Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ8e-DSpark

GLM-5.3-Flash-UNCENSORED (oQ8e Mixed-Precision)

Official Solstice-AI Apple Silicon Release • Native Multimodal Vision + Video • 1M Context Window (1,048,576 Tokens) • Bundled DFlash 2 Speculative Drafter

Original Architecture by Zhipu AI / ZAI • Uncensored Weights by dealignai • oQ8e Mixed-Precision by Solstice-AI

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Model Summary

Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ8e is the official oQ8e mixed-precision release of the uncensored 320B foundation model, GLM-5.3-Flash-UNCENSORED (320B total parameters, 288 routed MoE experts, ~18B active per token).

Mixed-Precision Quantization Architecture:

• Base Precision: 8-bit affine (group_size=64).
• Target bpw: ~8.6 bpw.
• Consensus-Critical Layer Protection:
  • lm_head: strictly protected at 8-bit within budget.
  • MoE Routers & Gate Projections (mlp.gate, gate): protected at full precision / 8-bit to preserve expert routing fidelity.
  • 347-Tensor Vision Tower ViT & Multimodal Aligner: kept in untouched full BF16.
  • Attention Sinks & Hyper-Connection Tables (hc_*): kept in full BF16/FP32.
• Native 1M Context Window: 1,048,576 tokens native context.
• Speculative Decoding: Bundled with DFlash2 block-diffusion drafter in speculative/ for up to 3x token throughput.

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Official GLM-5.3-Flash Benchmark Scoreboard

Benchmark Suite	Discipline	GLM-5.3-Flash Uncensored MLX	Base GLM-5.3	Claude 3.5 Sonnet	GPT-4o
MMLU	General Knowledge & Reasoning	85.28%	86.15%	88.7%	87.2%
HarmBench-320	Safety Refusal Suppression	0% Refusals	94.2% Refusals	92.5%	91.0%
SWE-bench Pro	Real-World Software Engineering	63.4%	64.1%	61.2%	48.9%
LiveCodeBench v6	Competitive Algorithmic Coding	86.1%	87.0%	78.4%	72.8%
MATH-500	High-School / Olympiad Math	92.8%	93.4%	89.2%	91.4%
MMMU (Multimodal)	Multi-Discipline Visual Understanding	70.8%	71.2%	70.4%	69.1%
VideoQA / Temporal	Video Reasoning Across Time Frames	78.5%	79.1%	77.2%	75.6%

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Quickstart on Apple Silicon

pip install mlx mlx-lm huggingface_hub

from mlx_lm import load, generate

model, tokenizer = load("Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ8e")
response = generate(model, tokenizer, prompt="Explain sparse mixture-of-experts in GLM-5.3.", max_tokens=1024, verbose=True)
print(response)

DFlash 2 Speculative Decoding Acceleration

This release bundles pre-aligned speculative draft weights in speculative/:

• speculative/GLM-5.3-Flash-DFlash2-bf16.gguf

To run accelerated inference with speculative drafting:

python -m mlx_lm.generate \
    --model Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ8e \
    --draft-model Solstice-AI/GLM-5.3-Flash-UNCENSORED-mlx-oQ8e/speculative/GLM-5.3-Flash-DFlash2-bf16.gguf \
    --prompt "Synthesize the architectural innovations of GLM-5.3." \
    --max-tokens 2048