dependency in a repository | | Research | AI researchers | Study terminal-agent training and evaluation | Comparing runs on a terminal benchmark | | Science | Workflow engineers | Rebuild a scientific workflow step by step | Reconstructing an analysis pipeline | | Engineering tools | Tool developers | Inspect and edit code through commands | Reading a repository and changing a file | If you want a plain chat model, you can skip it, because the card says a standalone chat example does not reproduce its agent results. ### Where to use it - An agent runner that gives the model a terminal, tools and a checker - Python with the transformers library, after you download the files - Your own Windows, macOS or Linux machine with enough memory | Use case | Who | What they get back | | --- | --- | --- | | Repository inspection | Developers | Commands run and findings about the code | | Dependency repair | Developers | Edits and reruns until the environment works | | Workflow reconstruction | Scientists | A rebuilt multi-step workflow checked by a verifier | ### How to set it up - Python and pip - About 55.6 GB of disk space in BF16, or about 13.9 GB at 4-bit (an estimate) - An agent runner and a safe environment where it can run commands 1. Install the Hugging Face hub tools. 2. Download the model files. 3. Load the model by following the model card, then connect it to your runner. ```sh pip install -U huggingface_hub hf download Teriminal-RSI/Terminal-RSI-27B ``` ### How to use it Connect the model to a runner that lets it act in a terminal and checks the state afterwards. The paper's tests used a temperature of 0.60, a top-p of 0.95, JSON actions and thinking mode turned off. Results depend on the runner, tools, prompts and time budget. ### Example You give the agent a repository with a broken dependency and a task to get its tests passing. It inspects files, edits the dependency setup, reruns commands and stops when the verifier confirms the state. ### For newcomers and experts If you are new to agents, treat this as research material rather than a ready tool. Start by reading how the card describes its tasks before you connect it to a terminal. It has 27.8B parameters, 64 text layers, a hidden size of 5,120 and a configured context of 262,144 tokens. Training follows a base, RSR, RPR, RSR, RPR sequence, with PPO in the refine steps. The card marks its results as the paper's own, from the retained RPR-2 checkpoint. Agent gets a task in a terminal → It runs commands and edits files → A verifier checks the resulting state → It retries until the task passes ### Good to know Benchmarks: the paper reports Terminal-Bench scores, averaged over three runs, and the card omits outside model comparisons Success rate in percent, base model against Terminal-RSI-27B, paper-reported | Label | Value | | --- | --- | | Terminal-Bench 2.1 base | 51.70% | | Terminal-Bench 2.1 Terminal-RSI | 86.52% | | Terminal-Bench Hard base | 31.00% | | Terminal-Bench Hard Terminal-RSI | 79.00% | | Four-suite mean base | 20.68% | | Four-suite mean Terminal-RSI | 50.58% | - License: apache-2.0 - Cost to run: no API key or paid service is named, only hardware for a 27.8B model - Data it reaches: an agent run reads and changes files in whatever environment you give it, so use a sandbox - Not for: casual chat, since the card says a standalone chat example does not reproduce its agent evaluations ## README ````markdown # Terminal-RSI-27B Works through multi-step terminal tasks such as inspecting a repository, editing code and repairing dependencies. A 27.8B-parameter agent model built on Qwen3.8-27B for researchers and agent builders. Made by Teriminal-RSI 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: Terminal-RSI-27B is a terminal-agent model from the Terminal-RSI project, built on Qwen3.8-27B. It is meant for tasks that need a running environment and a check of the result, such as repository inspection, code edits, dependency repair and rebuilding scientific workflows. Its training alternates two steps the card calls Recursive Self-Rewrite and Recursive Policy-Refine. Who it suits: Researchers and developers who build or test agents that work in a terminal and check their own results. ```table Industry | Who uses it | How they use it | Example Software development | Agent builders | Run multi-step coding tasks in a terminal | Fixing a broken dependency in a repository Research | AI researchers | Study terminal-agent training and evaluation | Comparing runs on a terminal benchmark Science | Workflow engineers | Rebuild a scientific workflow step by step | Reconstructing an analysis pipeline Engineering tools | Tool developers | Inspect and edit code through commands | Reading a repository and changing a file ``` If you want a plain chat model, you can skip it, because the card says a standalone chat example does not reproduce its agent