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Model Details
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maci0
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deepreinforce-ai/Ornith-1.0-9B
Input Modalities
Output Modalities
Supported Functionality
Near-lossless versus the bf16 source, 7.5 GB vs 18.8 GB bf16 (~40%), at wikitext-2 perplexity 8.02 and KL divergence 0.0416 to the original. GPTQ error compensation and an MSE observer keep the drop from bf16 minimal; the header lists the full characteristics and Quantization covers the recipe.
NVFP4 is auto-detected from config.json (compressed-tensors); no quantization flag
needed.
bash
vllm serve maci0/Ornith-1.0-9B-abliterated-NVFP4 \--served-model-name ornith-9b-abliterated-nvfp4 \--max-model-len 131072 \--gpu-memory-utilization 0.90 \--kv-cache-dtype fp8 \--reasoning-parser qwen3 \--enable-auto-tool-choice --tool-call-parser qwen3_coder
--language-model-only to skip the vision tower and free KV cache for text use.python
from openai import OpenAIclient = OpenAI(base_url="http://localhost:8000/v1", api_key="x")r = client.chat.completions.create(model="ornith-9b-abliterated-nvfp4",messages=[{"role": "user", "content": "Refactor this Python function to run in O(n) and explain the change."}],)print(r.choices[0].message.content)
bash
curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{"model": "ornith-9b-abliterated-nvfp4","messages": [{"role": "user", "content": "Refactor this Python function to run in O(n) and explain the change."}]}'
Ornith-1.0 is a self-improving family of open agentic-coding models from Deep Reinforce. The 9B-Dense member is a Qwen3.5-family vision-language model with thinking-mode reasoning and a 256K context.
max_position_embeddings 262144).Heretic runs a TPE-optimized search (200 trials) over
the refusal-ablation strength per model component, jointly minimizing refusal rate and
KL divergence from the original model, then merges the best trial. Because Ornith is a
thinking model, evaluation was run in non-thinking mode so each judged response is a real
answer rather than an unfinished <think> block; the refusal direction itself is computed
from the prompt's last-token residual and is unaffected by that choice.
mlabonne/harmless_alpaca (good) vs mlabonne/harmful_behaviors (bad).| Scheme | NVFP4, W4A4 |
| Weight rounding | GPTQ (Hessian-based error compensation), MSE observer |
| Weights | FP4 (E2M1), group_size=16, tensor_group, FP8 (E4M3) group scales, shared across fused layers |
| Activations | FP4, dynamic per-group, FP8 (E4M3) scales |
| Quantized | all language-model Linear layers |
| Kept in bf16 | vision tower (model.visual.*), lm_head |
GPTQ is a quantization-time cost only; inference speed and format are identical to plain round-to-nearest NVFP4, but it chooses better 4-bit values.
Calibration: 512 domain-matched samples (long reasoning + general chat + code),
max_seq_len=2048, text-only path through the VL model.
Thinking mode is the default.
temperature=0.6, top_p=0.95, top_k=20temperature=1.0, top_p=0.95, top_k=20temperature=0.7, top_p=0.80, top_k=20{%- set enable_thinking = false %} in the chat template, or
pass extra_body={"chat_template_kwargs": {"enable_thinking": false}}.Abliteration: heretic --model deepreinforce-ai/Ornith-1.0-9B (200 trials, export merge),
with the chat template's thinking default flipped off during the run for clean non-thinking
evaluation, then restored. Quantization: llmcompressor==0.12.0,
compressed-tensors==0.17.1, transformers==5.12.1, torch==2.11.0+cu130, on an NVIDIA
GB10 (Blackwell, sm_121); llm-compressor 0.12 shares the NVFP4 global scale across fused
layers automatically (q/k/v, gate/up).
--reasoning-parser and --tool-call-parser are not auto-detected; pass them explicitly.chat_template_kwargs.Apache-2.0, following the base model. Intended use and all responsibility for use follow the base model.
| Untouched | gated delta-net Conv1d and SSM params (A_log, dt_bias), never Linear |