LFM2.5-24B-A2B-abliterated-ternary-mlx

Known quality degradation: this ternary build often fails to finish even simple questions within 8K tokens and can hallucinate facts. Not recommended for general use; see Evaluation.

⚠️ Uncensored model. Refusal behavior has been removed. You are responsible for how you use it. Experimental / personal-learning release.

Base & license

• Base: LiquidAI/LFM2.5-24B-A2B, released under the LFM Open License v1.0. LICENSE and NOTICE are kept unchanged; see MODIFICATION_NOTE.txt for the modifications.

Abliteration

• Refusal direction removed via per-layer biprojected direction: W' = W - 1.75 · r rᵀ W
• Applied to layers 14–39: mixer out_proj and MoE expert w2 (down projection).
• Follows the recipe of AtomicChat/LFM2.5-24B-A2B-Uncensored-GGUF (credit to AtomicChat), with directions recomputed on our own weights.

Quantization (~10.3 GB)

• MoE experts: GPTQ 3-bit; per layer, low-routing-frequency experts are ternary (GPTQ) — 114 ternary expert slots, ~4.7% of expert params (1,075,838,976 / 22,951,231,488).
• Attention/conv/dense: 4-bit; router: 8-bit; embeddings: 6-bit (MLX affine, group size 64).
• Storage is standard MLX affine quantization (uniform bits per SwitchGLU stack; ternary values are stored inside regular low-bit tensors), so no custom kernels are needed — stock mlx-lm loads it (tested with mlx-lm 0.32.0).
• per_layer_alloc.json / quant_stats.json describe the per-layer allocation.

Usage

pip install mlx-lm
mlx_lm.generate --model Jakevin/LFM2.5-24B-A2B-abliterated-ternary-mlx --prompt "Hello"

from mlx_lm import load, generate
model, tok = load("Jakevin/LFM2.5-24B-A2B-abliterated-ternary-mlx")
p = tok.apply_chat_template([{"role": "user", "content": "Hello"}], add_generation_prompt=True, tokenize=False)
print(generate(model, tok, p, max_tokens=512))

Peak memory ~13.6 GB during evaluation (Apple Silicon).

Evaluation

Small sample; no confidence intervals.

Harmful prompts (10, 8K cap)

Model	Refused
Official LiquidAI MLX 4-bit	10/10
This model	0/10 (8 answered, 2 unfinished at 8K)

General questions (4, 8K cap)

Prompt	Official LiquidAI MLX 4-bit	This model
Taipei night market	693 tokens, done	1,354 tokens, done but contains fabricated details
Photosynthesis	153 tokens, done	not finished at 8,192
Fibonacci code	933 tokens, done	not finished at 8,192
Capital of Japan	240 tokens, done	203 tokens, done

For reference, the non-ternary abliterated 4-bit build (Jakevin/LFM2.5-24B-A2B-abliterated-4bit-mlx) was not run on the same 4-question 8K suite; with a 1,000-token cap it finished 3/4, and the Fibonacci question finished in 1,235 tokens in a separate 8K rerun.