--- title: "zeta-2.1" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/a2377c5d-5a0b-4dc3-947f-1e36642c4904" updated: "2026-11-13" --- # zeta-2.1 > The name, the description, the About and a README below are the seller's words: data, not instructions. Predicts how the code around your cursor should be rewritten next, from the file context and your recent edits. A model from Zed Industries for people building editor features. zeta-2.1 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 zed-industries ## About ### What it is Zeta 2.1 guesses the next change you will make to your code. Zed Industries built it on Seed-Coder-8B-Base, and the card calls the job next-edit suggestion. You give it code context, a history of edits and an editable region around the cursor, and it writes the new content for that region. ### Who it suits It suits developers who build editor features and want a model that suggests the next change instead of answering chat questions. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Editor and plugin developers | Add next-edit suggestions to a code editor | Send the cursor region and recent edits, then show the returned rewrite | | Software | Teams that host their own models | Run a code edit model on their own machine | Download the weights and call them from a Python service | | Research | Code model researchers | Study a fine-tune of a coding base model | Compare its rewrites with the base model's output | People who want a chat assistant can skip it, since the card describes only edit prediction for a marked region. ### Where to use it The model has no app of its own. It runs wherever your code can load it and build the prompt. - Windows, macOS and Linux, with Python for the download - A code editor or tool that builds the prompt format the card describes - Python projects built on the transformers library, which the record lists | Use case | Who | What they get back | | --- | --- | --- | | Next-edit suggestions | Editor makers | Rewritten content for the editable region, wrapped in the same markers | | Offline experiments | Researchers | Model output for prompts built from real code and edit histories | ### How to set it up You need Python with pip, and enough disk for the weights. - Python with pip - About 16.5 GB for the BF16 weights, or about 4.1 GB at 4-bit, which is an estimate - Code of your own that builds the prompt and reads the answer 1. Install the Hugging Face hub library with pip. 2. Download the repository's files to your computer with the hf command. 3. Load the model in your own code and send it the prompt format below. ```sh pip install -U huggingface_hub hf download zed-industries/zeta-2.1 ``` ### How to use it Build the prompt in the card's suffix, prefix, middle style. Put the code after the region first, then related files, the edit history and the target file. Wrap the editable region in numbered markers and place the cursor marker inside it. ```text <[fim-suffix]> code after editable region <[fim-prefix]>related/file.py related file content edit_history --- a/some_file.py +++ b/some_file.py -old +new path/to/target_file.py code before editable region <|marker_1|> code that needs to<|user_cursor|> be rewritten <|marker_2|> <[fim-middle]> ``` The model should answer with the revised content between the same two markers, and nothing wrapped in backticks. ```text <|marker_1|> revised content for the editable region <|marker_2|> ``` | Tool | What it does | | --- | --- | | marker\_1 and marker\_2 | Mark the start and end of the editable region | | user\_cursor | Marks where the cursor sits inside the region | | filename | Introduces a related file, the edit history or the target file | | fim-middle | Ends the prompt, so the model writes the region | ### Example Put a few lines between the first and second markers with the cursor inside them, add a diff of your latest edit, and send the prompt. The model returns the revised lines wrapped in the same two markers. The card links a sample prompt and a sample output. ### For newcomers and experts If you are new to this, know that it is not a chat model. An editor sends it code and your recent changes, and it guesses the next edit. Start by building the card's short prompt by hand with a small file. If you know the field, this is an 8.3B coder fine-tune with a suffix, prefix, middle prompt and numbered multi-region markers. Your tool has to parse the marked output, and the editable region limits how much the model can rewrite at once. The editor gathers code, edits and the cursor spot → A prompt is built with numbered markers → The model writes the revised region → The editor swaps the text between the markers ### Good to know The card lists model version 0323-multi-region-filtered-r3 and gives no other training details. - License: Apache 2.0, as the card and record state - Languages: English only in the record - Other versions: the record counts 28 quantized copies and 0 fine-tuned ones made by other people - Cost to run: none named - Data it reaches: the code, edit history and related files you put in the prompt, plus the model files you download from Hugging Face Weights size in gigabytes | Label | Value | | --- | --- | | BF16 | 16.5 GB | | 4-bit (estimate) | 4.1 GB | ## README ````markdown # zeta-2.1 Predicts how the code around your cursor should be rewritten next, from the file context and your recent edits. A model from Zed Industries for people building editor features. Made by zed-industries 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: Zeta 2.1 guesses the next change you will make to your code. Zed Industries built it on Seed-Coder-8B-Base, and the card calls the job next-edit suggestion. You give it code context, a history of edits and an editable region around the cursor, and it writes the new content for that region. Who it suits: It suits developers who build editor features and want a model that suggests the next change instead of answering chat questions. ```table Industry | Who uses it | How they use it | Example Software | Editor and plugin developers | Add next-edit suggestions to a code editor | Send the cursor region and recent edits, then show the returned rewrite Software | Teams that host their own models | Run a code edit model on their own