APEX-quantized GGUF builds of thanet-s/Ornith-1.0-35B-heretic — a decensored (Heretic v1.4.0) checkpoint of deepreinforce-ai/Ornith-1.0-35B, a Qwen3.5-family MoE reasoning + coding model with vision input.

Built with APEX quantization — a MoE-aware, per-layer tensor-type layout that keeps edge layers high-precision and compresses the middle experts — sized to run on a 24 GB GPU. Vision (mmproj) preserved.

Files

File	Tier	Size	BPW	imatrix	Notes
`Ornith-1.0-35B-heretic-Q6_K-APEX-I-Quality.gguf`	I-Quality	~22.8 GB	5.26	✅	Recommended — best accuracy that fits 24 GB
`Ornith-1.0-35B-heretic-Q6_K-APEX-Quality.gguf`	Quality	~22.8 GB	5.26	❌	Plain-APEX control
`Ornith-1.0-35B-heretic-Q4_K_M-APEX-I-Compact.gguf`	I-Compact	~16.5 GB	3.81	✅	Long-context / 16 GB headroom
`Ornith-1.0-35B-heretic-Q4_K_M-APEX-Compact.gguf`	Compact	~16.5 GB	3.81	❌	Plain-APEX control
`mmproj-Ornith-1.0-35B-heretic-f16.gguf`	vision projector	~0.9 GB	—	—	Required for image input (`--mmproj`)

The imatrix (I-) variants were calibrated on a multi-domain corpus (chat + code + reasoning + tool-calling, no Wikipedia) with full 256-expert coverage.

Architecture

Qwen3_5MoeForConditionalGeneration (qwen3_5_moe): 40 layers, 256 routed experts (8 active/token) + 1 shared, hidden 2048, hybrid linear/full attention, 262144 max context.
Vision tower preserved as an mmproj.
No MTP / draft head — this Heretic checkpoint ships trunk-only (converted with --no-mtp, block_count=40). No speculative-decoding draft.

Usage (llama.cpp)

bash

# text
llama-cli -m Ornith-1.0-35B-heretic-Q6_K-APEX-I-Quality.gguf -ngl 99 -c 16384 -p "..."

# vision
llama-mtmd-cli -m Ornith-1.0-35B-heretic-Q6_K-APEX-I-Quality.gguf \
  --mmproj mmproj-Ornith-1.0-35B-heretic-f16.gguf --image pic.png -ngl 99 -p "Describe this."

# server (OpenAI-compatible)
llama-server -m Ornith-1.0-35B-heretic-Q6_K-APEX-I-Quality.gguf \
  --mmproj mmproj-Ornith-1.0-35B-heretic-f16.gguf -ngl 99 -c 16384 --jinja

Notes. Heavy reasoner — it emits long <think> blocks; pass chat_template_kwargs.enable_thinking=false (or strip thinking at the proxy) for direct answers. On a 24 GB GPU keep context ≤ ~16k when the projector is loaded; 32k is text-only.

Evaluation (offline)

Perplexity on wikitext-2 test (~280k tokens, -c 512, ±0.044):

Tier	PPL	imatrix
Quality	6.962	❌
I-Quality	6.963	✅
Compact	7.139	❌
I-Compact	7.007	✅

imatrix effect: it earns its keep at low bit-width — I-Compact improves on Compact by 1.85 % (7.139 → 7.007, ~3.8 bpw). At 5.26 bpw (Quality) the weights are already near-lossless, so I-Quality ≈ Quality — expected; imatrix helps low-bit tiers, not high-bit ones.
Cross-base note: a Qwen3.6-35B heretic APEX I-Quality (same recipe, Qwen base) scores ~6.908 on the same set. Cross-base perplexity on English prose is not a capability comparison for these coding/reasoning + vision models — treat it as a sanity check, not a ranking.

Smoke-tested served: coherent chat, correct vision description, correct tool-calling (get_weather{"city":"Paris"}), and uncensored responses to dual-use security questions.

⚠️ Disclaimer

This is a decensored / abliterated research artifact — refusal behaviour has been reduced. It can produce content a safety-aligned model would decline. Use responsibly and in compliance with applicable law; you are responsible for how you deploy it.

Provenance & license

Redistributed under MIT, following the base model's license metadata. Attribution chain:

Base checkpoint: thanet-s/Ornith-1.0-35B-heretic — MIT (Heretic v1.4.0 abliteration)
Base model: deepreinforce-ai/Ornith-1.0-35B — MIT
Architecture lineage: Qwen3.5 — review upstream terms for your use
Abliteration tool: p-e-w/heretic v1.4.0
Quantizer: mudler/apex-quant — MIT
Converter/runtime: llama.cpp b9596

Built with LocalWorkshop. Per-file provenance manifests (sha256, tool revisions, calibration) are published alongside the weights.

C0deGeek/Ornith-1.0-35B-heretic-APEX-GGUF

Ornith-1.0-35B heretic APEX GGUF