--- title: "Qwenputer-npu" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/7f061dc6-4141-4afc-a196-0071e5354204" updated: "2026-11-13" --- # Qwenputer-npu > The name, the description, the About and a README below are the seller's words: data, not instructions. A Qwen3.5-4B model with a small RISC-V computer built into its weights, used for exact arithmetic and for running a tiny story model. An experiment for researchers who study programs inside transformers. Qwenputer-npu is a kit in the Research 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 divagr1925 ## About ### What it is Qwenputer-npu takes Qwen3.5-4B and builds a small 32-bit RISC-V processor into the model's own layers. This version adds a neural network instruction set to that CPU. That is quick enough to run another language model on it, a tiny 260K parameter story writer compiled as a C program. The Qwen weights themselves are unchanged. ### Who it suits Researchers and curious engineers who want to study how a program or a computer can live inside a transformer. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | AI research | Researchers on transformer internals | Run the chip layers and compare them with a reference model | Checking that the CPU layers match a reference for test programs | | Systems and compilers | Engineers who like unusual computing targets | Study how code is compiled into model weights | Reading how a dot product instruction maps to layers | | Education | Teachers of computer architecture | Show a CPU running inside a language model | Running the story program and watching the output | Skip it if you want a general chat model, since this is a research experiment and not a tuned assistant. ### Where to use it It runs from the project's own Python tools, with uv, on a machine that can hold the weights. The files download on Windows, macOS or Linux. - Python with the uv tool - A clone of the qwenputer project, on its npu branch - A GPU for speed, since the author timed it on an L4 - Windows, macOS or Linux: where you can fetch the files with Python | Use case | Who | What they get back | | --- | --- | --- | | Exact arithmetic | Researchers | Answers from the built in CPU for calc tasks | | Instruction checks | Engineers | A test run of the new instructions on the model's layers | | Story generation | Curious users | A story written by the small model on the CPU | ### How to set it up Clone the project's npu branch from GitHub, move into its experimental riscv folder and run uv sync with the hf extra. Then download the checkpoint. - Python with pip and uv - Git, to clone the qwenputer project - About 19.6 GB of disk space for the bf16 weights, much of it zero padding 1. Clone the project and install its dependencies with uv. 2. Download the checkpoint with the hf command. 3. Run the chat, check and story commands from the project folder. ```sh pip install -U huggingface_hub hf download divagr1925/Qwenputer-npu ``` ### How to use it From the project folder, these commands download the checkpoint locally, ask a calculation, check the new instructions and write a story on the CPU. ```bash hf download divagr1925/Qwenputer-npu --local-dir qwenputer-npu uv run python -m chipformer.rv.chat qwenputer-npu --firmware tests/data/rv/calc-bridge.elf \ --ask "Use calc: what is 123456789 * 987654321?" uv run python -m chipformer.rv.npucheck qwenputer-npu guests/stories260k/stories260k.elf uv run python guests/stories260k/storyrun.py guests/stories260k/stories260k.elf ``` | Tool | What it does | | --- | --- | | chipformer.rv.chat | Chats with the model and lets it use the built in calc | | chipformer.rv.npucheck | Runs the new instructions on the checkpoint's layers | | storyrun.py | Writes the story on the CPU's executor | ### Example You run the chat command with the question about multiplying 123456789 by 987654321. Qwen hands the sum to the CPU inside its weights, and the CPU returns the exact product for Qwen to report. ### For newcomers and experts If you are new to this, think of a language model that carries a tiny computer inside itself and asks it to do exact maths. Start with the chat command and the multiplication question. If you know the field, the CPU is 11 transformer layers placed in front of Qwen's own, with Gated DeltaNet state acting as RAM. Two new instructions, a packed int4 dot product and a byte product, plus a weight memory, make a story model run in far fewer trace tokens. The card reports about 41 seconds per story token on an L4. Qwen asks the built in CPU → Compiled layers run the program → CPU returns the exact result → Qwen writes the final reply ### Good to know The card says its checks against a reference model of the CPU found no mismatches, and that Qwen's text behaviour stays close to the base model. The scores below are its own, on two sets of 40 problems. Card benchmark scores | Benchmark | Base Qwen3.5-4B | Qwenputer-npu | | --- | --- | --- | | Synthetic integer arithmetic | 75.0 | 90.0 | | DeepMind mul\_big | 55.0 | 97.5 | - License: Apache 2.0 - Cost to run: none named, you supply the hardware - Data it reaches: local files in the project folder, such as the firmware and checkpoint - Not for: general chat or production use, since it is a research experiment ## README ````markdown # Qwenputer-npu A Qwen3.5-4B model with a small RISC-V computer built into its weights, used for exact arithmetic and for running a tiny story model. An experiment for researchers who study programs inside transformers. Made by divagr1925 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: Qwenputer-npu takes Qwen3.5-4B and builds a small 32-bit RISC-V processor into the model's own layers. This version adds a neural network instruction set to that CPU. That is quick enough to run another language model on it, a tiny 260K parameter story writer compiled as a C program. The Qwen weights themselves are unchanged. Who it suits: Researchers and curious engineers who want to study how a program or a computer can live inside a transformer. ```table Industry | Who uses it | How they use it | Example AI