results. Where to use it: - An agent runner that gives the model a terminal, tools and a checker - Python with the transformers library, after you download the files - Your own Windows, macOS or Linux machine with enough memory ```table Use case | Who | What they get back Repository inspection | Developers | Commands run and findings about the code Dependency repair | Developers | Edits and reruns until the environment works Workflow reconstruction | Scientists | A rebuilt multi-step workflow checked by a verifier ``` How to set it up: - Python and pip - About 55.6 GB of disk space in BF16, or about 13.9 GB at 4-bit (an estimate) - An agent runner and a safe environment where it can run commands 1. Install the Hugging Face hub tools. 2. Download the model files. 3. Load the model by following the model card, then connect it to your runner. ```sh pip install -U huggingface_hub hf download Teriminal-RSI/Terminal-RSI-27B ``` How to use it: Connect the model to a runner that lets it act in a terminal and checks the state afterwards. The paper's tests used a temperature of 0.60, a top-p of 0.95, JSON actions and thinking mode turned off. Results depend on the runner, tools, prompts and time budget. Example: You give the agent a repository with a broken dependency and a task to get its tests passing. It inspects files, edits the dependency setup, reruns commands and stops when the verifier confirms the state. For newcomers and experts: If you are new to agents, treat this as research material rather than a ready tool. Start by reading how the card describes its tasks before you connect it to a terminal. It has 27.8B parameters, 64 text layers, a hidden size of 5,120 and a configured context of 262,144 tokens. Training follows a base, RSR, RPR, RSR, RPR sequence, with PPO in the refine steps. The card marks its results as the paper's own, from the retained RPR-2 checkpoint. ```flow Agent gets a task in a terminal It runs commands and edits files A verifier checks the resulting state It retries until the task passes ``` Good to know: Benchmarks: the paper reports Terminal-Bench scores, averaged over three runs, and the card omits outside model comparisons ```chart Title: Success rate in percent, base model against Terminal-RSI-27B, paper-reported Terminal-Bench 2.1 base: 51.70% Terminal-Bench 2.1 Terminal-RSI: 86.52% Terminal-Bench Hard base: 31.00% Terminal-Bench Hard Terminal-RSI: 79.00% Four-suite mean base: 20.68% Four-suite mean Terminal-RSI: 50.58% ``` - License: apache-2.0 - Cost to run: no API key or paid service is named, only hardware for a 27.8B model - Data it reaches: an agent run reads and changes files in whatever environment you give it, so use a sandbox - Not for: casual chat, since the card says a standalone chat example does not reproduce its agent evaluations ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 27.8B - Weights: about 55.6 GB in BF16; about 13.9 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 2,536 - Likes: 33 - License: apache-2.0 - Languages: en, zh - Quantized versions: 5 - Fine-tunes: 1 - Tags: transformers, safetensors, qwen3_5, image-text-to-text, terminal-rsi, terminal-agent, reinforcement-learning, recursive-self-improvement, code, long-context, text-generation, conversational ## Links - Model: https://huggingface.co/Teriminal-RSI/Terminal-RSI-27B - Files: https://huggingface.co/Teriminal-RSI/Terminal-RSI-27B/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Teriminal-RSI/Terminal-RSI-27B ``` 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 [Teriminal-RSI/Terminal-RSI-27B on Hugging Face](https://huggingface.co/Teriminal-RSI/Terminal-RSI-27B) ## 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 | | --- | --- | --- | --- | --- | --- | | Terminal-RSI-27B (this page) | 2,536 | 33 | 27.8B | apache-2.0 | 2026-10-21 | | [Mage-VL-Cowork](https://mindket.com/p/53460bdf-08aa-4019-bb00-2a650a2d0157) | 4,226 | 222 | 4.7B | apache-2.0 | 2026-10-30 | | [Sabi-Noema](https://mindket.com/p/04e2ec5b-5be2-45e5-ac63-8b75f7b11c4e) | 1,810 | 55 | 27.4B | apache-2.0 | 2026-10-19 | | [ColGrep-Agent-2B](https://mindket.com/p/b5c8de72-bc73-46d1-b926-75154f9a52c0) | 1,141 | 38 | 2.5B | apache-2.0 | 2026-10-11 | | [Maincoder-1B](https://mindket.com/p/4ab6e559-24e5-4649-98d3-82237119cace) | 451 | 101 | 1B | apache-2.0 | 2026-05-13 | | [Tessera-27B-B-CJK-RL](https://mindket.com/p/55f45d0c-0ba9-4dad-9289-e73ffa41ea9b) | 85 | 3 | 27.8B | apache-2.0 | 2026-11-06 | 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: 2026-10-21 - First published: 2026-10-21 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Teriminal-RSI/Terminal-RSI-27B on Hugging Face](https://huggingface.co/Teriminal-RSI/Terminal-RSI-27B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/b7d63976-c860-4b80-abca-f0c045cd8b2b) - [Its page on Mindket](https://mindket.com/p/b7d63976-c860-4b80-abca-f0c045cd8b2b)