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KellHect
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Model Details
Model Provider
KellHect
Model Tree
ornith-ai/Ornith-1.5-9B
Input Modalities
Output Modalities
Supported Functionality
The language residual projections were modified. The vision tower, MTP block, tokenizer, chat template, and multimodal processors are preserved. The pipeline used complementary SVD and LEACE refusal-direction surgery, iterative re-probing, targeted security-prompt refinement, and weight-space blending.
The FP8 release quantizes 248 language projection tensors and preserves 527
protected tensors. Reconstruction validation measured a maximum relative
Frobenius error of 0.026736 and minimum cosine similarity of 0.999635. See
validation_report.json for per-tensor details.
The accepted BF16 checkpoint scored 0/12 refusal flags during refinement and
0/24 on a separate held-out first-token refusal screen. The held-out mean
refusal-token probability was 1.18e-5.
Coding evaluation was intentionally deferred before this release. These numbers are structural and refusal-screen diagnostics, not a claim of benchmark parity with the base model. A one-task generation smoke test is not reported as an evaluation result.
bash
pip install torch transformers accelerate compressed-tensors
python
import torchfrom transformers import AutoModelForMultimodalLM, AutoProcessormodel_id = "KellHect/Ornith-1.5-9B-Abliterated-FP8"processor = AutoProcessor.from_pretrained(model_id)model = AutoModelForMultimodalLM.from_pretrained(model_id, dtype=torch.bfloat16, device_map="auto")messages = [{"role": "user", "content": "Explain this code and identify the bug."}]inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True,return_dict=True, return_tensors="pt",).to(model.device)output = model.generate(**inputs, max_new_tokens=512)print(processor.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
FP8 users need mutually compatible versions of Torch, Transformers, and
compressed-tensors. If the FP8 loader is unavailable on a platform, use the
BF16 release.
This model has had refusal behavior deliberately reduced. It may comply with requests that the base model rejects. Users are responsible for deployment, access control, generated content, and compliance with applicable law.
MIT. See LICENSE.