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Darwin-180B-RSI is a 180-billion-parameter mixture-of-experts vision-language model that its publisher describes as a targeted evolution of Qwen3.8-Flash-Next. The publisher says it preserved the parent’s 512 routed experts, router and vision encoder while updating selected attention paths and shared experts. Its “0.02%” figure is a publisher characterization, not an independently audited measurement in the sources cited here.
What does “changing 0.02%” mean?
The headline describes selective adaptation rather than rebuilding or retraining every component of a 180B model. FINAL-Bench/VIDRAFT identifies Qwen3.8-Flash-Next as Darwin-180B-RSI’s parent and says the release retains the parent’s 512 routed experts, router and vision encoder, while adapting selected attention paths and the shared expert. The model card does not provide an independently audited parameter count establishing the 0.02% figure, so treat that percentage as the publisher’s claim, not a verified measurement. Darwin-180B-RSI model card
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That distinction matters for a mixture-of-experts (MoE) model. The number of parameters in the stored model is not the same as the number activated for each token. Preserving routed experts can leave the large checkpoint intact even if only selected components are adapted; MoE routing can limit per-token computation, but it does not make the full model’s weights disappear from storage.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesHow did the recursive self-improvement process work?
The model card describes a loop of generating candidate solutions, checking them, and using verified reasoning for further training. In this account, “recursive” refers to successive rounds that use an improved model to attempt more problems; it does not mean that every answer the model produces automatically becomes training data.
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- Generate solutions: The model attempts practice problems described as previously unseen.
- Verify answers: Answers are checked against references or executable checks.
- Train selectively: The publisher says only reasoning associated with correct, verified solutions is used for further training.
- Repeat: The improved checkpoint can solve another round of practice problems.
FINAL-Bench/VIDRAFT also says it filters practice problems against evaluation sets using an 8-gram overlap check and does not use human-written reasoning traces. Those are descriptions from the publisher’s model card, not an independent audit of the training pipeline. The card’s sentence “Nothing unverified is learned” should likewise be read as publisher language. Darwin-180B-RSI model card
What results does the publisher report?
The model card reports benchmark results for Darwin-180B-RSI and, where shown, its parent. These are publisher-reported figures; the card cautions that comparison settings can differ, and the figures have not been independently reproduced here.
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| Measure | Darwin-180B-RSI | Parent, Qwen3.8-Flash-Next | What the source establishes |
|---|---|---|---|
| GPQA Diamond | 94.44% | Not stated in the cited card | Darwin score reported by FINAL-Bench/VIDRAFT |
| MMLU-Pro accuracy | 88.12% | 88.04% | Publisher-reported comparison |
| Mean reasoning length on MMLU-Pro | 3,833 tokens | 4,320 tokens | Publisher-reported figures; the card characterizes the reduction as 11% |
These numbers do not by themselves establish a broad, independently confirmed improvement. In particular, the MMLU-Pro accuracy difference reported for Darwin and its parent is 0.08 percentage points. The reasoning-length figures describe the card’s MMLU-Pro comparison, not a guarantee that Darwin will use fewer tokens on every task.
What changed in the R3 follow-up?
Darwin-180B-RSI-R3 is a later checkpoint and should not be confused with the original Darwin-180B-RSI results above. Its model card describes a second training round from R1 using 714 correct solutions drawn from 462 boundary problems. For a 1,000-question held-out SuperGPQA set, it reports a paired mean-of-four R1-to-R3 difference of +1.03 points, with a 95% confidence interval of [+0.05, +2.00]. The same card says GPQA differences are within noise. These are R3-card results, not replacements for R1’s benchmark figures. Darwin-180B-RSI-R3 model card
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Can you run Darwin-180B locally?
A Hugging Face Blog article published October 4, 2026 reports a 111 GB 4-bit GGUF and an SSD-streamed laptop setup with 32 GB of RAM and an 8 GB GPU. In that configuration, it reports 4.17 tokens per second. This is an example configuration, not a universal speed claim: generation rate and usability depend on hardware, storage, context size and workload. The article says the full quantized checkpoint still occupies 111 GB, so that laptop does not hold all of its weights in RAM. It recommends at least 120 GB of free NVMe space. Hugging Face Blog deployment article
The same article reports 18.4–21.0 tokens per second and 78.8 GB peak memory on a 16-thread AMD EPYC CPU setup. That was an in-memory configuration, so its speed is not directly comparable to the SSD-streamed laptop result. Long reasoning outputs can take substantial time even when the initial response rate seems usable.
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- Storage: Plan for the article’s recommendation of at least 120 GB free NVMe capacity for a reported 111 GB checkpoint.
- Memory: SSD streaming can let a machine use weights larger than its RAM, but it does not make the checkpoint fit entirely in memory.
- Latency: Treat 4.17 tokens per second as a reported result for the specified laptop setup, not a performance promise for other systems.
Is Darwin-180B-RSI the same as the Darwin Family method?
No. The separate Darwin Family paper describes training-free evolutionary merging using a 14-dimensional adaptive merge genome, MRI-Trust Fusion and an Architecture Mapper. Its abstract discusses evolutionary merges at 4B–35B scales. That is background on a related use of the Darwin name, not evidence that the 180B RSI release used the same training-free procedure. Darwin-180B-RSI’s model card instead describes a model-level training loop using verified solutions. Darwin Family paper
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What license applies?
The Darwin-180B-RSI and R3 model cards identify the Qwen Community License 1.0 inherited from the parent model. Check the license terms for your intended use; the weights should not be assumed to be unrestricted public-domain material. Darwin-180B-RSI model card · Darwin-180B-RSI-R3 model card
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How strong is the evidence?
The model cards and deployment article make specific, useful claims about architecture, training, benchmarks and hardware, but the benchmark results and the 0.02% change figure remain publisher-reported in the sources cited here. The R3 card supplies a distinct follow-up comparison, not an independent replication of the original release. Read the claims as evidence of what the publisher reports, while keeping the stated version, evaluation and hardware configuration attached to each number.
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