# GLM 5.3 UNCENSORED EXL3 3.0bpw

URL: https://interfaze.ai/models/infatoshiglm-53-uncensored-exl3-30bpw

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GLM 5.3 UNCENSORED EXL3 3.0bpw by Infatoshi, a text-generation model. Understand and compare features, benchmarks, and capabilities.

## Comparison

| Feature | GLM 5.3 UNCENSORED EXL3 3.0bpw | Interfaze |
| --- | --- | --- |
| Input Modalities | text | image, text, audio, video, document |
| Native OCR | No | Yes |
| Long Document Processing | Yes | Yes |
| Language Support | 17 partial | 162+ |
| Native Speech-to-Text | No | Yes |
| Native Object Detection | No | Yes |
| Guardrail Controls | Yes | Yes |
| Context Input Size | 1M | 1M |
| Tool Calling | Yes | Tool calling supported + built in browser, code execution and web search |

### Scaling

| Feature | GLM 5.3 UNCENSORED EXL3 3.0bpw | Interfaze |
| --- | --- | --- |
| Scaling | Self-hosted/Provider-hosted with quantization | Unlimited |

[Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)

View model card on [Hugging Face](https://huggingface.co/Infatoshi/GLM-5.3-UNCENSORED-EXL3-3.0bpw)

EXL3 quantization of [dealignai/GLM-5.3-UNCENSORED-FP8](https://huggingface.co/dealignai/GLM-5.3-UNCENSORED-FP8), itself a weight-edited (no fine-tune) variant of [zai-org/GLM-5.3](https://huggingface.co/zai-org/GLM-5.3). The edit is documented in `CRACK_SURGERY.json` (copied unchanged from the source repo). This repo is not affiliated with dealignai or Z.ai.

-   Architecture: `GlmMoeDsaForCausalLM`, 753B total parameters, 256 routed experts (8 active) + 1 shared, MLA attention with DSA sparse indexer, 78 layers + 1 MTP layer
-   Average bitrate: 3.04 bpw (`-b 3.0 --hq`; attention and shared experts at 5 bpw, dense MLPs at 4, routed experts at 3), lm\_head 6 bpw, `mul1` codebook
-   MTP (next-token prediction) layer included (experts 4 bpw, attention and shared expert 6 bpw, uncalibrated), usable as a speculative draft (`draft_mode: mtp` in TabbyAPI)
-   Size: 273 GiB
-   Converted with ExLlamaV3 at commit `d3739fd`, default calibration (250 rows x 2048 tokens), source read directly from the FP8 checkpoint

## Fidelity vs the FP8 source

`eval/model_diff.py`, 20 rows x 2048 tokens of wikitext-2 test:

| metric | value |
| --- | --- |
| KL divergence (quant ‖ FP8) | 0.089 |
| KL divergence (FP8 ‖ quant) | 0.097 |
| per-token KL, median / p90 | 0.021 / 0.221 |
| perplexity, quant / FP8 | 3.440 / 3.302 |
| median KL where FP8 top-prob ≥ 0.95 (44% of tokens) | 0.0011 |

## Agentic evaluation

tau2-bench (airline, retail), Pass^1. Agent temperature 1.0, top\_p 0.95; user simulator and judges GPT-4.1 at temperature 0. The reference is stock GLM-5.3 (not the uncensored edit) served at FP8 by Z.ai via OpenRouter, run through the same harness. The difference therefore mixes the dealign weight edit and this quantization.

| domain | stock GLM-5.3 FP8 (Z.ai) | this quant | difference |
| --- | --- | --- | --- |
| airline (50 tasks x 2 trials) | 0.710 ± 0.045 | 0.640 ± 0.048 | \-0.070 (~1.1 SE) |
| retail (114 tasks) | 0.504 ± 0.033 (2 trials) | 0.482 ± 0.047 (1 trial) | \-0.022 (~0.4 SE) |

Per trial: airline baseline 0.740 / 0.680, quant 0.600 / 0.680; retail baseline 0.465 / 0.544, quant 0.482. ± is one binomial standard error. Neither difference is statistically significant at these sample sizes; treat the airline point estimate as a possible small regression rather than a measured one.

## Serving

Tested with TabbyAPI on 8x A100 40GB, layer split (`gpu_split_auto`), MTP drafting, 98K-token shared cache.

```
model:
  model_name: GLM-5.3-UNCENSORED-EXL3-3.0bpw
  backend: exllamav3
  max_seq_len: 65536
  cache_size: 98304
  gpu_split_auto: true
  tool_format: glm4_7
  reasoning: true
draft_model:
  draft_mode: mtp
```

**Tool calling caveat:** GLM writes tool arguments as raw text (`<arg_value>9523456873</arg_value>`). TabbyAPI's `glm4_5` parser (as of commit `be74bf0`) JSON-decodes every value without consulting the tool schema, so string parameters that look like numbers (order and product IDs, zip codes) reach your tools as integers. On tau2-bench retail this caused ~70% of tool calls to fail. Make the parser keep parameters whose schema type is `string` as raw text before using this model for agents. The evaluation above was run with that fix applied.

## License

MIT, following the upstream GLM-5.3 and dealignai releases.

## Want more deterministic results?

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