RAS-LAB

GLM-4.7-Flash-24B-A3B-KREAP

Intended use

Research on Korean-aware MoE Expert pruning and retention–compression trade-offs
Comparative evaluation against the base model and matched-size pruning baselines
Offline experimentation with the validated Transformers and PyTorch/CUDA path on sufficiently provisioned hardware
Not intended for production deployment, safety-critical use, or claims beyond the reported evaluations

What this checkpoint shows

Method: KREAP force-fit ablation
Korean MC macro: 62.8 (base 68.8, -6.0 pp)
Overall non-Korean composite: 56.4 (base 65.5, -9.1 pp)
Coding 2-benchmark macro: 70.9 (base 76.0, -5.1 pp)
LiveCodeBench v6 pass@1: 20.6 (base 22.9, -2.3 pp)
Coding 3-benchmark macro: 54.1 (base 58.3, -4.1 pp)
Protection budget: One layer exceeded the 48-Expert budget; 6 protected Experts were force-dropped.

Benchmark results

Table with columns: benchmark, base, KREAP 12.5%, KREAP force-fit 25% (this checkpoint), KREAP force-fit 50%, KREAP-Code 25%
benchmark	base	KREAP 12.5%	KREAP force-fit 25% (this checkpoint)	KREAP force-fit 50%	KREAP-Code 25%
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

Table with columns: Runtime or feature, Status
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

Table with columns: Group, Records, Role
Group	Records	Role
Korean/English semantic pairs	2,752	Korean↔English routing contrast
Common Korean	2,752	Korean routing coverage
General replay	1,344	General-capability retention
GLM-specialized	1,344	Documents, language retention, reasoning, security, structured output, tool calling
Total	8,192	5,472 Korean / 2,720 English; 1,376 pairs

6,848 records contain source teacher responses.
1,344 GLM-specialized records use deterministic base-model rollouts.
Prompt/response observations cover 3,898,756 token memberships.
The manifest passed a separate internal token-length and exact-prompt contamination validator.
The exact calibration manifest and deterministic reconstruction script are not included in this model repository, so the full GLM experiment 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: One layer exceeded the 48-Expert budget; 6 protected Experts were force-dropped.

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 release-gate result: The project plan's 25% gate allows at most -1.5 pp Korean macro and -2.5 pp overall non-Korean composite. This checkpoint measured -6.0 pp and -9.1 pp and force-dropped six protected Experts, so it did not pass the internal release criteria.

Interpretation boundaries

At the same 48-Expert size, this checkpoint's Korean MC macro is 9.3 pp above KREAP-Code-48E, while its overall non-Korean composite is 4.0 pp lower, LiveCodeBench is 4.6 pp lower, and its coding 3-benchmark macro is 3.6 pp lower. This is a trade-off, not evidence that one checkpoint is universally better.
This is not a controlled single-variable ablation: KREAP-48E and KREAP-Code-48E differ in both calibration data and survivor-selection rules. A same-calibration REAP baseline and component ablations are still required to isolate hard protection.
Machine-readable results include direct KREAP-48E versus KREAP-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:

bash

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.

python

import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_id = "RAS-LAB/GLM-4.7-Flash-24B-A3B-KREAP"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
artifacts/protected-experts.json: exact KREAP protected set and evidence

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, REAP, and K-REAP when using this model:

bibtex

@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}} @software{chedrian07_2026_kreap,  author  = {Chedrian07},  title   = {K-REAP: Language-preserving one-shot MoE expert pruning},  year    = {2026},  url     = {https://github.com/Chedrian07/K-REAP},  version = {234666e0dde8714b0c61f36b67f6f9f053c02d07}}

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Model Details

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RAS-LAB

Model Tree

Base

zai-org/GLM-4.7-Flash

Fine-tuned

this model

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Intended use

Research on Korean-aware MoE Expert pruning and retention–compression trade-offs
Comparative evaluation against the base model and matched-size pruning baselines
Offline experimentation with the validated Transformers and PyTorch/CUDA path on sufficiently provisioned hardware
Not intended for production deployment, safety-critical use, or claims beyond the reported evaluations

