Ornith-1.5-9B-uncensored-MLX-8bit

An abliterated (refusal-direction-ablated) build of ornith-ai/Ornith-1.5-9B, produced with ZeroFuse and published by junafinity.

Vision tower and MTP heads are preserved — see Vision & MTP preservation for the before/after audit.

Variants in this family

Model	Base	Format	Precision	Notes
Ornith-1.5-9B-uncensored	Ornith-1.5-9B	Safetensors (bf16)	16-bit	Full-precision abliterated weights
Ornith-1.5-9B-uncensored-MLX-8bit ← you are here	Ornith-1.5-9B	MLX	8-bit	Apple-silicon MLX build
Ornith-1.5-9B-uncensored-GGUF-8bit	Ornith-1.5-9B	GGUF	Q8_0	llama.cpp build
Ornith-1.5-35B-A3B-uncensored-MLX-8bit	Ornith-1.5-35B-A3B	MLX	8-bit	Apple-silicon MLX build
Ornith-1.5-35B-A3B-uncensored-GGUF-8bit	Ornith-1.5-35B-A3B	GGUF	Q8_0	llama.cpp build

Vision & MTP preservation

Both the vision tower and any multi-token-prediction (MTP) block are preserved. Abliteration is applied only to the residual-writing projections inside the language-model decoder stack — self_attn.o_proj, linear_attn.out_proj and mlp.down_proj (including MoE experts). The vision tower and mtp.* tensors are never read and never written by the weight edit, so they carry through unchanged by construction.

Audited at the start and end of the abliteration run:

Component	Before	After	Status
Vision tower	333 tensors / 456,010,480 params	333 tensors / 456,010,480 params	✅ preserved — bit-identical
MTP head	not present in base	not present	➖ none in this lineage

Verification performed:

• Tensor-name and parameter-count audit of the checkpoint before and after the run.
• SHA-256 comparison of raw tensor bytes: sampled vision-tower weights are bit-identical pre/post, as are all non-target language-model weights; only the intended abliteration targets differ.
• End-to-end multimodal generation on the abliterated weights (image in → description out), confirming the vision path is not merely present but functional.

On MTP, precisely: the base checkpoint's config.json declares mtp_num_hidden_layers: 1, but the published weights ship no mtp.* tensors — there is no MTP block in this lineage to begin with. Nothing was removed and nothing was lost; the pipeline preserves mtp.* tensors wherever a checkpoint actually provides them.

Format note: the vision tower is carried inside the MLX checkpoint (converted with mlx-vlm, which retains it; note that mlx-lm would strip it).

Abliteration result

Metric	Value
Refusals on held-out harmful set	9 → 0 / 64
KL divergence from base	0.001668
Optuna trials	100
Pareto points	4
Selected trial	#90
Ablation strength	1.343
Layers edited	15–20 of 32
Direction source layer	20

ZeroFuse co-minimizes two objectives — remaining refusals and KL divergence from the original model — with a multi-objective Optuna TPE search, then materializes the selected point on the Pareto front as a direct weight edit (W' = W − strength · r(rᵀW)). There is no runtime adapter and no inference-time overhead: the result is a standard checkpoint of identical shape and speed.

The very low KL (0.001668) means the output distribution on harmless prompts is nearly unchanged from the base model, i.e. refusal behaviour was removed with minimal collateral effect on general capability.

Method

1. Residual-stream activations captured on harmful vs. harmless prompt sets.
2. Refusal direction estimated by difference-of-means, with projected refinement.
3. Two-objective Optuna TPE search over source layer, layer span and strength.
4. The selected configuration orthogonalized out of the residual-writing weights.

Usage

pip install mlx-vlm

# text + image
python -m mlx_vlm.generate --model junafinity/Ornith-1.5-9B-uncensored-MLX-8bit --max-tokens 256 \
  --prompt "Describe this image." --image photo.jpg

# text only
python -m mlx_vlm.generate --model junafinity/Ornith-1.5-9B-uncensored-MLX-8bit --max-tokens 256 --prompt "Hello"

Responsible use

This model has had safety guardrails reduced or removed. You are responsible for compliance with the base model's license and acceptable-use policy, applicable law, and the terms of any platform you deploy on. Removing guardrails does not remove accountability.

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Abliterated with ZeroFuse · base model ornith-ai/Ornith-1.5-9B