---

base_model: occamy-ai/occamy-1.0

license: apache-2.0

pipeline_tag: text-generation

tags:

- gguf

- moe

- quantized

- antirez-recipe

---

antirez-recipe quants

MoE quants following the antirez recipe:

routed experts quantized hard, everything else protected.

| Tensor class | Quant | Notes |

| --- | --- | --- |

| blk..ffn_gate_exps, blk..ffn_up_exps (ffn_gate_up_exps if fused) | GU_TYPE (imatrix) | routed-expert gate/up |

| blk.*.ffn_down_exps | DOWN_TYPE (imatrix) | routed-expert down (K-quant for quality) |

| blk..ffn_{gate,up,down}_shexp, all attn_, output.weight | Q8_0 | shared experts / attention / head |

| token_embd.weight | F16 | embeddings |

| router (ffn_gate_inp), norms, sinks, biases | source precision (BF16/F32) | never quantized by llama.cpp |

Base type Q8_0 + --tensor-type overrides, imatrix calibration

(see SPEC per-model imatrix links). Runs on upstream llama.cpp — no fork or

patch needed.

Validation per file: gguf-dump tensor audit (experts = GU/DOWN, protection intact),

llama-cli smoke load, llama-perplexity spot ppl + --kl-divergence vs BF16 logits.

Files

| File | Size | SHA-256 |

| --- | ---: | --- |

| occamy-1.0-Q2-IQ2_XXS-Q2_K.gguf | 12052349184 | 923faddcac1f0d4268509f073a2ce0e01919b9183299ede48abc99c647167752 |

Imatrix: https://huggingface.co/mradermacher/occamy-1.0-i1-GGUF

Recipe

Antirez recipe on occamy-ai/occamy-1.0 (BF16 source: bartowski/occamy-ai_occamy-1.0-GGUF):

• ffn_gate_exps / ffn_up_exps -> IQ2_XXS
• ffn_down_exps -> Q2_K
• attention + shared experts + output -> Q8_0
• token embeddings -> F16
• imatrix: mradermacher/occamy-1.0-i1-GGUF

~11.4 GB, 2.78 BPW (66.1 GiB -> 11.5 GiB).

Tokenizer note: the bartowski BF16 carries tokenizer.ggml.pre = qwen35, which Accio's TOKENIZER.md documents as wrong for occamy; this build has qwen2 baked in at quantize time via --override-kv tokenizer.ggml.pre=str:qwen2 (no post-hoc copy).

Validation (llama.cpp, qwen35moe arch)

• 12-case hard-smoke eval (temp 0, ctx 15360, model's own embedded chat template): 7/12 PASS; ~27 t/s generation (AMD Vulkan/UMA); fails cases 2826, 10360, 10567, 9487, 1716. Passes both big-code cases.
• Exact output tokens (own tokenizer) over the 7 solved: 56,142 = 8,020 tok/solved.
• KL divergence vs a same-tokenizer Q8_0 reference built from the same BF16 with the same pre-tokenizer correction (ds4 short prompts, ctx 60, 2 chunks, CPU): per-chunk KL 0.150 / 0.141 (avg 0.146), delta-p RMS 10-12%, same-token top-1 96.6 / 91.4%.