Instructions to use litigerking/Qwen3.8-27B-OrcaRouter-NInfer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NInfer
How to use litigerking/Qwen3.8-27B-OrcaRouter-NInfer with NInfer:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Qwen3.8-27B OrcaRouter โ NInfer artifact
This repository provides a converted NInfer artifact for the OrcaRouter Qwen3.8-27B Uncensored checkpoint. It is intended for inference with the NInfer engine and is not a Transformers checkpoint.
Artifact
- File:
qwen3_8_27b_orcarouter-v2.ninfer - Size: approximately 18.2 GB
- Architecture: Qwen3.8-27B / Qwen3.5 multimodal architecture
- Context length: up to 262,144 tokens
- Quantization: NInfer groupwise integer recipe (
Q4G64_F16S,Q5G64_F16S,Q6G64_F16S, andW8G32_F16Scomponents) - MTP tensors: preserved for speculative decoding
- Conversion device: NVIDIA A40, CUDA
Running with NInfer
git clone https://github.com/tctiger/ninfer-A40.git
cd ninfer-A40
ninfer-serve /models/qwen3_8_27b_orcarouter-v2.ninfer \
--host 0.0.0.0 \
--port 8080 \
--max-context 262144 \
--kv-dtype bf16 \
--spec mtp \
--draft-tokens 3 \
--lm-head-draft
The server exposes an OpenAI-compatible API. Exact memory use and throughput depend on GPU, KV-cache type, batch size, prompt length, and sampling settings.
Provenance and license
This artifact is derived from the upstream OrcaRouter/Qwen3.8-27B-Uncensored checkpoint. Please review and comply with the upstream model license and usage requirements. The NInfer conversion does not change the upstream model's intended attribution or restrictions.
Limitations
This file can only be loaded by an NInfer build that supports the Qwen3.8
artifact format. It cannot be loaded directly with transformers or
llama.cpp. Validate outputs and tool-use behavior in your own environment
before production deployment.
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