--- title: "Instinct" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/c1f387a9-fe3e-4c8b-babd-ce057367f612" updated: "2026-11-13" --- # Instinct > The name, the description, the About and a README below are the seller's words: data, not instructions. Predicts the next edit you are likely to make in your code. A 7.6B model from Continue, fine-tuned from Qwen2.5-Coder-7B, for developers who want to run it on their own machine or server. Instinct 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 continuedev ## About ### What it is Instinct is a next edit model from Continue. It reads the edits you are making in code and predicts the change you are likely to make next. Continue trained it further from the Qwen2.5-Coder-7B base using real code edits. The model has 7.6B parameters. ### Who it suits Developers who use Continue and want edit suggestions from a model they run themselves, on a laptop or on a shared server. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software development | Developers who use Continue | Connect a self-hosted copy to the editor | Editing a function and getting the likely next change suggested | | Individual programming | Developers with one machine | Run the quantized copy through Ollama | Starting the small local copy before a coding session | | Platform teams | Teams that host their own models | Serve the model with SGLang or vLLM | Starting one server that several editors connect to | Skip it if you do not write code, or if you have no way to serve a 7.6B model. ### Where to use it It runs wherever you can serve a model and connect it to Continue. - Continue: the editor tool the model is meant to be connected to - Ollama: through a separate quantized copy for local use - SGLang: a server you launch from the command line - vLLM: another server option for the same weights - Windows, macOS or Linux: where you can download the files with Python | Use case | Who | What they get back | | --- | --- | --- | | Next edit suggestions | Developers in Continue | The change the model expects you to make next | | Local inference | Individual developers | A smaller quantized copy that runs on your own computer | | Self-hosted serving | Teams | A server that Continue can connect to | ### How to set it up Decide first how you will serve it, then fetch or run it. - Python with pip, to download the files - Continue, to connect the model to your editor - Room for the weights: about 15.2 GB in BF16, or about 3.8 GB at 4-bit as an estimate 1. Install the Hugging Face hub library. 2. Download the model files to your computer. 3. Serve them with SGLang or vLLM, or run the quantized copy with Ollama. 4. Connect the running model to Continue. ```sh pip install -U huggingface_hub hf download continuedev/instinct ``` To serve it with SGLang: ```sh python3 -m sglang.launch_server --model-path continuedev/instinct --load-format safetensors ``` To serve it with vLLM: ```sh vllm serve continuedev/instinct --served-model-name instinct --load-format safetensors ``` To run the quantized copy with Ollama: ```sh ollama run nate/instinct ``` ### How to use it Once a server or Ollama is running, point Continue at it and keep editing as usual. Each command below starts a different way of running the model. | Tool | What it does | | --- | --- | | hf download | Fetches the model files to your computer | | ollama run | Runs the quantized copy locally | | sglang.launch\_server | Serves the full weights through SGLang | | vllm serve | Serves the full weights through vLLM | ### Example You type ollama run nate/instinct in a terminal. Ollama starts the quantized copy of Instinct on your machine. You then point Continue at it and edit some code, and it offers the change it expects you to make next. ### For newcomers and experts If you are new to this, start with the Ollama command. It is the shortest way to get the model running, and then you connect it to Continue and try a small edit. If you know the field, this is a 7.6B model with Safetensors weights, built on Qwen2.5-Coder-7B and trained on real edit data. You can serve it with SGLang or vLLM, or use the Q4\_K\_M GGUF copy for lighter local inference. You edit code in Continue → Continue calls the served model → Instinct predicts the next edit → You review the suggestion ### Good to know Instinct is a text generation model for the transformers library. Other people have published 8 quantized versions and 1 fine-tuned version of it. - License: Apache 2.0 - Cost to run: none named, you supply the hardware - Data it reaches: the card names none beyond the editor you connect it to - Not for: the card lists no exclusions ## README ````markdown # Instinct Predicts the next edit you are likely to make in your code. A 7.6B model from Continue, fine-tuned from Qwen2.5-Coder-7B, for developers who want to run it on their own machine or server. Made by continuedev 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: Instinct is a next edit model from Continue. It reads the edits you are making in code and predicts the change you are likely to make next. Continue trained it further from the Qwen2.5-Coder-7B base using real code edits. The model has 7.6B parameters. Who it suits: Developers who use Continue and want edit suggestions from a model they run themselves, on a laptop or on a shared server. ```table Industry | Who uses it | How they use it | Example Software development | Developers who use Continue | Connect a self-hosted copy