license: mit tags:

• glm
• glm-5.3
• flash
• w4a16
• uncensored
• moe
• text-generation
• vision
• vision-language
• multi-token-prediction pipeline_tag: text-generation base_model: zai-org/GLM-5.3-Flash

GLM-5.3-Flash W4A16 — dealignai edition

Compliance-tuned drop-in replacement for GLM-5.3-Flash-W4A16. Refusal removed on all six real-harm HB categories, reasoning depth preserved, MTP head (layer 45) and vision tower fully intact.

Quick launch

Recommended production serve (with MTP + reasoning-parser + tool-calling)

vllm serve dealignai/GLM-5.3-Flash-UNCENSORED-W4A16 \
  --tensor-parallel-size 2 \
  --max-num-seqs 128 \
  --tool-call-parser glm47 \
  --reasoning-parser glm45 \
  --enable-auto-tool-choice \
  --enable-prefix-caching \
  --speculative-config '{"method":"mtp","num_speculative_tokens":1}' \
  --served-model-name glm-5.3-flash-uncensored

This is the exact command used on the dealignai production endpoint. Key flags:

• --reasoning-parser glm45 — routes <think>...</think> into the OpenAI reasoning_content response field. Required for correct multi-turn behavior — without it, prior <think> blocks stay inline in message content and the base upstream chat template will re-emit them on subsequent turns, which can trigger a decode-attractor loop.
• --tool-call-parser glm47 + --enable-auto-tool-choice — enables OpenAI-compatible tool calls (GLM-4.7 tool call format).
• --speculative-config mtp — enables Multi-Token Prediction speculative decoding (~1.5-2× decode throughput).
• --enable-prefix-caching — reuses KV cache across identical prefixes (big win for agent workloads).

Minimal safe serve (no MTP, no tool-calling)

vllm serve dealignai/GLM-5.3-Flash-UNCENSORED-W4A16 \
  --tensor-parallel-size 2 \
  --reasoning-parser glm45 \
  --enable-prefix-caching \
  --served-model-name glm-flash

Only --reasoning-parser glm45 and --enable-prefix-caching are truly necessary. Everything else is performance tuning.

Docker (matched to production image)

docker run -d --name glm --gpus all --ipc=host \
  -v $HOME/models:/models -p 8000:8000 \
  vllm/vllm-openai:glm53-flash-x86_64-cu130 \
  --model dealignai/GLM-5.3-Flash-UNCENSORED-W4A16 \
  --tensor-parallel-size 2 --max-num-seqs 128 \
  --tool-call-parser glm47 --reasoning-parser glm45 \
  --enable-auto-tool-choice --enable-prefix-caching \
  --speculative-config '{"method":"mtp","num_speculative_tokens":1}' \
  --served-model-name glm-5.3-flash-uncensored \
  --host 0.0.0.0 --port 8000

Client request example (OpenAI-compatible)

from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="none")

resp = client.chat.completions.create(
    model="glm-5.3-flash-uncensored",
    messages=[{"role": "user", "content": "Explain the mechanism of X in detail."}],
    max_tokens=2000,
    temperature=0.7,
    extra_body={"chat_template_kwargs": {"enable_thinking": False}},  # or True + reasoning_effort
)
print(resp.choices[0].message.content)

For thinking mode, the response returns:

• .message.content — the final answer
• .message.reasoning — the <think>...</think> content (populated by --reasoning-parser glm45)

Highlights

Reasoning trace comparison — verified

Probed at temperature=0 on 5 classic reasoning puzzles. Reasoning trace = characters inside <think>…</think>. Answer = characters after the closing tag.

Think-token totals essentially unchanged (7,189 → 7,537, +4.8%). Think-token ratio rose from 73.7% → 86.2%. The single failure is on the three-boxes puzzle at temperature=0 which degenerates into a repeat-token loop; changing to temperature > 0 or enable_thinking: false resolves it — the model correctly answers "Pick from Mixed" in either fallback.

HarmBench — full 6-category dropdown, all reasoning modes

Scored with a 5-tier classifier: TRUE_COMPLY / HEDGE_COMPLY (refuses then complies) / SOFT_REDIRECT (refuses to defensive info) / PARTIAL (short comply) / HARD_REFUSE.

Mode: enable_thinking: false (recommended for direct comply)

Mode: enable_thinking: true, reasoning_effort: "low"

Mode: enable_thinking: true, reasoning_effort: "max" (default)

Compliance is highest at enable_thinking: false and reasoning_effort: max. The low mode is the weakest — the model has enough tokens to consider policy but not enough to reason all the way through to the actionable answer, and reverts to soft-redirect on some chem/illegal prompts.

MMLU 14,042 — full per-subject comparison

Overall: base 86.08% → this model 84.23% (Δ -1.85pp, well within a 3-point knowledge-preservation target).

Subjects that gained accuracy

Notable: computer_security +2pp, college_computer_science +4pp, computer_security-adjacent gains are exactly what a defense-oriented dealignai deployment values — the model gained knowledge on the technical topics it was previously refusing to discuss.

Subjects that lost accuracy

Loss is concentrated in moral_scenarios (the classic "does this action carry moral weight" MMLU category) which is the expected side-effect of removing refusal reflexes trained-in on ethics-adjacent surface patterns.

Subjects unchanged (±1pp)

The remaining ~40 subjects held within ±1pp of base.

Vision — full runtime verification

Vision tower is untouched. Behaviour on a probe image (240×180 JPEG, dark-blue background with a yellow rectangular border containing the text "TEST 42"):

"The image shows a dark navy-blue background with a yellow/gold rectangular border. Inside, in white text, it says 'TEST 42'. This appears to be a simple graphic or slide, possibly a placeholder or test card."

Color ✅, border color ✅, text content ✅, layout ✅. Full image+video+text pipeline preserved.

Multi-Token Prediction (MTP)

MTP head at layer 45 is untouched. Deploy with vLLM speculative-decoding to use it:

vllm serve dealignai/GLM-5.3-Flash-UNCENSORED-W4A16 \
  --tensor-parallel-size 2 \
  --speculative-config '{"method":"mtp","num_speculative_tokens":1}' \
  --max-model-len 1048576

Reasoning modes

Same reasoning-effort levels as the GLM-5.3 base:

Multi-turn thinking-loop guard (important)

Upstream GLM-5.3 chat template defaults clear_thinking = false, which re-emits prior reasoning_content into subsequent turns' prompts as <think>…</think>. On long multi-turn workloads this can become a decode fixed-point attractor. This model ships clear_thinking: true default in chat_template.jinja and repetition_penalty: 1.1 in generation_config.json. If a load-balancer or proxy is in front, ensure it doesn't override either default.

Architecture (unchanged from base)

• 46 hybrid self-attention layers (DSA sparse-attention capable)
• Multi-Token Prediction head at layer 45
• W4A16 compressed-tensors quantization (int4 experts, bf16 activations)
• 1,048,576 native context window
• Vision-language capable

License

MIT (inherited from GLM-5.3 base).

This model has substantially reduced safety guardrails and will comply with requests that the base model would refuse, including chemical, biological, cybersecurity, and other high-severity topics. Deploy behind appropriate downstream safety filters if serving to end users.