license: apache-2.0 language:

en
tr tags:
qwen3.5
qwen3.6
moe
a3b
uncensored
abliterated
ninfer
rtx-5090
blackwell
mtp
long-context base_model:
huihui-ai/Huihui-Ornith-1.5-35B-A3B-abliterated
ornith-ai/Ornith-1.5-35B-A3B
Qwen/Qwen3.6-35B-A3B
z-lab/Qwen3.6-35B-A3B-DFlash

Huihui-Ornith-1.5-35B-A3B Uncensored — ready-to-run NInfer artifact

A single .ninfer file (~22.8 GB) that runs huihui-ai/Huihui-Ornith-1.5-35B-A3B-abliterated (uncensored 35B-A3B MoE) on one RTX 5090 (32 GB) at the model's full 262,144-token context, with 4-bit K8V4 KV cache and MTP speculative decoding, using the NInfer engine.

Full build guide + scripts: pcmaker/Huihui-Ornith-1.5-35B-A3B-Uncensored-NInfer-RTX5090-Guide.

Authorized use only. These are uncensored / abliterated weights: the model's built-in refusals are removed. Use only for red-teaming and security testing of your own systems, safety research, and offline experimentation. You are responsible for what you generate.

Measured on an RTX 5090

Full 262,144 context loads; ~7,412 tok/s prefill on a 184,910-token prompt.
Decode ~510 tok/s @185k, 600-850 tok/s on short prompts (MTP, ~76-83% accept).
Needle-in-haystack at 185k: passed. Model load ~5 s.

Quick start

# 1) build NInfer (Ubuntu 24.04, CUDA 13.3, RTX 5090) — see the guide repo
git clone --depth 1 https://github.com/Neroued/ninfer.git && cd ninfer
export CUDA_HOME=/usr/local/cuda-13.3 PATH="/usr/local/cuda-13.3/bin:$PATH"
cmake -S . -B build -G Ninja -DCMAKE_BUILD_TYPE=Release && cmake --build build -j 6
export LD_LIBRARY_PATH=/usr/local/cuda-13.3/lib64:$LD_LIBRARY_PATH

# 2) download this artifact
hf download pcmaker/Huihui-Ornith-1.5-35B-A3B-Uncensored-NInfer --local-dir ./art

# 3) serve (OpenAI/Anthropic-compatible on :8188)
./build/apps/ninfer-serve ./art/ornith_35b_a3b.ninfer \
  --host 0.0.0.0 --port 8188 --model-id ornith-35b \
  --max-context 262144 --kv-dtype k8v4 --spec mtp --draft-tokens 3 \
  --default-thinking-budget 8192

curl http://localhost:8188/v1/chat/completions -H 'Content-Type: application/json' \
  -d '{"model":"ornith-35b","messages":[{"role":"user","content":"Merhaba, kimsin?"}],
       "max_tokens":100,"temperature":0.6,"top_p":0.95,"top_k":20,"reasoning_effort":"none"}'

Notes

Recommended sampling: temperature=0.6, top_p=0.95, top_k=20 (general use).
Thinking is on by default; send {"reasoning_effort":"none"} to disable per request.
Speculative: use --spec mtp (self-aligned, best on this base). --spec dflash --draft-tokens 7 --lm-head-draft is possible but usually lower accept.
The artifact must be served on the same NInfer version it was built with. If you see tensor descriptor does not match target contract, rebuild from current master and re-convert (see the guide).

Credits

Ornith team, huihui-ai, the Qwen team, z-lab, and the NInfer author (Neroued). All weights and tools belong to their respective authors and licenses.

Huihui-Ornith-1.5-35B-A3B Uncensored — ready-to-run NInfer artifact

Authorized use only. These are uncensored / abliterated weights: the model's built-in refusals are removed. Use only for red-teaming and security testing of your own systems, safety research, and offline experimentation. You are responsible for what you generate.

Quick start

# 1) build NInfer (Ubuntu 24.04, CUDA 13.3, RTX 5090) — see the guide repo git clone --depth 1 https://github.com/Neroued/ninfer.git && cd ninfer export CUDA_HOME=/usr/local/cuda-13.3 PATH="/usr/local/cuda-13.3/bin:$PATH" cmake -S . -B build -G Ninja -DCMAKE_BUILD_TYPE=Release && cmake --build build -j 6 export LD_LIBRARY_PATH=/usr/local/cuda-13.3/lib64:$LD_LIBRARY_PATH # 2) download this artifact hf download pcmaker/Huihui-Ornith-1.5-35B-A3B-Uncensored-NInfer --local-dir ./art # 3) serve (OpenAI/Anthropic-compatible on :8188) ./build/apps/ninfer-serve ./art/ornith_35b_a3b.ninfer \ --host 0.0.0.0 --port 8188 --model-id ornith-35b \ --max-context 262144 --kv-dtype k8v4 --spec mtp --draft-tokens 3 \ --default-thinking-budget 8192

curl http://localhost:8188/v1/chat/completions -H 'Content-Type: application/json' \ -d '{"model":"ornith-35b","messages":[{"role":"user","content":"Merhaba, kimsin?"}], "max_tokens":100,"temperature":0.6,"top_p":0.95,"top_k":20,"reasoning_effort":"none"}'

Notes

Recommended sampling: temperature=0.6, top_p=0.95, top_k=20 (general use).

Thinking is on by default; send {"reasoning_effort":"none"} to disable per request.

Speculative: use --spec mtp (self-aligned, best on this base). --spec dflash --draft-tokens 7 --lm-head-draft is possible but usually lower accept.

The artifact must be served on the same NInfer version it was built with. If you see tensor descriptor does not match target contract, rebuild from current master and re-convert (see the guide).