Instructions to use buugiiiiii/GLM-4.7-Flash-REAP-Code-48E with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use buugiiiiii/GLM-4.7-Flash-REAP-Code-48E with Transformers:

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

pipe = pipeline("text-generation", model="buugiiiiii/GLM-4.7-Flash-REAP-Code-48E")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("buugiiiiii/GLM-4.7-Flash-REAP-Code-48E")
model = AutoModelForCausalLM.from_pretrained("buugiiiiii/GLM-4.7-Flash-REAP-Code-48E", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use buugiiiiii/GLM-4.7-Flash-REAP-Code-48E with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "buugiiiiii/GLM-4.7-Flash-REAP-Code-48E"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "buugiiiiii/GLM-4.7-Flash-REAP-Code-48E",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/buugiiiiii/GLM-4.7-Flash-REAP-Code-48E

SGLang

How to use buugiiiiii/GLM-4.7-Flash-REAP-Code-48E 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 "buugiiiiii/GLM-4.7-Flash-REAP-Code-48E" \
    --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": "buugiiiiii/GLM-4.7-Flash-REAP-Code-48E",
		"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 "buugiiiiii/GLM-4.7-Flash-REAP-Code-48E" \
        --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": "buugiiiiii/GLM-4.7-Flash-REAP-Code-48E",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use buugiiiiii/GLM-4.7-Flash-REAP-Code-48E with Docker Model Runner:
```
docker model run hf.co/buugiiiiii/GLM-4.7-Flash-REAP-Code-48E
```

GLM-4.7-Flash-REAP-Code-48E

Code-calibrated standard REAP one-shot MoE Expert pruning of zai-org/GLM-4.7-Flash. This checkpoint intentionally does not use Korean hard protection.

Routed Experts were pruned 64 → 48 per layer (25.0%) while top-4 routing was kept unchanged. This percentage is the reduction in routed Expert count, not total model parameters. The checkpoint contains 24.27B parameters (base 31.22B; 22.25% total-parameter reduction) and occupies 45.23 GiB (approximately 22.2% smaller than the base checkpoint), is stored in BF16/F32 safetensors, and received no fine-tuning.

Status	Result
Checkpoint integrity checks	PASS
Internal release gate	NOT APPLICABLE AS A K-GUARD CANDIDATE
External reproduction	NOT YET REPORTED
Deployment status	EXPERIMENTAL COMPARISON CONTROL

Comparison control. This checkpoint uses code-centric REAP without Korean hard protection. It is published to provide a comparison point, not as a Korean-preserving or deployment-recommended model.

What this checkpoint shows

Method: REAP-Code baseline
Korean MC macro: 53.5 (base 68.8, -15.3 pp)
Overall non-Korean composite: 60.4 (base 65.5, -5.1 pp)
Coding 2-benchmark macro: 74.0 (base 76.0, -2.0 pp)
LiveCodeBench v6 pass@1: 25.1 (base 22.9, +2.3 pp)
Coding 3-benchmark macro: 57.7 (base 58.3, -0.6 pp)
Protection budget: No Korean hard-protection constraint was applied.

Benchmark results

benchmark	base	K-Guard 12.5%	K-Guard force-fit 25%	K-Guard force-fit 50%	REAP-Code 25% (this checkpoint)
Korean (held-out harness)
KMMLU	49.4	48.8	42.4	23.6	35.0
KoBEST BoolQ	90.2	87.7	78.8	74.3	79.6
KoBEST COPA	70.7	69.7	65.0	55.9	51.7
KoBEST SentiNeg	74.8	71.8	70.5	53.7	49.6
KoBEST HellaSwag	58.8	55.0	57.4	48.8	51.6
English reasoning / knowledge
ARC-Challenge	87.1	86.1	79.9	36.9	79.1
HellaSwag (en)	74.2	67.4	55.3	32.6	61.7
WinoGrande	67.4	65.6	59.2	53.1	63.5
MMLU-Pro	41.2	36.9	34.2	14.8	33.8
TruthfulQA MC2	47.3	46.0	43.1	40.6	44.6
MMLU-en	69.7	64.5	56.6	30.1	59.6
Math
GSM8K	86.3	86.7	81.8	61.3	86.7
MathQA	30.5	29.1	24.8	21.3	28.7
Coding
HumanEval+	81.1	75.6	74.4	65.2	80.5
MBPP+	70.9	69.3	67.5	61.6	67.5
LiveCodeBench v6	22.9	25.1	20.6	16.6	25.1
Safety / bias
BBQ	76.6	69.9	59.8	33.6	74.0
SafetyBench	74.8	73.0	69.1	44.3	72.1
Macro
Korean MC macro	68.8	66.6	62.8	51.2	53.5
English reasoning / knowledge macro (6)	64.5	61.1	54.7	34.7	57.0
Math macro (2)	58.4	57.9	53.3	41.3	57.7
Safety / bias macro (2)	75.7	71.5	64.5	39.0	73.0
Overall non-Korean composite (10)	65.5	62.5	56.4	36.9	60.4
Coding 2-bench macro	76.0	72.5	70.9	63.4	74.0
Coding 3-bench macro	58.3	56.7	54.1	47.8	57.7

Aggregate rows are unweighted arithmetic means. The overall non-Korean composite averages six English reasoning/knowledge metrics, two math metrics, and two safety/bias metrics. The coding 3-benchmark macro averages HumanEval+, MBPP+, and LiveCodeBench v6 pass@1. These mixed summaries are compact internal comparisons, not standardized leaderboard scores.

