--- title: "Needle 3" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/1c09a900-c54d-42c8-8fe4-e0d3ded42834" updated: "2026-11-12" --- # Needle 3 > The name, the description, the About and a README below are the seller's words: data, not instructions. A small on-device model from Cactus Compute for tool calling, structured extraction and text embeddings, used through the cactus Python package. For developers building local agents. Needle 3 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 Cactus-Compute ## About ### What it is Needle 3 is a model from Cactus Compute that runs on the device. The card says it is made for three jobs: calling tools, pulling structured data out of text, and making text embeddings. You use it through the cactus-compute Python package. This repository provides the model files needle3.cact and checkpoints/needle3.safetensors. The package downloads and keeps the engine and weights on first use. ### Who it suits Developers who want an agent that picks and calls tools on a phone, laptop or other device without a remote server. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | App developers | Let a local agent call functions | Look up the weather for a city | | Data work | Analysts | Pull structured fields out of text | Turn a message into named fields | | Search | Product teams | Make embeddings on the device | Compare short texts locally | Skip it if you want a general chat model or long writing, since the card only describes tool calling, extraction and embeddings. ### Where to use it - Python programs that import the cactus package - Consumer devices, which the Cactus banner names as the target - Windows, macOS or Linux with Python, to download the files | Use case | Who | What they get back | | --- | --- | --- | | Tool calling | Agent builders | A call to one of your Python functions with the right arguments | | Structured extraction | Data teams | Fields taken out of free text | | Embeddings | Search builders | Vectors for text made on the device | ### How to set it up - Python and pip - The cactus-compute package - A device the Cactus runtime supports, as listed in the Cactus repository 1. Install the package. 2. Write a Python function and mark it as a tool. 3. Start the agent with your tools and run a request. ```sh pip install -U cactus-compute ``` ### How to use it Mark a function with the tool decorator, give it a short description in a docstring, and pass it to the agent. Then call run with a plain question. ```python import cactus @cactus.tool def get_weather(city: str): "Get the current weather for a city." return {"city": city, "temp_c": 27, "sky": "clear"} with cactus.Cactus(tools=[get_weather]) as agent: print(agent.run("What's the weather in Lagos?")["results"]) ``` | Part | What it does | | --- | --- | | cactus.tool | Marks a function so the agent can call it | | cactus.Cactus | Starts the agent with the tools you give it | | agent.run | Sends a request and returns the results | ### Example You ask the agent for the weather in Lagos. It chooses the get\_weather tool, passes the city name, and returns the dictionary your function produced. ### For newcomers and experts If you are new to tool calling, copy the weather example and change the function to something of your own. Keep each tool small, with a clear description. If you know the field, the card points to separate guides for the Python API, designing tools, structured extraction and fine-tuning. This page does not repeat benchmark numbers, so check them on the makers' site. Install the cactus-compute package → Write and mark your tool → Start the agent with the tool → Run a request and read the results ### Good to know - The first run downloads and caches the engine and the weights - The banner on the card describes it as under 50 MB, but the card gives no exact file size - Hugging Face lists 3 quantized and 8 fine-tuned versions made by other people - License: Apache 2.0 - Cost to run: none named - Data it reaches: the files you download and the requests and tool results you pass through it - Not for: general chat or long writing ## README ````markdown # Needle 3 A small on-device model from Cactus Compute for tool calling, structured extraction and text embeddings, used through the cactus Python package. For developers building local agents. Made by Cactus-Compute 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: Needle 3 is a model from Cactus Compute that runs on the device. The card says it is made for three jobs: calling tools, pulling structured data out of text, and making text embeddings. You use it through the cactus-compute Python package. This repository provides the model files needle3.cact and checkpoints/needle3.safetensors. The package downloads and keeps the engine and weights on first use. Who it suits: Developers who want an agent that picks and calls tools on a phone, laptop or other device