Parameters are what determine an AI's capacity to learn, reason, and store information. Having 26 billion total parameters gives the model a huge breadth of knowledge. But by only "waking up" the 4 billion parameters relevant to your specific prompt, it delivers the high-quality results of a massive AI while remaining lightweight enough to run smoothly on everyday hardware.
The base model choice matters here. Google's Gemma 4, released on April 2, is built directly from the same research and technology as Gemini 3—the company said so explicitly at launch. That means Gemopus carries something no Qwen-based fine-tune can claim: The DNA of Google's own state-of-the-art closed model under the hood, wrapped in Anthropic's thinking style on top. The best of both worlds, more or less.
What makes Gemopus different from the wave of other Gemma fine-tunes flooding Hugging Face right now is the philosophy behind it. Jackrong deliberately chose not to force Claude's chain-of-thought reasoning traces into Gemma's weights—a shortcut most competing releases take.
His argument, backed by recent research, is that stuffing a student model with a teacher's surface-level reasoning text doesn't actually transfer real reasoning ability. It teaches imitation, not logic. "There is no need for excessive imagination or superstitious replication of the Claude-style chain of thought," the model card reads. Instead, he focused on answer quality, structural clarity, and conversational naturalness—fixing Gemma's stiff Wikipedia tone and its tendency to lecture you about things you didn't ask.
AI infrastructure engineer Kyle Hessling ran independent benchmarks and published the results directly on the model card. His verdict on the 26B variant was pretty favorable. "Happy to have benched this one pretty hard and it is an excellent finetune of an already exceptional model,” he wrote on X. “It rocks at one-shot requests over long contexts, and runs incredibly fast thanks to the MOE (mixture of experts) architecture."
Gemopus-4-26B-A4B from Jackrong is LIVE!
Happy to have benched this one pretty hard (see my benches in the model card) and it is an excellent finetune of an already exceptional model! My friend Jackrong is always cooking the greatest!
It rocks at one-shot requests over long…
The smaller E4B variant passed all 14 core competence tests—instruction following, coding, math, multi-step reasoning, translation, safety, caching—and cleared all 12 long-context tests at 30K and 60K tokens. On needle-in-haystack retrieval, it passed 13 out of 13 probes including a stretch test at one million tokens with YaRN 8× RoPE scaling.
The 26B extends natively to 131K context and all the way out to 524K with YaRN, which Hessling also stress-tested: "It also crushed my simple needle-in-the-haystack tests all the way out to an extended context of 524k!"
On edge hardware, the E4B is genuinely fast. Jackrong reports 45–60 tokens per second on iPhone 17 Pro Max, and 90–120 tokens per second on MacBook Air M3/M4 via MLX. The 26B MoE architecture means it offloads gracefully on unified memory systems or GPUs with under 10GB of VRAM. Hessling called it his daily driver recommendation for VRAM-starved setups.
Gemopus is not without its rough edges. Tool calling remains broken across the entire Gemma 4 series in llama.cpp and LM Studio—call failures, format mismatches, loops—so if your workflow depends on agents using external tools, this is not your model yet. Jackrong himself calls it "an engineering exploration reference rather than a fully production-ready solution," and recommends his own Qwopus 3.5 series for anyone who needs something more stable for real workloads.
And because Jackrong deliberately avoided aggressive Claude-style chain-of-thought distillation, don't expect it to feel as deeply Opus-brained as Qwopus—that was a conscious tradeoff for stability, not an oversight.
Yeah the philosophy on this one was stability first, it is my understanding that the Gemma models tend to become unstable if you force a bunch of Claude thinking traces into them, you can see this when testing many other Opus gemma fine tunes on hugging face.
Jackrong tried a…
One honest caveat: Gemma's training dynamics are messier than Qwen's for fine-tuners—wider loss fluctuations, more hyperparameter sensitivity. Jackrong says so himself. If you need a more battle-tested local model for production workflows, his Qwopus 3.5 series remains more robustly validated. But if you want an American model with Opus-style polish, Gemopus is currently your best available option. A denser 31B Gemopus variant is also in the pipeline, with Hessling teasing it as "a banger for sure."