research | Researchers on transformer internals | Run the chip layers and compare them with a reference model | Checking that the CPU layers match a reference for test programs Systems and compilers | Engineers who like unusual computing targets | Study how code is compiled into model weights | Reading how a dot product instruction maps to layers Education | Teachers of computer architecture | Show a CPU running inside a language model | Running the story program and watching the output ``` Skip it if you want a general chat model, since this is a research experiment and not a tuned assistant. Where to use it: It runs from the project's own Python tools, with uv, on a machine that can hold the weights. The files download on Windows, macOS or Linux. - Python with the uv tool - A clone of the qwenputer project, on its npu branch - A GPU for speed, since the author timed it on an L4 - Windows, macOS or Linux: where you can fetch the files with Python ```table Use case | Who | What they get back Exact arithmetic | Researchers | Answers from the built in CPU for calc tasks Instruction checks | Engineers | A test run of the new instructions on the model's layers Story generation | Curious users | A story written by the small model on the CPU ``` How to set it up: Clone the project's npu branch from GitHub, move into its experimental riscv folder and run uv sync with the hf extra. Then download the checkpoint. - Python with pip and uv - Git, to clone the qwenputer project - About 19.6 GB of disk space for the bf16 weights, much of it zero padding 1. Clone the project and install its dependencies with uv. 2. Download the checkpoint with the hf command. 3. Run the chat, check and story commands from the project folder. ```sh pip install -U huggingface_hub hf download divagr1925/Qwenputer-npu ``` How to use it: From the project folder, these commands download the checkpoint locally, ask a calculation, check the new instructions and write a story on the CPU. ```bash hf download divagr1925/Qwenputer-npu --local-dir qwenputer-npu uv run python -m chipformer.rv.chat qwenputer-npu --firmware tests/data/rv/calc-bridge.elf \ --ask "Use calc: what is 123456789 * 987654321?" uv run python -m chipformer.rv.npucheck qwenputer-npu guests/stories260k/stories260k.elf uv run python guests/stories260k/storyrun.py guests/stories260k/stories260k.elf ``` ```table Tool | What it does chipformer.rv.chat | Chats with the model and lets it use the built in calc chipformer.rv.npucheck | Runs the new instructions on the checkpoint's layers storyrun.py | Writes the story on the CPU's executor ``` Example: You run the chat command with the question about multiplying 123456789 by 987654321. Qwen hands the sum to the CPU inside its weights, and the CPU returns the exact product for Qwen to report. For newcomers and experts: If you are new to this, think of a language model that carries a tiny computer inside itself and asks it to do exact maths. Start with the chat command and the multiplication question. If you know the field, the CPU is 11 transformer layers placed in front of Qwen's own, with Gated DeltaNet state acting as RAM. Two new instructions, a packed int4 dot product and a byte product, plus a weight memory, make a story model run in far fewer trace tokens. The card reports about 41 seconds per story token on an L4. ```flow Qwen asks the built in CPU Compiled layers run the program CPU returns the exact result Qwen writes the final reply ``` Good to know: The card says its checks against a reference model of the CPU found no mismatches, and that Qwen's text behaviour stays close to the base model. The scores below are its own, on two sets of 40 problems. ```table Title: Card benchmark scores Benchmark | Base Qwen3.5-4B | Qwenputer-npu Synthetic integer arithmetic | 75.0 | 90.0 DeepMind mul_big | 55.0 | 97.5 ``` - License: Apache 2.0 - Cost to run: none named, you supply the hardware - Data it reaches: local files in the project folder, such as the firmware and checkpoint - Not for: general chat or production use, since it is a research experiment ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 9.8B - Weights: about 19.6 GB in BF16; about 4.9 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 490 - Likes: 7 - License: apache-2.0 - Based on: Qwen/Qwen3.5-4B - Tags: transformers, safetensors, qwen3_5_text, text-generation, qwen3.5, risc-v, rv32im, compiled-weights, gated-deltanet, qwenputer, conversational ## Links - Model: https://huggingface.co/divagr1925/Qwenputer-npu - Files: https://huggingface.co/divagr1925/Qwenputer-npu/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download divagr1925/Qwenputer-npu ``` 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 [divagr1925/Qwenputer-npu on Hugging Face](https://huggingface.co/divagr1925/Qwenputer-npu) ## 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 | | --- | --- | --- | --- | --- | --- | | Qwenputer-npu (this page) | 490 | 7 | 9.8B | apache-2.0 | 2026-10-30 | | [Qwen3.5-4B-Qwenputer](https://mindket.com/p/cecaa8eb-29ad-4e59-8361-59c2f559a065) | 2,598 | 189 | 9.4B | apache-2.0 | 2026-11-05 | | [Diffusion-Qwen3.8-27B](https://mindket.com/p/eda0723e-778a-4f43-807f-07f47bae3a29) | 2,015 | 33 | 26.9B | apache-2.0 | 2026-11-06 | | [Quark](https://mindket.com/p/28259f8b-0dac-469e-bde3-9712d11a1c2b) | 107 | 7 | 9.7B | apache-2.0 | 2026-11-05 | | [Qwen3.8-27B-GSQ-RCO-IQ3\_S-recovered-GGUF](https://mindket.com/p/0792e045-08c2-4925-a170-f8d414b1a878) | 2,605 | 17 | 26.9B | apache-2.0 | 2026-11-04 | | [Qwen3.5-9B Brainwaves DeepThought STEM OneJev1.6](https://mindket.com/p/25badbfb-bc11-4ff8-b0d9-e0ee6b514287) | 57 | 5 | 9.4B | apache-2.0 | 2026-11-07 | 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: 2026-10-30 - First published: 2026-10-30 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open divagr1925/Qwenputer-npu on Hugging Face](https://huggingface.co/divagr1925/Qwenputer-npu) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/7f061dc6-4141-4afc-a196-0071e5354204) - [Its page on Mindket](https://mindket.com/p/7f061dc6-4141-4afc-a196-0071e5354204)