--- title: "Bonsai-1.7B-unpacked" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/14bbc17d-6c93-4d4f-a6a2-1bb2c0327eb6" updated: "2026-11-13" --- # Bonsai-1.7B-unpacked > The name, the description, the About and a README below are the seller's words: data, not instructions. FP16 safetensors copy of the 1-bit Bonsai-1.7B model, for running it in standard Hugging Face tools. The card advises the native 1-bit releases instead, which are smaller and faster. Bonsai-1.7B-unpacked 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 prism-ml ## About ### What it is This repository holds Bonsai-1.7B, a model of about 1.7B parameters, unpacked from its 1-bit form into FP16 safetensors. It exists so you can run Bonsai in ordinary Hugging Face tooling or in frameworks that cannot yet read 1-bit weights. The FP16 files come to about 3.4 GB. ### Who it suits It suits developers who need Bonsai in a standard Hugging Face setup and cannot use the 1-bit builds yet. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Load Bonsai in a framework that lacks 1-bit support | Test prompts against the model in their existing pipeline | | Research | ML researchers | Work with the weights in plain FP16 form | Inspect the files with their usual Hugging Face tools | | Tool building | Framework maintainers | Check how their tool handles the model before 1-bit kernels arrive | Run it through a standard loader | Anyone who can run the native 1-bit models should skip this copy, because the card recommends those instead. ### Where to use it You use it wherever Hugging Face safetensors models load. - Standard Hugging Face tooling and frameworks without native 1-bit support - Windows, macOS or Linux, with Python, for downloading the files - For the recommended 1-bit builds: llama.cpp on CUDA, Metal or CPU, and MLX on Apple Silicon | Use case | Who | What they get back | | --- | --- | --- | | Run Bonsai in standard tools | Developers | The model working at full FP16 size | | Study the weights | Researchers | Plain safetensors files they can open anywhere | | Bridge until 1-bit support lands | Tool maintainers | A stand-in that loads today | ### How to set it up Setup is a download, nothing more. - Python, for the Hugging Face hub tools - Disk space for the FP16 files, about 3.4 GB 1. Install the Hugging Face hub tools. 2. Download the repository files. 3. Load the model with your usual Hugging Face tooling. ```sh pip install -U huggingface_hub hf download prism-ml/Bonsai-1.7B-unpacked ``` ### How to use it The card shows no run command. You load the files as you would any Hugging Face safetensors model. For better results on memory and speed, the card points to two native 1-bit releases. | Tool | What it does | | --- | --- | | Bonsai-1.7B GGUF Q1\_0 | A 1-bit GGUF file that runs in llama.cpp on CUDA, Metal or CPU | | Bonsai-1.7B MLX 1-bit | A 1-bit MLX build for Apple Silicon | ### Example You want to try Bonsai in a framework with no 1-bit support. You download this FP16 copy, load it like any Hugging Face model and run your prompts. It works at full size and has none of the memory or speed gains of the 1-bit version. ### For newcomers and experts The 1-bit version is the compact one. This repository is the same model expanded back to FP16 so ordinary tools can open it. If your tool reads GGUF or MLX, use the 1-bit build, and otherwise download this one. For someone who knows the field, the 1-bit kernels live in the authors' forks of MLX and llama.cpp. The card says this unpacked copy becomes unnecessary once those land upstream. The card claims the 1-bit format cuts memory use by up to 14 times and runs about three times faster, with less energy per token. It says this FP16 copy has none of those gains. 1-bit Bonsai weights → Unpacked into FP16 safetensors → Loaded by standard Hugging Face tools → Runs at full size ### Good to know The record estimates about 860 MB for a 4-bit copy. The card names no language, task or benchmark. - License: Apache-2.0 - Cost to run: none named - Data it reaches: it downloads files from Hugging Face. The card names no account, web or clipboard access. - Not for: anyone who can run the native 1-bit models, which the card recommends over this copy ## README ````markdown # Bonsai-1.7B-unpacked FP16 safetensors copy of the 1-bit Bonsai-1.7B model, for running it in standard Hugging Face tools. The card advises the native 1-bit releases instead, which are smaller and faster. Made by prism-ml 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: This repository holds Bonsai-1.7B, a model of about 1.7B parameters, unpacked from its 1-bit form into FP16 safetensors. It exists so you can run Bonsai in ordinary Hugging Face tooling or in frameworks that cannot yet read 1-bit weights. The FP16 files come to about 3.4 GB. Who it suits: It suits developers who need Bonsai in a standard Hugging Face setup and cannot use the 1-bit builds yet. ```table Industry | Who uses it | How they use it | Example Software | Developers | Load Bonsai in a framework that lacks 