Flight Simulator — Coded by This Model

A real-world coding benchmark: each model was prompted to write a complete flight simulator from scratch. The resulting code was rendered and recorded.

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.

Local SOTA for 48GB Macs — Intelligence Benchmark Comparison

This model is part of a benchmark comparison of the best local MLX-quantized LLMs that fit in 48GB unified memory on Apple Silicon. All benchmarks run in instruct mode (no thinking) with n=50 samples per benchmark.

Benchmark	Samples	Agents-A1 6bit-XL	Gemma-4 26B 6bit-XL	Huihui-Qwen3.6 6bit-XL	Ornith-35B 6bit-XL	Qwen3.6-27B oQ4e	Qwen3.6-35B 6bit-XL	Qwen3.6-35B oQ4e	Qwen3.6-35B oQ4e-XL	Qwen3.6-35B oQ6
MMLU	50/14042	66%	76%	74%	64%	74%	64%	66%	72%	64%
MMLU_PRO	50/12032	58%	82%	66%	66%	56%	64%	60%	64%	60%
ARC_CHALLENGE	50/1172	90%	90%	92%	92%	88%	90%	92%	92%	90%
HUMANEVAL	50/164	90%	98%	84%	78%	92%	78%	92%	90%	66%
MBPP	50/500	70%	82%	78%	78%	86%	78%	80%	76%	76%
Average		74.8%	85.6%	78.8%	75.6%	79.2%	74.8%	78.0%	78.8%	71.2%

Collection: Local SOTA for 48GB Macs

⚠️ n=50 sampling means wide confidence intervals (±~13% at 95% CI). Differences under ~6 points may not be statistically significant. Models using data-aware quantization (oQ/oQe) may be calibrated on benchmark-like data — their scores carry a benchmaxxing caveat. The BaseQuant_XL variants (data-agnostic) provide the most honest generalization estimates.

leonsarmiento/Ornith-1.0-35B-6bit-XL-mlx

This model was converted to MLX format from deepreinforce-ai/Ornith-1.0-35B using BaseQuant_XL 6/8-bit mixed quantization optimized for Apple Silicon. The vision encoder is preserved and quantized at 6-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.

Benchmark Highlights

Benchmark	Ornith-1.0-35B	Qwen3.5-35B	Qwen3.6-35B
Terminal-Bench 2.1 (Terminus-2)	64.2	41.4	52.5
Terminal-Bench 2.1 (Claude Code)	62.8	38.9	49.2
SWE-bench Verified	75.6	70	73.4
SWE-bench Pro	50.4	44.6	49.5
SWE-bench Multilingual	69.3	60.3	67.2
NL2Repo	34.6	20.5	29.4
Claw-eval Avg	69.8	65.4	68.7

Use with mlx

pip install -U mlx-vlm

python -m mlx_vlm.generate --model leonsarmiento/Ornith-1.0-35B-6bit-XL-mlx --max-tokens 256 --temperature 1.0 --top-p 1.0 --prompt "Hello"

BaseQuant_XL Quantization Strategy

Bit Depth	Layers	Rationale
bf16 (unquantized)	mlp.gate (router), shared_expert_gate, lm_head, shared_expert	Routing decisions and shared computation path — errors here are qualitatively different from precision loss
8-bit	embed_tokens, self_attn (full attention), linear_attn (DeltaNet)	Every-token layers with moderate sensitivity — 8-bit is near-lossless
6-bit	vision_tower, switch_mlp (routed experts)	Bulk of parameters, only 8 of 256 experts active per token — natural redundancy tolerates lower precision

Quantization Details

Layer	Bits	Group Size
mlp.gate (router)	bf16	—
shared_expert_gate	bf16	—
lm_head	bf16	—
shared_expert	bf16	—
embed_tokens	8	64
self_attn (full attention)	8	64
linear_attn (DeltaNet)	8	64
vision_tower	6	64
switch_mlp (routed experts)	6	64
Default fallback	8	64

• Quantization type: BaseQuant_XL mixed (multimodal, vision preserved)
• Bits per weight: 6.808
• Total size: ~28 GB (6 shards)
• Group size: 64
• Method: Custom quant_predicate via mlx_vlm

Recommended Inference Parameters

Parameter	Value
temperature	1.0
top_p	1.0
top_k	40
min_p	0.01
repeat_penalty	1.0

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.