without a remote server. ```table Industry | Who uses it | How they use it | Example Software | App developers | Let a local agent call functions | Look up the weather for a city Data work | Analysts | Pull structured fields out of text | Turn a message into named fields Search | Product teams | Make embeddings on the device | Compare short texts locally ``` Skip it if you want a general chat model or long writing, since the card only describes tool calling, extraction and embeddings. Where to use it: - Python programs that import the cactus package - Consumer devices, which the Cactus banner names as the target - Windows, macOS or Linux with Python, to download the files ```table Use case | Who | What they get back Tool calling | Agent builders | A call to one of your Python functions with the right arguments Structured extraction | Data teams | Fields taken out of free text Embeddings | Search builders | Vectors for text made on the device ``` How to set it up: - Python and pip - The cactus-compute package - A device the Cactus runtime supports, as listed in the Cactus repository 1. Install the package. 2. Write a Python function and mark it as a tool. 3. Start the agent with your tools and run a request. ```sh pip install -U cactus-compute ``` How to use it: Mark a function with the tool decorator, give it a short description in a docstring, and pass it to the agent. Then call run with a plain question. ```python import cactus @cactus.tool def get_weather(city: str): "Get the current weather for a city." return {"city": city, "temp_c": 27, "sky": "clear"} with cactus.Cactus(tools=[get_weather]) as agent: print(agent.run("What's the weather in Lagos?")["results"]) ``` ```table Part | What it does cactus.tool | Marks a function so the agent can call it cactus.Cactus | Starts the agent with the tools you give it agent.run | Sends a request and returns the results ``` Example: You ask the agent for the weather in Lagos. It chooses the get_weather tool, passes the city name, and returns the dictionary your function produced. For newcomers and experts: If you are new to tool calling, copy the weather example and change the function to something of your own. Keep each tool small, with a clear description. If you know the field, the card points to separate guides for the Python API, designing tools, structured extraction and fine-tuning. This page does not repeat benchmark numbers, so check them on the makers' site. ```flow Install the cactus-compute package Write and mark your tool Start the agent with the tool Run a request and read the results ``` Good to know: - The first run downloads and caches the engine and the weights - The banner on the card describes it as under 50 MB, but the card gives no exact file size - Hugging Face lists 3 quantized and 8 fine-tuned versions made by other people - License: Apache 2.0 - Cost to run: none named - Data it reaches: the files you download and the requests and tool results you pass through it - Not for: general chat or long writing ## On Hugging Face, 13 November 2026 - Task: text generation - Library: cactus-compute - Downloads in the last 30 days: 147,784 - Likes: 411 - License: apache-2.0 - Quantized versions: 3 - Fine-tunes: 8 - Tags: cactus-compute, needle, tool-calling, function-calling, on-device, edge, quantization, webassembly, text-generation ## Links - Model: https://huggingface.co/Cactus-Compute/needle3 - Files: https://huggingface.co/Cactus-Compute/needle3/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Cactus-Compute/needle3 ``` 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 [Cactus-Compute/needle3 on Hugging Face](https://huggingface.co/Cactus-Compute/needle3) ## 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 | | --- | --- | --- | --- | --- | --- | | Needle 3 (this page) | 147,784 | 411 | — | apache-2.0 | 2026-11-05 | | [Needle 2](https://mindket.com/p/783df142-0389-4901-b593-575f13265cbd) | 1,736 | 341 | — | apache-2.0 | 2026-09-03 | | [Whistle](https://mindket.com/p/2a83a355-5760-461d-9833-a5d15efc8cc8) | 94,489 | 570 | — | apache-2.0 | 2026-11-05 | | [BotPsy-0.8B-GGUFs](https://mindket.com/p/0e1056b8-6362-46d4-90d2-5c20c6cc3d6c) | 827 | 17 | 752M | apache-2.0 | 2026-11-11 | | [BotPsy-0.8B](https://mindket.com/p/c6124e37-b426-4e92-b98a-2f3e97a58f8b) | 215 | 14 | 853M | apache-2.0 | 2026-11-11 | | [MNN-Pocket-0.3B](https://mindket.com/p/499799ab-3a05-4d11-854c-81e4c4e3e6fa) | 3,397 | 197 | 362M | apache-2.0 | 2026-11-10 | Numbers as Mindket last read them, on 2026-11-12. ## 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: 2026-09-16 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Cactus-Compute/needle3 on Hugging Face](https://huggingface.co/Cactus-Compute/needle3) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/1c09a900-c54d-42c8-8fe4-e0d3ded42834) - [Its page on Mindket](https://mindket.com/p/1c09a900-c54d-42c8-8fe4-e0d3ded42834)