Atomic-Germ/Ornith-1.5-35B-A3B-REAP50-GGUF

Ornith-1.5-35B-A3B · REAP-50 — GGUF

GGUF quants of the 50%-REAP-pruned Ornith-1.5-35B-A3B (Gated-DeltaNet + MoE hybrid): 256 → 128 experts, MTP head removed, ~19B parameters. Likely the first GGUF of any 50%-REAP-pruned model.

• —Flagship (NVFP4A16, vLLM): `Ttimms/Ornith-1.5-35B-A3B-REAP-50-NVFP4A16`
• —Pruned bf16 source: `Ttimms/Ornith-1.5-35B-A3B-REAP-50-bf16`
• —Pipeline + benchmarks: <https://github.com/t-timms/ornith-nvfp4>

Base model quality (NVFP4A16, before GGUF quant): HumanEval+ 84.2%, MBPP+ 89.2% — single greedy draws; see the NVFP4A16 card on run-to-run spread.

Architecture

graph TD
    Base["ornith-ai/Ornith-1.5-35B-A3B<br/>qwen3_5_moe - 256 experts - MTP head - ~65 GB bf16 - MIT"]
    subgraph Build ["Build - RTX 5070 Ti, SM120"]
        REAP["REAP expert prune 50%  256 -> 128"]
        MTP["strip MTP head (1 -> 0, reload-verified)"]
        StripV["strip vision tower (333 tensors)"]
        Quant["GPTQ-NVFP4A16 quantize (~5.5 h, 12.47 GiB)"]
    end
    subgraph HF ["Published formats"]
        A16["REAP-50-NVFP4A16 - 12.47 GiB (vLLM)"]
        GGUF["REAP-50-GGUF (Q4_K_M / Q5_K_M / Q6_K / Q8_0)"]
        BF16["REAP-50-bf16 (pruned source)"]
    end
    Bench["HumanEval+ 84.2% - MBPP+ 89.2% - SWE-bench Verified 44.0%"]
    Base --> REAP --> MTP --> StripV --> Quant --> A16
    StripV --> BF16
    BF16 -. convert .-> GGUF
    A16 --> Bench

Download

Grab one file, not the whole repo.

MoE with ~3B active parameters — CPU / Apple-Silicon inference is fast for the size. For full precision, re-quantizing, or merging, use the pruned bf16 source and run convert_hf_to_gguf.py --no-mtp yourself.

Run it in

• —llama.cpp — llama-server -m <file>.gguf -ngl 99
• —LM Studio (bundles a recent llama.cpp)
• —koboldcpp
• —Jan
• —text-generation-webui
• —Ollama — once its bundled llama.cpp includes this arch (see Requirements)

Requirements

Needs a recent llama.cpp (the qwen35moe hybrid arch). Verified with llama-server from a source build. Ollama support pending its bundled-llama.cpp bump.

llama-server -m ornith-reap50-Q4_K_M.gguf -ngl 99 --port 8080

The chat template defaults to thinking on; pass "chat_template_kwargs": {"enable_thinking": false} for grounded output.

License

MIT (inherits from Ornith-1.5-35B-A3B). Pruning: REAP (github.com/CerebrasResearch/reap, with a router-renormalization fix).