-> **Axion 1.0-35B** is an Apache-2.0 reproduction checkpoint of ByteDance-linked Axion 1.0, following the recipe in *Axion 1.0: Open Agent Foundation Models, Simplified*. The 35B MoE model has approximately 3B active parameters and targets scientific reasoning, information seeking, and tool-augmented problem solving. Its three-stage training combines roughly 18K SFT examples for reasoning and investigation with reinforcement learning over agent trajectories. Benchmarks include **HLE 52.8, BMB 35.3, SEAL-0 60.4, HiPhO 45.9, FrontierScience Olympiad 85.0, and FrontierScience Research 55.0**, closely tracking the paper’s reported results. It leads the comparison table on SEAL-0 and performs strongly on HiPhO and FrontierScience Olympiad, while trailing top models on HLE, BMB, and FrontierScience Research. Released in BF16, it supports vLLM serving through an OpenAI-compatible endpoint; tool-use evaluation requires a separate execution harness.
43
44
> [!NOTE]
45
> MTP — Speculative Decoding: No | Don't know which one to choose? Grab MXFP4_MOE (best for NVIDIA Blackwell) or Q4_K_M for balanced performance.
39
40
# **Axion-1.0-35B-GGUF**
41
42
+> Axion 1.0-35B is an Apache-2.0 reproduction checkpoint of ByteDance-linked Axion 1.0, following the recipe in *Axion 1.0: Open Agent Foundation Models, Simplified*. The 35B MoE model has approximately 3B active parameters and targets scientific reasoning, information seeking, and tool-augmented problem solving. Its three-stage training combines roughly 18K SFT examples for reasoning and investigation with reinforcement learning over agent trajectories. Benchmarks include HLE 52.8, BMB 35.3, SEAL-0 60.4, HiPhO 45.9, FrontierScience Olympiad 85.0, and FrontierScience Research 55.0, closely tracking the paper’s reported results. It leads the comparison table on SEAL-0 and performs strongly on HiPhO and FrontierScience Olympiad, while trailing top models on HLE, BMB, and FrontierScience Research. Released in BF16, it supports vLLM serving through an OpenAI-compatible endpoint; tool-use evaluation requires a separate execution harness.
43
44
> [!NOTE]
45
> MTP — Speculative Decoding: No | Don't know which one to choose? Grab MXFP4_MOE (best for NVIDIA Blackwell) or Q4_K_M for balanced performance.
