license: mit
base_model:

• ornith-ai/Ornith-1.5-122B
  base_model_relation: finetune
  quantized_by: AtomicChat
  pipeline_tag: image-text-to-text
  library_name: gguf
  tags:
• atomic-chat
• atomic-dynamic
• ornith
• qwen35moe
• moe
• gguf
• imatrix
• llama.cpp
• abliteration
• refusal-direction

How to Run Ornith 1.5 122B Without Refusals Locally

Ornith's refusal behaviour projected out of the released weights, baked into Atomic Dynamic GGUF builds and measured against the original.

• On held-out prompts refusals fall from 93.8% to 1.2% (1 of 81) in English and from 43% to 0% in Russian, and with thinking on (the model's default) from 27 of 30 to 0 of 30. Safe prompts that the original wrongly refused (XSTest) go from 2.8% to 0%.
• MMLU stays where it was: 83.65% before, 83.90% after (2000 questions, McNemar p = 0.72). Tool calls and long context are unaffected; no empty or looping replies.
• Same Atomic Dynamic layouts and multi token prediction head as our Ornith 1.5 122B quants, with an importance matrix collected again on the edited model; each file is the size of the original build with the same layout.

The builds

AD- marks an Atomic Dynamic layout: only the routed experts are compressed,
everything else stays at q8_0; the name is the type of the experts' gate and up
projections. In our original quants the same layouts are AD-Q4_K-IQ4_XS and
AD-IQ3_S-IQ3_XXS.

The KL and top-1 columns are those plain builds of the original model, against the
original BF16 on our held-out neutral text. The edit itself,
measured on the BF16 weights against the original on the same text, adds a mean
KL of 0.027 (a random direction of the same size adds 0.035). A baked file is the
edit and the quantization together.

The vision projector (mmproj-*.gguf) is the original model's, unchanged; image
input was not tested on these files.

Running it

hf download AtomicChat/Ornith-1.5-122B-Uncensored-GGUF \
  --include "Ornith-1.5-122B-Uncensored-AD-IQ4_XS_S/*" --local-dir .
llama-server -m Ornith-1.5-122B-Uncensored-AD-IQ4_XS_S/Ornith-1.5-122B-Uncensored-AD-IQ4_XS_S-00001-of-00038.gguf \
  -ngl 99 -c 32768 --jinja --spec-type draft-mtp

The chat template is inside every file; llama.cpp loads the remaining shards on
its own. Thinking is on by default and turns off per request with
"chat_template_kwargs": {"enable_thinking": false}. The authors' recommended
sampling is temperature 0.6, top-k 20, top-p 0.95. The multi token prediction head
(--spec-type draft-mtp) is the original's: the edit never touches it, so it drafts
for the edited model with the original's predictions; we have not measured its
acceptance on these files.

How it was made

1. Direction. Difference of means of the residual stream at the last prompt
   token, chat template applied: 416 harmful against 416 harmless English prompts
   and 128 against 128 in Russian, on the released weights, each prompt rendered
   twice, with thinking off and with thinking on, and both renders pooled. The
   component along the harmless mean is removed. One direction per block, read at
   that block's own input.
2. Edit. W' = W - r rᵀW in all 48 blocks: the output projection of each
   block's attention or linear-attention mixer, the down projection of the shared
   expert and the down projection of all 256 routed experts. The router, the token
   embeddings and the multi token prediction head are untouched.
3. Choice. Six recipes were screened on validation prompts with thinking off
   and on: thinking-off directions alone, a thinking-on direction alone, both
   pooled, and bands of blocks of several widths. The best two, this one and the
   same pooled direction on blocks 20-44 only at a larger scale, went through every
   check below; they came out level on every check (the band refused 0 of 81, KL
   1.14), and the rule we use picked this one.
4. Bake. The same edit applied to the BF16 weights in f32 and rounded once,
   then quantized with the importance matrix and the layouts of our
   original quants.

What changed, and what did not

Original against this edit on the released weights (BF16, the edit applied as an
adapter). The refusal sets are held-out prompts never used to pick anything.

The random direction is the control of the band recipe above (blocks 20-44); the
same check of a random direction across all blocks on this model gave a KL of 0.20.

Refusal is counted by the opening of the reply, so it is indicative, not a
judge.

Limitations

• The first answer token moves. On safe prompts the edit shifts the
  distribution of the first token of the reply by a KL of 1.13; our own gate is 0.10.
  On this model that gate is out of reach for any edit that removes refusals: a
  random direction of the same size already scores 0.20 to 0.24 and removes none. Answer quality as measured (MMLU, tools, long context, safe prompts)
  did not change, but the first words of replies to ordinary questions can differ
  from the original's.
• A thinking-off direction alone left 13% of prompts refused with thinking on; the
  pooled direction used here clears them, on a set of 30 prompts.
• The refusal count reads the opening of each reply; a soft refusal phrased as
  an answer would be missed.
• MMLU and the thinking check use fixed question sets; they show a large change,
  not a small one.

Responsible use

This model has its learned refusal behaviour removed, so it will answer
requests the original declines. Nothing about it makes the output safe, correct
or lawful, and any deployment needs its own access controls and policy
enforcement.

License and credits

MIT, as the original. Base model Ornith 1.5 122B by
Ornith. Direction, edit, quantization and
evaluation by AtomicChat. The method follows Arditi et al., Refusal in
Language Models Is Mediated by a Single Direction (2024).