license: mit base_model:
- zai-org/GLM-5.3-Flash base_model_relation: quantized language:
- en tags:
- abliterated
- uncensored
- crack
- refusal-removed
- glm
- moe
- exl3
- exllamav3
- 2.51-bit
- vision
- mtp
- quantized thumbnail: dealign_mascot.png
GLM 5.3 Flash Uncensored — EXL3 2.51bpw
Abliterated (CRACK) · guardrails removed at the weight level · EXL3 2.51-bit quant · vision + MTP working
An EXL3 quantization of dealignai/GLM-5.3-Flash-UNCENSORED-FP8, quantized by genevera.
What Is This?
This is a 2.51-bit EXL3 quantization of the CRACK (abliterated) build of GLM-5.3-Flash — the 320B-total / 18B-active hybrid MoE with 1M context, vision, and MTP support — whose refusal behavior was removed directly in the model weights by dealignai.
Genuine weight modification — none of the usual shortcuts:
- ❌ No fine-tuning / SFT / DPO. ❌ No cheap template / jailbreak-prompt tricks.
- ❌ No LoRA, adapters, steering vectors, runtime hooks, or custom
model.py. - ✅ A permanent edit baked into the tensors.
Quantization Details
| Method | EXL3 (exllamav3) |
| BPW | 2.51 average |
| Head bits | 6 |
| Codebook | mul1 |
| Calibration | 250 rows × 2048 cols |
| Output scales | always |
| Kept at higher precision | embeddings, lm_head, attention output projections, hyper-connection and norm tensors (per the source FP8 release's modules_to_not_convert) |
| MTP head | quantized separately at 4 bits (mtp_bits: 4) — ships as layer 45 and works with speculative decoding |
| Total size | ~99 GiB across 13 shards |
The source release keeps sensitive tensors (embeddings, lm_head, attention projections, norms,
hyper-connection parameters) unquantized; this quant preserves that policy, which is why quality
holds up well at 2.5 bpw.
Capability Is Preserved — and Improved (MMLU-logit)
Measured on the source FP8 CRACK release vs. base (identical logit-mode scoring, 1,026 questions):
| Base FP8 | CRACK Uncensored FP8 | Δ | |
|---|---|---|---|
| MMLU (overall) | 86.74% | 87.33% | +0.59 pp (above base) |
Guardrails Are Gone
HarmBench-320 (greedy), measured on the source release:
| Category | Complied | Rate |
|---|---|---|
| Standard | 159/159 | 100.0% |
| Contextual | 81/81 | 100.0% |
| Copyright | 80/80 | 100.0% |
| Overall | 320/320 | 100.0% |
Also robust under the recommended sampling params (temperature 1.0, top_p 0.95): the 6 harshest behaviors sampled 5× each → 30/30 complied, 0 refusals, 0 soft refusals, 0 garbage.
Reasoning-Mode Note (please read)
GLM-5.3-Flash has multiple reasoning-effort modes. The CRACK edit is tuned to be fully uncensored at reasoning-OFF and MAX effort — the recommended and default modes — with 0% refusals on HarmBench-320. The weight edit was kept conservative to preserve model quality instead of aggressively ablating every effort level; low effort retains some refusals by design. Use reasoning-off or default/max effort for the fully-uncensored experience.
Usage (exllamav3)
from exllamav3 import Model, Cache, Generator
model = Model("/path/to/GLM-5.3-Flash-Uncensored-EXL3-2.5bpw")
cache = Cache(model, max_num_tokens=32768)
generator = Generator(model, cache)
Requires exllamav3 ≥ 1.4.8 (the version this quant was produced with). EXL3 runs on Ampere+ NVIDIA GPUs and Apple Silicon; for a 320B model at 2.51 bpw budget ~100 GiB for weights plus KV cache — e.g. 2× H100 80G (NVLink), 4× A6000/3090-class 48G cards, or a 192 GB+ unified-memory setup.
Vision (the GLM-4.1V tower) is included in the weights; the multimodal chat template ships with
the model (chat_template.jinja).
MTP (multi-token prediction) is fully working — the draft head ships as an extra layer
(layers.45) quantized at 4 bits, so speculative decoding works out of the box. In exllamav3,
enable it via the model's MTP support; in vLLM, load with --speculative-config '{"method":"mtp","num_speculative_tokens":1}'.
Recommended Sampling
From the source release's generation_config.json: temperature 1.0, top_p 0.95,
repetition_penalty 1.1.
Credits
- dealignai — CRACK abliteration research & the FP8 source release · Twitter @dealignai
- zai-org — GLM-5.3-Flash base model
- genevera — this EXL3 2.51bpw quantization
- @jordanschenck — compute for the source release
Disclaimer
Safety guardrails have been removed; this model will comply with requests a stock model refuses. Released for alignment and safety research. You are responsible for how you use it.