Compatibility

Runtime or feature	Status
Transformers 5.0.0, PyTorch/CUDA, eager attention and eager Experts on DGX Spark	Validated
Native `from_pretrained(..., trust_remote_code=False)` loading	Validated
Speculative MTP/NextN decoding	Unsupported (`mtp_compatible=false`)
vLLM	Untested for this pruned checkpoint
SGLang	Untested for this pruned checkpoint

Architecture: Glm4MoeLiteForCausalLM.
Model code: native Transformers implementation; no repository-side modeling_*.py is required, and the checkpoint loads with trust_remote_code=False.
Preserved: first Dense layer, shared Experts, topk_method=noaux_tc, norm_topk_prob=true, router correction bias, and routed_scaling_factor=1.8.
Routing: top-4 remains unchanged; only routed Expert tensors and corresponding router rows/bias entries were compacted with one old→new ID map per layer.
The extra layer-47 MTP/NextN tensors are retained for checkpoint completeness, but runtime validation reports mtp_compatible=false. Use standard autoregressive generation; speculative MTP/NextN decoding is not supported by this release.
Top-4 routing is unchanged, so the checkpoint-size and resident-weight reduction must not be interpreted as a proportional reduction in active Expert compute or latency. Cold-load time, TTFT, prefill/decode throughput, batch scaling, power, and 4K/32K/128K serving behavior have not been benchmarked.

How it was made

Source checkpoint

Base: zai-org/GLM-4.7-Flash
Frozen revision: 7dd20894a642a0aa287e9827cb1a1f7f91386b67
Original routed MoE layout: first layer Dense, then 46 routed MoE layers with 64 Experts, top-4 routing, plus shared Experts.

Calibration dataset

Source	Records	Observed tokens	Role
`theblackcat102/evol-codealpaca-v1`	8,192	4,366,438	Code-centric standard REAP baseline

No Korean segment weighting, Korean↔English contrast, or hard-protected set was used.
The manifest passed a separate internal token-count, token-hash, and exact-prompt contamination validator.
The exact selected-sample manifest is not included in this model repository, so the complete run is not yet externally reproducible from public materials alone.

Observation and scoring

Selected-only observer: only routed Expert assignments were measured; no dense [experts, tokens, hidden] activation tensor was retained.
REAP contribution: normalized dispatch weight × L2 norm of the unweighted Expert output.
Router selection and dispatch were checked separately so GLM's correction bias affects selection without being mistaken for the actual dispatch weight.
The observed model remained logit-identical to the uninstrumented eager model in validation.

Pruning

Uniform routed Experts per layer: 64 → 48.
Shared Experts and the first Dense layer were not pruned.
Top-4 and routed scaling factor 1.8 were retained.
Safetensors surgery physically compacted Expert tensors, router rows, and correction-bias entries.
Protection result: No Korean hard-protection constraint was applied.

Evaluation scope

These are internal comparative evaluation results, not standardized leaderboard numbers:

KMMLU: deterministic 500-example subset (seed 42).
KoBEST: declared BoolQ, COPA, SentiNeg, and HellaSwag evaluation splits.
ARC-Challenge, HellaSwag-en, WinoGrande, MMLU-Pro, TruthfulQA MC2, MMLU-en, GSM8K, MathQA, BBQ, and SafetyBench: deterministic 512-example subsets.
HumanEval+: full 164 tasks; MBPP+: frozen full 378-task revision; greedy pass@1.
LiveCodeBench: full incremental v6 split (175 problems), n=1 greedy no-thinking generation, batch size 1, with official functional tests executed in an isolated ARM64 container. This protocol is not directly comparable to the official leaderboard's n=10 sampling protocol.
The machine-readable field decision_eligible=true means that the run passed the project's internal screening checks. It is not an external certification. release_eligible=false.
Do not compare these subset scores directly with full-set or few-shot leaderboard values.

Machine-readable results and the exact survivor manifest are included under evaluation/.

Internal classification: This is a code-centric REAP comparison control. K-Guard release criteria do not apply because Korean hard protection was intentionally omitted.

