DeepReinforce's Ornith 1.0 Drops Uncensored GGUF Builds for Local Coding
A new batch of GGUF quantizations makes an uncensored, abliterated Ornith 1.0 9B coding model runnable on an 8GB laptop through llama.cpp or Ollama.
- mradermacher released static GGUF quantizations of 3MPER0RR's abliterated Ornith 1.0 9B.
- Q4_K_M weighs 5.7GB and runs on an 8GB laptop via llama.cpp or Ollama.
- Base model Ornith 1.0 9B scores 69.4 on SWE-Bench Verified and 43.1 on Terminal-Bench 2.1.
- Ornith uses RL that jointly optimizes reasoning scaffolds and solution rollouts.
- Abliteration removes refusal direction via weight orthogonalization, no fine-tuning.
- Full quant ladder from Q2_K (3.9GB) to f16 (18GB), MIT licensed.
Community packager mradermacher has published GGUF quantizations of 3MPER0RR’s checkpoint, a refusal-suppressed version of DeepReinforce’s Ornith 1.0 9B coding model. The files make the checkpoint directly usable with llama.cpp, Ollama, LM Studio, and other GGUF-compatible runtimes.
The Q4_K_M file occupies about 5.7GB, substantially reducing the storage and memory required for local deployment. Total memory use will be higher because the runtime also needs space for the context window, KV cache, compute buffers, and application overhead.
Three stages, one downloadable artifact
The published files sit at the end of a three-stage model chain:
- Base model: Ornith 1.0 9B, an agentic coding model from DeepReinforce.
- Edited checkpoint: 3MPER0RR’s version, which applies directional ablation to reduce refusal behavior.
- GGUF conversion: mradermacher’s offline quantizations for local inference runtimes.
Directional ablation estimates an activation-space vector associated with refusals, then projects selected weight matrices away from that direction. The operation changes model weights without gradient-based retraining and may affect behavior beyond refusals because internal representations are not perfectly isolated.
The conversion adds no new training or benchmark results. Reported scores for the original model therefore cannot establish the coding quality of the ablated or quantized versions.
Pick the quant by total memory
The repository provides several precision levels. Lower-bit files reduce memory use and often improve inference speed, while higher-bit files usually retain more of the source checkpoint’s output distribution.
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