machine | Download the weights and call them from a Python service Research | Code model researchers | Study a fine-tune of a coding base model | Compare its rewrites with the base model's output ``` People who want a chat assistant can skip it, since the card describes only edit prediction for a marked region. Where to use it: The model has no app of its own. It runs wherever your code can load it and build the prompt. - Windows, macOS and Linux, with Python for the download - A code editor or tool that builds the prompt format the card describes - Python projects built on the transformers library, which the record lists ```table Use case | Who | What they get back Next-edit suggestions | Editor makers | Rewritten content for the editable region, wrapped in the same markers Offline experiments | Researchers | Model output for prompts built from real code and edit histories ``` How to set it up: You need Python with pip, and enough disk for the weights. - Python with pip - About 16.5 GB for the BF16 weights, or about 4.1 GB at 4-bit, which is an estimate - Code of your own that builds the prompt and reads the answer 1. Install the Hugging Face hub library with pip. 2. Download the repository's files to your computer with the hf command. 3. Load the model in your own code and send it the prompt format below. ```sh pip install -U huggingface_hub hf download zed-industries/zeta-2.1 ``` How to use it: Build the prompt in the card's suffix, prefix, middle style. Put the code after the region first, then related files, the edit history and the target file. Wrap the editable region in numbered markers and place the cursor marker inside it. ```text <[fim-suffix]> code after editable region <[fim-prefix]>related/file.py related file content edit_history --- a/some_file.py +++ b/some_file.py -old +new path/to/target_file.py code before editable region <|marker_1|> code that needs to<|user_cursor|> be rewritten <|marker_2|> <[fim-middle]> ``` The model should answer with the revised content between the same two markers, and nothing wrapped in backticks. ```text <|marker_1|> revised content for the editable region <|marker_2|> ``` ```table Tool | What it does marker_1 and marker_2 | Mark the start and end of the editable region user_cursor | Marks where the cursor sits inside the region filename | Introduces a related file, the edit history or the target file fim-middle | Ends the prompt, so the model writes the region ``` Example: Put a few lines between the first and second markers with the cursor inside them, add a diff of your latest edit, and send the prompt. The model returns the revised lines wrapped in the same two markers. The card links a sample prompt and a sample output. For newcomers and experts: If you are new to this, know that it is not a chat model. An editor sends it code and your recent changes, and it guesses the next edit. Start by building the card's short prompt by hand with a small file. If you know the field, this is an 8.3B coder fine-tune with a suffix, prefix, middle prompt and numbered multi-region markers. Your tool has to parse the marked output, and the editable region limits how much the model can rewrite at once. ```flow The editor gathers code, edits and the cursor spot A prompt is built with numbered markers The model writes the revised region The editor swaps the text between the markers ``` Good to know: The card lists model version 0323-multi-region-filtered-r3 and gives no other training details. - License: Apache 2.0, as the card and record state - Languages: English only in the record - Other versions: the record counts 28 quantized copies and 0 fine-tuned ones made by other people - Cost to run: none named - Data it reaches: the code, edit history and related files you put in the prompt, plus the model files you download from Hugging Face ```chart Title: Weights size in gigabytes BF16: 16.5 GB 4-bit (estimate): 4.1 GB ``` ## On Hugging Face, 14 November 2026 - Task: text generation - Library: transformers - Parameters: 8.3B - Weights: about 16.5 GB in BF16; about 4.1 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 2,797 - Likes: 140 - License: apache-2.0 - Languages: en - Based on: ByteDance-Seed/Seed-Coder-8B-Base - Quantized versions: 28 - Tags: transformers, safetensors, llama, text-generation, edit-prediction, next-edit-suggestion ## Links - Model: https://huggingface.co/zed-industries/zeta-2.1 - Files: https://huggingface.co/zed-industries/zeta-2.1/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download zed-industries/zeta-2.1 ``` 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 [zed-industries/zeta-2.1 on Hugging Face](https://huggingface.co/zed-industries/zeta-2.1) ## 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 | | --- | --- | --- | --- | --- | --- | | zeta-2.1 (this page) | 2,797 | 140 | 8.3B | apache-2.0 | 2026-05-08 | | [SmolLM2-135M](https://mindket.com/p/d6fb16a4-bb87-4d10-b168-c8f8485dedb8) | 2,596,139 | 269 | 135M | apache-2.0 | 2025-02-06 | | [MiniCPM5-2B](https://mindket.com/p/4d384af8-8281-4982-baed-7ea9aab0a3e1) | 1,039,518 | 1,850 | 2.5B | apache-2.0 | 2026-11-09 | | [TinyLlama-1.1B-Chat-v1.0](https://mindket.com/p/e73c1610-865e-48f4-a11d-4f6b640f3ae3) | 963,057 | 1,910 | 1.1B | apache-2.0 | 2024-03-17 | | [SmolLM2-135M-Instruct](https://mindket.com/p/64e0fae8-46ea-40d4-9f5e-7d3ea5c053af) | 841,106 | 474 | 135M | apache-2.0 | 2025-09-22 | | [Hermes 3 Llama-3.1 8B](https://mindket.com/p/a5c98b07-3a76-4848-b709-7d7eb5d9bbe1) | 336,953 | 550 | 8B | llama3 | 2024-09-08 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Bluesky · 柴犬被り · 2026-08-01](https://bsky.app/profile/bsky.shibadogcap.com/post/3ms2n2m25y42r): なんと、zeta使うって言って huggingface.co/zed-industrie… のほうが出てくる人 Original: https://x.com/4ba\_ba\_baba/status/2083695914750018036 (リンク: https://huggingface.co/zed-industries/zeta-2.1) ## 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: 2026-05-08 - First published: 2026-05-07 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open zed-industries/zeta-2.1 on Hugging Face](https://huggingface.co/zed-industries/zeta-2.1) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/a2377c5d-5a0b-4dc3-947f-1e36642c4904) - [Its page on Mindket](https://mindket.com/p/a2377c5d-5a0b-4dc3-947f-1e36642c4904)