This model was converted to MLX format from deepreinforce-ai/Ornith-1.0-35B using BaseQuant_XL 5/8-bit mixed quantization optimized for Apple Silicon. The vision encoder is preserved and quantized at 5-bit, making this a full multimodal model.
BaseQuant_XL keeps the most routing-critical layers in full bf16 precision — the MoE router gate, shared expert gate, shared expert, and lm_head — while applying aggressive quantization to the bulk parameters. This preserves routing accuracy and output quality where it matters most.
Ornith-1.0-35B is a 35B-parameter MoE (Mixture of Experts) model fine-tuned from Qwen3.5-35B-A3B by DeepReinforce AI, using a self-improving RL training framework that jointly optimizes scaffold and solution rollouts for agentic coding tasks. Despite 35B total parameters, only ~3B are activated per token. It features 256 experts (8 active per token + 1 shared expert), hybrid full + linear (Gated DeltaNet) attention, and a vision encoder.
About XL Quantization
BaseQuant_XL is a fully data-agnostic, static quantization. No calibration dataset, no sensitivity analysis, no importance matrix. Precision is allocated purely by architectural role — routing-critical layers get higher precision, bulk expert parameters get lower precision. The result is a transparent, faithful capture of the source model.
Data-dependent calibration quantizations (iMatrix, AWQ, GPTQ, oQ, oQ4e, etc.) use a calibration set to guide bit allocation. This can produce a skewed representation of the model: domains well-represented in the calibration data (English, popular topics, public or leaked benchmarks) are preserved better, while underrepresented domains (non-English languages, niche use cases, your own data) are preserved worse. XL avoids this trade-off entirely — it generalizes honestly because it is never fit to any particular data distribution.
Note: Ornith-1.0-35B uses Temp 1.0 and Top_p 1.0 per the model's Terminal-Bench 2.1 benchmark recipe. This is a Qwen3.5-based model — preserve_thinking is not applicable.