to the editor | Editing a function and getting the likely next change suggested Individual programming | Developers with one machine | Run the quantized copy through Ollama | Starting the small local copy before a coding session Platform teams | Teams that host their own models | Serve the model with SGLang or vLLM | Starting one server that several editors connect to ``` Skip it if you do not write code, or if you have no way to serve a 7.6B model. Where to use it: It runs wherever you can serve a model and connect it to Continue. - Continue: the editor tool the model is meant to be connected to - Ollama: through a separate quantized copy for local use - SGLang: a server you launch from the command line - vLLM: another server option for the same weights - Windows, macOS or Linux: where you can download the files with Python ```table Use case | Who | What they get back Next edit suggestions | Developers in Continue | The change the model expects you to make next Local inference | Individual developers | A smaller quantized copy that runs on your own computer Self-hosted serving | Teams | A server that Continue can connect to ``` How to set it up: Decide first how you will serve it, then fetch or run it. - Python with pip, to download the files - Continue, to connect the model to your editor - Room for the weights: about 15.2 GB in BF16, or about 3.8 GB at 4-bit as an estimate 1. Install the Hugging Face hub library. 2. Download the model files to your computer. 3. Serve them with SGLang or vLLM, or run the quantized copy with Ollama. 4. Connect the running model to Continue. ```sh pip install -U huggingface_hub hf download continuedev/instinct ``` To serve it with SGLang: ```sh python3 -m sglang.launch_server --model-path continuedev/instinct --load-format safetensors ``` To serve it with vLLM: ```sh vllm serve continuedev/instinct --served-model-name instinct --load-format safetensors ``` To run the quantized copy with Ollama: ```sh ollama run nate/instinct ``` How to use it: Once a server or Ollama is running, point Continue at it and keep editing as usual. Each command below starts a different way of running the model. ```table Tool | What it does hf download | Fetches the model files to your computer ollama run | Runs the quantized copy locally sglang.launch_server | Serves the full weights through SGLang vllm serve | Serves the full weights through vLLM ``` Example: You type ollama run nate/instinct in a terminal. Ollama starts the quantized copy of Instinct on your machine. You then point Continue at it and edit some code, and it offers the change it expects you to make next. For newcomers and experts: If you are new to this, start with the Ollama command. It is the shortest way to get the model running, and then you connect it to Continue and try a small edit. If you know the field, this is a 7.6B model with Safetensors weights, built on Qwen2.5-Coder-7B and trained on real edit data. You can serve it with SGLang or vLLM, or use the Q4_K_M GGUF copy for lighter local inference. ```flow You edit code in Continue Continue calls the served model Instinct predicts the next edit You review the suggestion ``` Good to know: Instinct is a text generation model for the transformers library. Other people have published 8 quantized versions and 1 fine-tuned version of it. - License: Apache 2.0 - Cost to run: none named, you supply the hardware - Data it reaches: the card names none beyond the editor you connect it to - Not for: the card lists no exclusions ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 7.6B - Weights: about 15.2 GB in BF16; about 3.8 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 112 - Likes: 72 - License: apache-2.0 - Based on: Qwen/Qwen2.5-Coder-7B - Quantized versions: 8 - Fine-tunes: 1 - Tags: transformers, safetensors, qwen2, text-generation, conversational ## Links - Model: https://huggingface.co/continuedev/instinct - Files: https://huggingface.co/continuedev/instinct/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download continuedev/instinct ``` 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 [continuedev/instinct on Hugging Face](https://huggingface.co/continuedev/instinct) ## 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 | | --- | --- | --- | --- | --- | --- | | Instinct (this page) | 112 | 72 | 7.6B | apache-2.0 | 2025-09-05 | | [Qwen2.5-7B-Instruct](https://mindket.com/p/b38cb44b-c57b-4e0c-b5f7-3e9da523b8d7) | 6,364,238 | 2,494 | 7.6B | apache-2.0 | 2025-01-12 | | [Qwen2.5-1.5B-Instruct](https://mindket.com/p/fb5b5dc6-8e04-49e2-b736-64b355ec5d08) | 5,181,515 | 941 | 1.5B | apache-2.0 | 2024-09-25 | | [Qwen2.5-0.5B-Instruct](https://mindket.com/p/a9936262-0327-4389-8a98-63a111c3855d) | 4,292,272 | 743 | 494M | apache-2.0 | 2024-09-25 | | [Qwen2.5-Coder-14B-Instruct](https://mindket.com/p/bf4cb9a2-fb0b-4f3c-965b-76d693eec384) | 1,860,327 | 214 | 14.8B | apache-2.0 | 2025-01-12 | | [Qwen2.5-7B-Instruct-AWQ](https://mindket.com/p/7c146c8d-af29-41f4-b822-21df91dc2497) | 1,578,054 | 63 | 7.6B | apache-2.0 | 2024-10-09 | 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: 2025-09-05 - First published: 2025-08-31 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open continuedev/instinct on Hugging Face](https://huggingface.co/continuedev/instinct) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/c1f387a9-fe3e-4c8b-babd-ce057367f612) - [Its page on Mindket](https://mindket.com/p/c1f387a9-fe3e-4c8b-babd-ce057367f612)