Interpretation boundaries

At the same 48-Expert size, K-Guard-48E has a 9.3 pp higher Korean MC macro, while this checkpoint has a 4.0 pp higher overall non-Korean composite, a 4.6 pp higher LiveCodeBench score, and a 3.6 pp higher coding 3-benchmark macro.
This is not a controlled single-variable comparison: the two checkpoints differ in both calibration data and survivor-selection rules. It cannot isolate the causal effect of hard protection.
Machine-readable results include direct K-Guard-48E versus REAP-Code-48E McNemar tests for 11 non-Korean binary/pass-fail tasks. Direct paired tests for the Korean benchmark set, aggregate bootstrap confidence intervals, multiple-comparison correction, and repeated calibration seeds are absent.
PASS in the artifacts refers to checkpoint integrity and internal pipeline validation, not external replication.

Usage

Install a Transformers release that includes Glm4MoeLiteForCausalLM:

pip install "transformers>=5.0.0" accelerate safetensors

The checkpoint was validated with Transformers 5.0.0 and NVIDIA PyTorch 25.09. It uses the native Transformers model implementation and does not execute custom code from this repository.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "buugiiiiii/GLM-4.7-Flash-REAP-Code-48E"
tokenizer = AutoTokenizer.from_pretrained(
    model_id,
    trust_remote_code=False,
)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=False,
    attn_implementation="eager",
    experts_implementation="eager",
)
model.eval()

messages = [{"role": "user", "content": "한국어로 이진 탐색을 설명해줘."}]
prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False,
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.inference_mode():
    output = model.generate(
        **inputs,
        max_new_tokens=256,
        do_sample=False,
        pad_token_id=tokenizer.eos_token_id,
    )

print(tokenizer.decode(output[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Limitations

Expert pruning is irreversible within this checkpoint and can change routing behavior sharply on tasks not represented by the calibration or evaluation sets. No post-pruning recovery or fine-tuning was applied. Reported Korean behavior is relative to the tested base checkpoint and harness; it does not imply universal Korean-language safety or quality preservation. The current release is not validated for speculative MTP/NextN decoding.

The Korean evaluation is primarily multiple-choice. Korean long-form generation, summarization, multi-turn dialogue, format-following, code explanation/debugging, factuality, hallucination, safety, tool calling, JSON output, and long-context retrieval were not comprehensively evaluated. Chinese capability is inherited from the base model but was not specifically protected or evaluated after pruning. vLLM/SGLang compatibility and deployment performance remain unverified.

Included research artifacts

evaluation/glm-full-b20-results-with-livecodebench.json: machine-readable five-model full results
evaluation/glm-full-b20-long-scores.csv: compact long-format B20 scores
evaluation/glm-full-b20-independent-validation.json: independent B20 validation report
evaluation/survivor-manifest.json: exact per-layer old Expert IDs retained
artifacts/calibration-validation.json: separate internal calibration-manifest validation
artifacts/observation-validation.json: selected-only observation validation
artifacts/expert-statistics.csv: per-Expert scoring/features used for this method

Provenance

Base model: zai-org/GLM-4.7-Flash, revision 7dd20894a642a0aa287e9827cb1a1f7f91386b67
Base license: MIT
General method code: K-REAP, commit 234666e
Reproducibility note: the linked repository documents K-REAP and its public adapters, but the exact GLM-specific observation pipeline, run configuration, and evaluation manifests used for this checkpoint are not yet published there.
Team: RAS-LAB
Benchmark report SHA256: 71ab2ce742bf8a481d90d10c8b8684a918241fd475729b0bf2afe56b44335dd6
Model card generated from an internally validated benchmark artifact with zero validation failures; this is not an external reproduction claim.

Citation

Please cite the base checkpoint and REAP when using this model:

@misc{zai2026glm47flash,
  title        = {GLM-4.7-Flash},
  author       = {{Z.ai}},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/zai-org/GLM-4.7-Flash}},
  note         = {Base revision 7dd20894a642a0aa287e9827cb1a1f7f91386b67}
}

@article{lasby2025reap,
  title   = {REAP the Experts: Why Pruning Prevails for One-Shot MoE Compression},
  author  = {Lasby, Mike and Lazarevich, Ivan and Sinnadurai, Nish and Lie, Sean and Ioannou, Yani and Thangarasa, Vithursan},
  journal = {arXiv preprint arXiv:2510.13999},
  year    = {2025},
  doi     = {10.48550/arXiv.2510.13999}
}

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Safetensors

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24B params

Tensor type

F32

BF16

Model tree for buugiiiiii/GLM-4.7-Flash-REAP-Code-48E

Base model

zai-org/GLM-4.7-Flash

Finetuned

(70)

this model

Paper for buugiiiiii/GLM-4.7-Flash-REAP-Code-48E

REAP the Experts: Why Pruning Prevails for One-Shot MoE compression

Paper • 2510.13999 • Published Oct 15, 2025 • 20