25% Experts Pruned, 36.0 HUMANEVAL (base 40.9)

OLMoE-1B-7B-0924-Instruct compacted via per-layer-normalized MoE expert pruning against the unmodified teacher.

HUMANEVAL: 36.0 (base 40.9, Δ -4.9)
HUMANEVAL+PLUS: 31.7 (base 36.6, Δ -4.9)

Every claim on this card is verified
Trust: self-attested · 2 benchmarks · 1 device tested
ForgeAlloy chain of custody · Download alloy · Merkle-chained

Cross-architecture validation artifact for §4.1.3.4. OLMoE-1B-7B (the smallest serious MoE on HF, fully-open Allen AI release) compacted from 7B to ~5B via calibration-aware MoE expert pruning on a held-out Python code corpus. Hardware-measured 36.0 HumanEval against unmodified base 40.9 (Δ −4.9, both Q5_K_M on the same 5090). The forge methodology that produced qwen3-coder-30b-a3b-compacted-19b-256k ports to a structurally distinct MoE family (OlmoeForCausalLM vs Qwen3MoeForCausalLM) without any modification to the forge scripts. The negative-baseline broad-corpus variant scored 28.0 — the +8.0 swing from changing only the calibration corpus is the lever §4.1.3.4 names. This is a methodology proof point, not a tier-leading artifact; OLMoE is general-purpose, not coder-specific, so HumanEval is not its strength. Use the qwen3-coder-30b-a3b artifact if you need a fits-12-GB code model.

Benchmarks

Benchmark	Score	Base	Δ	Verified
humaneval	36.0	40.9	-4.9	✅ Result hash
humaneval_plus	31.7	36.6	-4.9	✅ Result hash

What Changed (Base → Forged)

	Base	Forged	Delta
Pipeline		expert-activation-profile → expert-prune → quant → eval	1 cycles

Runs On

Device	Format	Size	Speed
NVIDIA GeForce RTX 5090	Q5_K_M	3.6GB	Verified
MacBook Pro 32GB	fp16	3.6GB	Expected
MacBook Air 16GB	Q8_0	~1.8GB	Expected
MacBook Air 8GB	Q4_K_M	~1.1GB	Expected
iPhone / Android	Q4_K_M	~1.1GB	Expected

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("continuum-ai/olmoe-1b-7b-compacted-5b",
    torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("continuum-ai/olmoe-1b-7b-compacted-5b")

inputs = tokenizer("def merge_sort(arr):", return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Methodology

Produced via MoE expert pruning, GGUF quantization. Full methodology, ablations, and per-stage rationale are in the methodology paper and the companion MODEL_METHODOLOGY.md in this repository. The pipeline ran as expert-activation-profile → expert-prune → quant → eval over 1 cycle on NVIDIA GeForce RTX 5090.

Limitations

HumanEval is not OLMoE's natural benchmark. OLMoE is general-purpose (Allen AI), not coder-specific. The 40.9 base / 36.0 student numbers are methodology validation, not tier-leading absolute quality. For a tier-leading code model, see qwen3-coder-30b-a3b-compacted-19b-256k.
Validates §4.1.3.4 cross-architecture; does NOT compete on absolute numbers. This is the second empirical anchor for the methodology paper, alongside the Qwen3-Coder-30B-A3B v1. Together they demonstrate that the activation-count importance metric is architecture-invariant across two structurally distinct MoE families.
Calibration corpus was 300 Python code examples. For non-code workloads (math/reasoning/general), the methodology will preserve OLMoE's general capability if profiled on a matching corpus — but that's a separate forge run.
Single GGUF tier shipped (Q5_K_M, 3.6 GB). Q4_K_M and Q8_0 will be added in v1.1 if there's demand.

Chain of Custody

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What	Proof
Model weights	`sha256:7f3b3c31279035cd5226f13cd602875ba...`
Forged on	NVIDIA GeForce RTX 5090, ?
Published	huggingface — 2026-04-08T16:36:55.037319+00:00
Trust level	`self-attested`
Spec	ForgeAlloy — Rust/Python/TypeScript

Make Your Own

Forged with Continuum — a distributed AI world that runs on your hardware.

The Factory configurator lets you design and forge custom models visually — context extension, pruning, LoRA, quantization, vision/audio modalities. Pick your target devices, the system figures out what fits.

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