What this checkpoint shows

Method: KREAP force-fit ablation
Korean MC macro: 62.8 (base 68.8, -6.0 pp)
Overall non-Korean composite: 56.4 (base 65.5, -9.1 pp)
Coding 2-benchmark macro: 70.9 (base 76.0, -5.1 pp)
LiveCodeBench v6 pass@1: 20.6 (base 22.9, -2.3 pp)
Coding 3-benchmark macro: 54.1 (base 58.3, -4.1 pp)
Protection budget: One layer exceeded the 48-Expert budget; 6 protected Experts were force-dropped.

Benchmark results

Table with columns: benchmark, base, KREAP 12.5%, KREAP force-fit 25% (this checkpoint), KREAP force-fit 50%, KREAP-Code 25%
benchmark	base	KREAP 12.5%	KREAP force-fit 25% (this checkpoint)	KREAP force-fit 50%	KREAP-Code 25%
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

Compatibility

Table with columns: Runtime or feature, Status
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

Table with columns: Group, Records, Role
Group	Records	Role
Korean/English semantic pairs	2,752	Korean↔English routing contrast
Common Korean	2,752	Korean routing coverage
General replay	1,344	General-capability retention
GLM-specialized	1,344	Documents, language retention, reasoning, security, structured output, tool calling
Total	8,192	5,472 Korean / 2,720 English; 1,376 pairs

6,848 records contain source teacher responses.
1,344 GLM-specialized records use deterministic base-model rollouts.
Prompt/response observations cover 3,898,756 token memberships.
The manifest passed a separate internal token-length and exact-prompt contamination validator.
The exact calibration manifest and deterministic reconstruction script are not included in this model repository, so the full GLM experiment 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: One layer exceeded the 48-Expert budget; 6 protected Experts were force-dropped.

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/.

Interpretation boundaries

At the same 48-Expert size, this checkpoint's Korean MC macro is 9.3 pp above KREAP-Code-48E, while its overall non-Korean composite is 4.0 pp lower, LiveCodeBench is 4.6 pp lower, and its coding 3-benchmark macro is 3.6 pp lower. This is a trade-off, not evidence that one checkpoint is universally better.
This is not a controlled single-variable ablation: KREAP-48E and KREAP-Code-48E differ in both calibration data and survivor-selection rules. A same-calibration REAP baseline and component ablations are still required to isolate hard protection.
Machine-readable results include direct KREAP-48E versus KREAP-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:

bash

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.

python

import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_id = "RAS-LAB/GLM-4.7-Flash-24B-A3B-KREAP"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))

import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_id = "RAS-LAB/GLM-4.7-Flash-24B-A3B-KREAP"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

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
artifacts/protected-experts.json: exact KREAP protected set and evidence

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, REAP, and K-REAP when using this model:

bibtex

@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}} @software{chedrian07_2026_kreap,  author  = {Chedrian07},  title   = {K-REAP: Language-preserving one-shot MoE expert pruning},  year    = {2026},  url     = {https://github.com/Chedrian07/K-REAP},  version = {234666e0dde8714b0c61f36b67f6f9f053c02d07}}

@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}} @software{chedrian07_2026_kreap,  author  = {Chedrian07},  title   = {K-REAP: Language-preserving one-shot MoE expert pruning},  year    = {2026},  url     = {https://github.com/Chedrian07/K-REAP},  version = {234666e0dde8714b0c61f36b67f6f9f053c02d07}}

GLM-4.7-Flash-24B-A3B-KREAP

README

Intended use

What this checkpoint shows

Benchmark results

Compatibility

How it was made

Source checkpoint

Calibration dataset

Observation and scoring

Pruning

Evaluation scope

Interpretation boundaries

Usage

Limitations

Included research artifacts

Provenance

Citation

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README

Intended use

What this checkpoint shows

Benchmark results

Compatibility

How it was made

Source checkpoint

Calibration dataset

Observation and scoring

Pruning

Evaluation scope

Interpretation boundaries

Usage

Limitations

Included research artifacts

Provenance

Citation