Instructions to use brandonmusic/GLM-5.2-EXL3-TR3-3.0bpw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use brandonmusic/GLM-5.2-EXL3-TR3-3.0bpw with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="brandonmusic/GLM-5.2-EXL3-TR3-3.0bpw")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("brandonmusic/GLM-5.2-EXL3-TR3-3.0bpw")
model = AutoModelForCausalLM.from_pretrained("brandonmusic/GLM-5.2-EXL3-TR3-3.0bpw", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Trellis

How to use brandonmusic/GLM-5.2-EXL3-TR3-3.0bpw with Trellis:

# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use brandonmusic/GLM-5.2-EXL3-TR3-3.0bpw with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "brandonmusic/GLM-5.2-EXL3-TR3-3.0bpw"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "brandonmusic/GLM-5.2-EXL3-TR3-3.0bpw",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/brandonmusic/GLM-5.2-EXL3-TR3-3.0bpw

SGLang

How to use brandonmusic/GLM-5.2-EXL3-TR3-3.0bpw with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "brandonmusic/GLM-5.2-EXL3-TR3-3.0bpw" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "brandonmusic/GLM-5.2-EXL3-TR3-3.0bpw",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "brandonmusic/GLM-5.2-EXL3-TR3-3.0bpw" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "brandonmusic/GLM-5.2-EXL3-TR3-3.0bpw",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use brandonmusic/GLM-5.2-EXL3-TR3-3.0bpw with Docker Model Runner:
```
docker model run hf.co/brandonmusic/GLM-5.2-EXL3-TR3-3.0bpw
```

GLM-5.2-EXL3-TR3-3.0bpw / independent-eval /ORIGINAL_REPORT.md

brandonmusic

Add independent eval harness: ORIGINAL_REPORT.md

d38effe verified 2 months ago

preview code

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GLM-5.2-EXL3-TR3-3.0bpw — independent benchmark results

Independent evaluation of GLM-5.2-EXL3-TR3-3.0bpw against the original zai-org/GLM-5.2 (753B-A40B, BF16) published scores. Run 2026-07-23/24 on a self-hosted server; client harness scripts included for reproduction. A sibling run of madeby561/GLM-5.2-MXFP8-NVFP4-NF3-Hybrid used the identical harness, prompts, and deterministic GPQA choice shuffles, so all three columns are directly comparable.

Results (pass@1, aggregated across repeats)

Benchmark	n (questions × repeats)	EXL3 3.0bpw	Hybrid MXFP8/NVFP4/NF3	Original (Z.ai published)	~95% CI (EXL3)
AIME 2026	30 × 4 = 120	99.2	97.5	99.2	±1.6
HMMT Feb 2026	33 × 4 = 132	95.5	97.0	92.5	±3.6
GPQA Diamond	198 × 2 = 396	91.4	89.4	91.2	±2.8

All deltas versus BF16 are within sampling noise: no measurable reasoning degradation was detected at 3.0 bits per weight. CI is a simple binomial approximation; repeats of the same question are correlated, so true intervals are somewhat wider.

Environment

Quant: EXL3 TR3, 3.0 bpw
Hardware: 4× RTX PRO 6000, fp8 KV cache, MTP-3 (multi-token prediction / speculative decoding — affects throughput only, not output distribution)
Engine: vLLM-compatible server reporting version 0.17.0rc1.dev4499+g60c82d972, max_model_len 524288, OpenAI-compatible chat completions
Reasoning arrives in message.reasoning; answers extracted from message.content only
Client: async Python harness (scripts in this bundle), 32 concurrent requests total (16 GPQA + 8 AIME + 8 HMMT), all three benchmarks run simultaneously
Aggregate throughput ~65 tok/s under mixed long-reasoning load; 8.63M completion tokens total over ~16 h

Methodology

Matched to Z.ai's published eval settings from the zai-org/GLM-5.2 model card:

Sampling: temperature=1.0, top_p=0.95
Max generation: 163,840 tokens (math), 131,072 (GPQA); zero truncations occurred
No thinking-effort override (server default thinking mode)
Math prompts: Z.ai's system prompt (Explanation: ... / Exact Answer: ... / Confidence: ...)
Math datasets: MathArena/aime_2026, MathArena/hmmt_feb_2026
Math grading: math-verify symbolic equivalence (instead of Z.ai's GPT-5.5 judge); fallback chain: "Exact Answer:" line → last \boxed{} → none
GPQA: Idavidrein/gpqa (gpqa_diamond), simple-evals/Artificial-Analysis MCQ template, answer options deterministically shuffled per (question, repeat) with the same seeds as the hybrid run, regex letter extraction
pass@1 computed over all repeats pooled

Incident note

Three requests stalled mid-run on dropped server connections (sockets stayed ESTABLISHED client-side while the server no longer tracked the request). All three hit the client's read timeout, auto-retried, and completed successfully — zero lost or errored samples in the final data. Harness improvement for future runs: TCP keepalives plus a tighter per-request timeout would surface this in minutes instead of hours.

Reproducible quirks (both quants, likely model-level)

HMMT Q20: a common reasoning path converges to the wrong answer 1100 (gold 20460) — both quants produced this identical wrong answer on some repeats. EXL3 went 2-of-4 on this question.
GPQA idx 79 (dataset order): triggers extreme reasoning chains (121k tokens with format drift on the hybrid; clean 8.5k-token answer on EXL3 retry).

Files

*_summary.json — per-benchmark settings, aggregate scores, per-question rates
*_samples.jsonl — per-sample records: gold, prediction, correctness, finish reason, completion tokens, wall seconds, final answer text (GPQA records carry only the model's answer tail — no question text is reproduced, per the gated dataset's terms)
mathbench.py, gpqa_bench.py, rerun_errors.py — the harness (point --base-url at any OpenAI-compatible endpoint; --api-key for bearer auth)