Qwen3.8-Flash-Next-Uncensored-oQ6e-mtp

Qwen3.8-Flash-Next-Uncensored-oQ6e-mtp

An oMLX-native oQ6e quantization of orcarouter/Qwen3.8-Flash-Next-Uncensored, the BF16 abliterated build of Qwen3.8-Flash-Next. Targets a 128 GB Apple Silicon Mac and preserves the vision tower, the MTP head, and the checkpoint's native 262,144-token architecture setting.

For the abliteration method, refusal-direction details, and MTP-consistency notes, see the source model card linked above. This repository only documents the quantization.

Companion build: LMLiutenant/Qwen3.8-Flash-Next-Uncensored-oQ5e-mtp

Nature of this model

This is an uncensored (abliterated) model: the source weights have had their refusal direction removed, so it will attempt most requests without the safety refusals present in the official Qwen release. It has no added guardrails. You are responsible for how you use it and for complying with the license and applicable law. Do not expose it to untrusted input in an agentic setup without your own safeguards; with no refusal behaviour, it will not push back on injected instructions.

Quantization

The oQ6e weights were quantized directly from the abliterated BF16 source. The sensitivity model used to guide mixed-precision allocation was this project's own oQ5e build, for matched abliterated lineage and higher proxy fidelity than a 4-bit sensitivity model. Nominal group size was 64; oQ mixed-precision may use different effective settings for selected tensors.

Hardware and memory

Qwen3.8-Flash-Next contains a very large N-gram/PLE component. With SSD N-gram Offload it does not all stay resident in unified memory, which is what makes a model this size practical on a 128 GB machine. Long-context and KV-cache use raise memory further with context length.

Tested on a MacBook Pro, Apple M4 Max, 128 GB, oMLX, with SSD N-gram Offload enabled. Peak MLX allocation (weights + KV + activations), measured in oMLX with SSD N-gram Offload on: ~102 GB at 4K context, ~106 GB at 128K. About 14 GB above the oQ5e build (the extra weight bits). These are process-level allocator peaks, not total-system.

Recommended oMLX settings

For a 128 GB Apple Silicon Mac:

• SSD N-gram Offload: ON
• Lightning MTP: ON for chat/reasoning; consider OFF for coding agents

Serve via oMLX's OpenAI-compatible API. The weights are standard mlx-lm compatible safetensors and should also load in mlx-lm and other MLX apps (untested).

Note: an unrelated oMLX engine bug on 0.7.0.dev1/dev2 can silently drop tool calls to unregistered function names on the streaming path (jundot/omlx#3660, open as of 2026-09; 0.6.4 unaffected). It does not affect the weights.

Benchmarks

Practical local tests, limited by compute time, not a standardized academic suite, performed on oMLX 0.7.0.dev2.

Thinking OFF

¹ jedisct1/Qwen3.8-Flash-Next-Uncensored-oQ4e-100K-MTP (abliterated lineage) ² GBP-DE/Qwen3.8-Flash-Next-oQ5e-mtp (base, non-abliterated)

At 2000 samples MMLU is flat across all four builds (~88%), within sampling noise: the abliterated quants are at parity with the base build on broad knowledge. On MMLU-Pro, accuracy rises with bit width across the oQ4e/oQ5e/oQ6e series.

Smaller (n=100) GSM8K and HumanEval runs were near ceiling (90-98%) for every build and are omitted as non-discriminating.

Model architecture

Quantized from Qwen3.8-Flash-Next (sparse Mixture-of-Experts): ~125B LM parameters, ~6B activated, ~51B N-gram embedding parameters, ~4B MTP parameters, 48 layers, 512 experts, 10 routed + 1 shared activated. See the official Qwen model card for full architecture and context details.

Credits

• Base (abliterated) model: orcarouter/Qwen3.8-Flash-Next-Uncensored, itself an abliteration of Qwen/Qwen3.8-Flash-Next.
• Quantization: oMLX 0.6.4, oQ5e enhanced/imatrix.
• Sensitivity model: LMLiutenant/Qwen3.8-Flash-Next-Uncensored-oQ5e-mtp.

License

Qwen Community License 1.0, inherited from the source repository and included as LICENSE. Note the source card labels itself Apache-2.0, which does not match the license file it ships; review before use or redistribution.