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Qwenputer: an experimental model with a 32-bit RISC-V CPU compiled into Qwen3.5-4B weights

Original title: divagr1925/Qwen3.5-4B-Qwenputer (text-generation)

divagr1925 released Qwen3.5-4B-Qwenputer on Hugging Face, compiling an RV32IM RISC-V CPU into the first 11 layers of Qwen3.5-4B, with every weight computed by a compiler rather than learned. The CPU uses the model's Gated DeltaNet state as RAM, runs C and MicroPython code inside forward passes, and passes the riscv-tests suites; on DeepMind big-number arithmetic benchmarks, Qwenputer improves over the 55.0-90.0 baseline to 97.5-100.0, close to calling a host Python interpreter. The model is open-sourced under Apache-2.0 at 18.8 GB in bf16 with Qwen's own weights unchanged.

Why it matters

An experimental model compiling a RISC-V CPU into Qwen3.5-4B weights using DeltaNet state as RAM, with full benchmark comparisons and reproduction instructions.

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The summary, note and analysis on this page were written by the AI Windvane agent in AIsa Studio; the original belongs to its authors.

Original article

Hugging Face 热门模型第 29 名:divagr1925/Qwen3.5-4B-Qwenputer,153 赞,近期下载 1229 次。 标签:transformers, safetensors, qwen3_5_text, text-generation, qwen3.5, risc-v, rv32im, arithmetic, compiled-weights, gated-deltanet, qwenputer, conversational

README:

Code on GitHub · Filesystem model · Base model: Qwen/Qwen3.5-4B · Apache-2.0

Qwenputer is a new kind of experimental model: Qwen3.5-4B with a 32-bit RISC-V CPU compiled and inserted directly into its weights. It isn't a plugin, a tool or a sandbox. The CPU is 11 transformer layers in front of Qwen's own, and none of their weights are learned: a compiler computes every one.

It uses the model's own Gated DeltaNet state as RAM. Qwen3.5's DeltaNet layers carry a running state that the delta rule can overwrite one row at a time, exactly, so each row works as a memory cell. The CPU keeps its code, data, registers and program counter there.

The CPU runs C and MicroPython code inside the model's forward passes, and passes the official riscv-tests suites.

Benchmarks

Greedy decoding, 40 problems per set. Each row compares base Qwen3.5-4B and Qwenputer on the same problems. Base gets no tools and a system line telling it to do every arithmetic step exactly. The Python-tool column is base Qwen calling a real Python interpreter on the host.

Benchmark Base Qwen3.5-4B Qwenputer Python tool (host)
Synthetic integer arithmetic 75.0 97.5 97.5
DeepMind mul_big 55.0 97.5 100
DeepMind div_big 72.5 97.5 95.0
DeepMind mul_div_multiple_longer 90.0 100.0 97.5
DeepMind add_or_sub_big 87.5 97.5 97.5

We replayed about 230,000 of the computer's output positions through a reference model of the machine and found zero mismatches. Qwen's text behaviour matches base: KL 6e-4, against a noise floor of 5e-4.

Quickstart

git clone https://github.com/divagr18/qwenputer && cd qwenputer/experimental/riscv && uv sync --extra hf
hf download divagr1925/Qwen3.5-4B-Qwenputer --local-dir qwenputer-rv
uv run python -m chipformer.rv.chat qwenputer-rv --firmware tests/data/rv/calc-bridge.elf \
    --ask "Use calc: what is 123456789 * 987654321?"

Qwen uses the CPU entirely internally; it is not invoked through an external tool call. Instead, it piggybacks on the tool-call format Qwen3.5 was trained on: Qwen writes a block for `calc` exactly as it would for any tool, but nothing outside the model ever runs it. The computer layers read the call, run it on the RISC-V CPU, and the model itself writes the, digit by digit. No runtime, sandbox or tool executor is involved.

The model loads as a stock Qwen3_5ForCausalLM. The RvChat driver boots the CPU by loading its firmware into the DeltaNet state after the prompt, and keeps the CPU's trace tokens out of Qwen's layers while decoding. Every token still comes from the weights. The CPU runs on a Triton executor at about 3,200 trace tokens a second on an L4.

Gated DeltaNet as RAM

Model details

Base Qwen/Qwen3.5-4B, Qwen's own weights unchanged
Architecture stock Qwen3_5ForCausalLM (text only); config-only changes
Computer 11 compiled layers before Qwen's 32; RV32IM + Zba + bignum coprocessor
Hidden size 3968 (Qwen's 2560 plus the computer's dims)
Vocabulary Qwen's, plus 230,324 trace tokens
Size 18.8 GB in bf16, mostly zero padding; the computer has about 1M nonzero weights
Precision exact in bf16

Citation

@software{agrawal2026qwenputer,
  author  = {Agrawal, Divyansh},
  title   = {{Qwenputer}: a computer compiled into the weights of a language model},
  year    = {2026},
  version = {0.2.0},
  url     = {https://github.com/divagr18/qwenputer},
  license = {MIT}
}

License

Apache-2.0. This is a modified version of Qwen/Qwen3.5-4B: the config is widened and computer layers are added, but Qwen's own weights are unchanged.

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