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Trust: self-attested · 2 benchmarks · 1 device tested
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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.
1from transformers import AutoModelForCausalLM, AutoTokenizer
2
3model = AutoModelForCausalLM.from_pretrained("continuum-ai/olmoe-1b-7b-compacted-5b",
4 torch_dtype="auto", device_map="auto")
5tokenizer = AutoTokenizer.from_pretrained("continuum-ai/olmoe-1b-7b-compacted-5b")
6
7inputs = tokenizer("def merge_sort(arr):", return_tensors="pt").to(model.device)
8output = model.generate(**inputs, max_new_tokens=200)
9print(tokenizer.decode(output[0], skip_special_tokens=True))
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.
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.
