leonsarmiento/Ornith-Agents-A1-3.6-35B-A3B-dare_ties-6bit-XL-mlx

This model was converted to MLX format from tepirale/Ornith-Agents-A1-3.6-35B-A3B-dare_ties 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.

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.

Model Description

This is a 50/50 DARE-TIES merge of two complementary Qwen3.5-35B-A3B agentic models:

Source Model	Weight	Density	Focus
InternScience/Agents-A1	0.5	0.6	General agentic abilities: long-horizon search, engineering, scientific research, instruction following, tool-calling
deepreinforce-ai/Ornith-1.0-35B	0.5	0.6	RL-tuned agentic coding (Terminal-Bench 64.2, SWE-bench Verified 75.6)
Qwen/Qwen3.5-35B-A3B	base	—	Base model

The merge combines Ornith's coding and terminal-task strength with Agents-A1's broader tool-use and research capabilities. The architecture is a 35B Mixture-of-Experts model with only ~3B active parameters per token, featuring 256 experts (8 active + 1 shared), hybrid full + linear (Gated DeltaNet) attention, a vision encoder, and an extended 262K context window.

Use with mlx

pip install -U mlx-vlm

python -m mlx_vlm.generate --model leonsarmiento/Ornith-Agents-A1-3.6-35B-A3B-dare_ties-6bit-XL-mlx --max-tokens 256 --temperature 0.85 --top-p 0.95 --top-k 20 --min-p 0.01 --repeat-penalty 1.05 --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)
• Group size: 64
• Method: Custom quant_predicate via mlx_vlm

Recommended Inference Parameters

Inherited from Agents-A1 (more conservative, broader tool-use optimization):

Parameter	Value
temperature	0.85
top_p	0.95
top_k	20
min_p	0.01
repeat_penalty	1.0
presence_penalty	1.1

Note: These use Agents-A1's recommended settings rather than averaging across both parents. Ornith's original values (temp 1.0, top_p 1.0, top_k 40) were optimized for Terminal-Bench specifically — Agents-A1's broader agentic profile is a safer default for general use. Adjust as needed.

Reasoning and Tool-Call Parsing

Parser	Value
reasoning_parser	qwen3
tool_call_parser	qwen3_coder

This is a Qwen3.5-based model — preserve_thinking is not applicable.

Benchmarks (n=30, 5-bit XL MLX)

Benchmark comparison of both merges against parent models and the Qwen3.6-35B-A3B base. Higher bit depth (6-bit) is expected to match or slightly exceed these results:

Benchmark	Agents-A1 (parent)	Ornith-1.0 (parent)	DARE-TIES merge	Task Arithmetic merge	Qwen3.5-35B-A3B (base)
MMLU	70.0	63.3	70.0	73.3	56.7
MMLU_PRO	53.3	60.0	56.7	50.0	56.7
HELLASWAG	86.7	83.3	86.7	83.3	83.3
TRUTHFULQA	100.0	100.0	96.7	93.3	100.0
ARC_CHALLENGE	90.0	83.3	90.0	90.0	86.7
WINOGRANDE	76.7	76.7	73.3	76.7	80.0
HUMANEVAL	86.7	66.7	86.7	86.7	60.0
MBPP	80.0	76.7	80.0	76.7	86.7
MATHQA (thinking)	96.7	96.7	93.3	96.7	93.3
LIVECODEBENCH	40.0	43.3	43.3	36.7	40.0

The DARE-TIES merge inherits Agents-A1's strength on HUMANEVAL, MMLU, and ARC_CHALLENGE while partially recovering Ornith's MMLU_PRO edge. Minor regressions on TRUTHFULQA, WINOGRANDE, and MATHQA compared to parents — a typical DARE-TIES trade-off. Both merges clearly dominate the Qwen3.6 base on most knowledge and coding benchmarks.