1-bit support | Test prompts against the model in their existing pipeline Research | ML researchers | Work with the weights in plain FP16 form | Inspect the files with their usual Hugging Face tools Tool building | Framework maintainers | Check how their tool handles the model before 1-bit kernels arrive | Run it through a standard loader ``` Anyone who can run the native 1-bit models should skip this copy, because the card recommends those instead. Where to use it: You use it wherever Hugging Face safetensors models load. - Standard Hugging Face tooling and frameworks without native 1-bit support - Windows, macOS or Linux, with Python, for downloading the files - For the recommended 1-bit builds: llama.cpp on CUDA, Metal or CPU, and MLX on Apple Silicon ```table Use case | Who | What they get back Run Bonsai in standard tools | Developers | The model working at full FP16 size Study the weights | Researchers | Plain safetensors files they can open anywhere Bridge until 1-bit support lands | Tool maintainers | A stand-in that loads today ``` How to set it up: Setup is a download, nothing more. - Python, for the Hugging Face hub tools - Disk space for the FP16 files, about 3.4 GB 1. Install the Hugging Face hub tools. 2. Download the repository files. 3. Load the model with your usual Hugging Face tooling. ```sh pip install -U huggingface_hub hf download prism-ml/Bonsai-1.7B-unpacked ``` How to use it: The card shows no run command. You load the files as you would any Hugging Face safetensors model. For better results on memory and speed, the card points to two native 1-bit releases. ```table Tool | What it does Bonsai-1.7B GGUF Q1_0 | A 1-bit GGUF file that runs in llama.cpp on CUDA, Metal or CPU Bonsai-1.7B MLX 1-bit | A 1-bit MLX build for Apple Silicon ``` Example: You want to try Bonsai in a framework with no 1-bit support. You download this FP16 copy, load it like any Hugging Face model and run your prompts. It works at full size and has none of the memory or speed gains of the 1-bit version. For newcomers and experts: The 1-bit version is the compact one. This repository is the same model expanded back to FP16 so ordinary tools can open it. If your tool reads GGUF or MLX, use the 1-bit build, and otherwise download this one. For someone who knows the field, the 1-bit kernels live in the authors' forks of MLX and llama.cpp. The card says this unpacked copy becomes unnecessary once those land upstream. The card claims the 1-bit format cuts memory use by up to 14 times and runs about three times faster, with less energy per token. It says this FP16 copy has none of those gains. ```flow 1-bit Bonsai weights Unpacked into FP16 safetensors Loaded by standard Hugging Face tools Runs at full size ``` Good to know: The record estimates about 860 MB for a 4-bit copy. The card names no language, task or benchmark. - License: Apache-2.0 - Cost to run: none named - Data it reaches: it downloads files from Hugging Face. The card names no account, web or clipboard access. - Not for: anyone who can run the native 1-bit models, which the card recommends over this copy ## On Hugging Face, 13 November 2026 - Parameters: 1.7B - Weights: about 3.4 GB in F16; about 860 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 2,362 - Likes: 15 - License: apache-2.0 - Quantized versions: 13 - Fine-tunes: 4 - Tags: safetensors, qwen3, prismml, bonsai ## Links - Model: https://huggingface.co/prism-ml/Bonsai-1.7B-unpacked - Files: https://huggingface.co/prism-ml/Bonsai-1.7B-unpacked/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download prism-ml/Bonsai-1.7B-unpacked ``` 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 [prism-ml/Bonsai-1.7B-unpacked on Hugging Face](https://huggingface.co/prism-ml/Bonsai-1.7B-unpacked) ## 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 | | --- | --- | --- | --- | --- | --- | | Bonsai-1.7B-unpacked (this page) | 2,362 | 15 | 1.7B | apache-2.0 | 2026-04-16 | | [1-bit Bonsai 27B (MLX)](https://mindket.com/p/d7dcb13a-512c-4d6e-8d42-d76b91e7140d) | 1,003,554 | 291 | 1.7B | apache-2.0 | 2026-07-14 | | [Bonsai-27B-gguf](https://mindket.com/p/0f4c3b2e-9ef6-4680-896e-4319b270e9f8) | 179,818 | 945 | 26.9B | apache-2.0 | 2026-07-17 | | [Bonsai-1.7B-GGUF](https://mindket.com/p/976ed9c8-c475-4a88-a05b-e149421d5c28) | 173,963 | 100 | 1.7B | apache-2.0 | 2026-04-18 | | [Qwen3-0.6B](https://mindket.com/p/0598bfe0-640a-4bf0-b9f4-ca4cf70a04ae) | 19,718,599 | 1,894 | 752M | apache-2.0 | 2025-07-26 | | [Qwen3-14B](https://mindket.com/p/56074bfc-f2ce-4109-8314-1af039530db4) | 12,965,376 | 540 | 14.8B | apache-2.0 | 2025-07-26 | 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: 2026-04-16 - First published: 2026-03-29 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open prism-ml/Bonsai-1.7B-unpacked on Hugging Face](https://huggingface.co/prism-ml/Bonsai-1.7B-unpacked) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/14bbc17d-6c93-4d4f-a6a2-1bb2c0327eb6) - [Its page on Mindket](https://mindket.com/p/14bbc17d-6c93-4d4f-a6a2-1bb2c0327eb6)