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MODEL_METHODOLOGY.md

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+---
+tags:
+- 1b
+- 1b-active
+- 5b
+- 7b
+- allenai
+- android
+- apple-silicon
+- attested
+- calibration-aware-pruning
+- chain-of-custody
+- chinese
+- consumer-gpu
+- cryptographically-verified
+- edge-inference
+- embedded
+- english
+- expert-pruning
+- forge-alloy
+- fully-open
+- general
+- general-purpose
+- ggml
+- gguf
+- iphone
+- llama-cpp
+- lm-studio
+- local-inference
+- macbook
+- mixture-of-experts
+- mlx
+- mobile
+- moe
+- multilingual
+- ollama
+- olmoe
+- on-device
+- q5-k-m
+- q5_k_m
+- quantized
+- raspberry-pi
+- reproducible
+- sparse-moe
+- text-generation
+- versatile
+base_model: allenai/OLMoE-1B-7B-0924-Instruct
+pipeline_tag: text-generation
+license: apache-2.0
+---
+
+# 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)
+
+
+<p align="center">
+<a href="https://cambriantech.github.io/forge-alloy/verify/#bba0a92ff0c8bebb">
+<img src="alloy-qr.png" alt="Verify Chain of Custody" width="160"/>
+</a>
+</p>
+
+<p align="center">
+<a href="https://cambriantech.github.io/forge-alloy/verify/#bba0a92ff0c8bebb"><b>Every claim on this card is verified</b></a><br>
+<b>Trust: self-attested</b> · 2 benchmarks · 1 device tested<br>
+<a href="https://github.com/CambrianTech/forge-alloy">ForgeAlloy</a> chain of custody · <a href="olmoe-1b-7b-compacted-5b.alloy.json">Download alloy</a> · Merkle-chained
+</p>
+
+---
+
+## About this model
+
+Cross-architecture validation artifact for the §4.1.3.4 calibration-aware expert importance methodology. OLMoE-1B-7B-0924-Instruct (the smallest serious MoE on HuggingFace, fully-open Allen AI release) compacted from 64 experts per layer to 48 via per-layer-normalized activation-count importance ranking on a held-out Python code calibration corpus. Hardware-measured 36.0 HumanEval / 31.7 HumanEval+ vs the unmodified base's 40.9 / 36.6 — within −4.9 / −4.9 of the base anchor. The negative-baseline broad-corpus variant scored 28.0 / 26.2 (Δ −12.9 / −10.4); the +8.0 / +5.5 swing from changing only the calibration corpus is the second empirical anchor for §4.1.3.4 (the first was Qwen3-Coder-30B-A3B with a +9.7 swing). Two architectures (`Qwen3MoeForCausalLM` and `OlmoeForCausalLM`) now empirically validate the cross-architecture invariance claim: the metric is architecture-invariant, the calibration-corpus alignment is the lever.
+
+
+## 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
+
+```python
+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))
+```
+
+
+## How It Was Made
+
+```
+expert-activation-profile → expert-prune → quant → eval (1 cycles)
+```
+
+- **expert-activation-profile**
+  > Same script unchanged from the Qwen3-Coder-30B-A3B forge — first cross-architecture validation that the activation-count importance metric ports across MoE families. The hooks register on `model.layers.{L}.mlp.gate` for both Qwen3MoE and OlmoeForCausalLM (same module path).
+- **Expert pruning**: 0% of MoE experts removed pre-load
+  > Same script unchanged. Identical regex layout (unfused per-expert tensors at `model.layers.{L}.mlp.experts.{K}.{gate,up,down}_proj.weight`). Cross-arch portability confirmed: OlmoeForCausalLM and Qwen3MoeForCausalLM share the same prunable-unit module structure, so the script works without modification.
+- **quant**
+- **Calibrated evaluation**: anchored against `OLMoE-1B-7B-0924-Instruct` (published None, measured 40.9, ±3.0pt tolerance)
+  > Self-anchor calibration. HumanEval is not OLMoE's natural benchmark — OLMoE is general-purpose, not coder-specific. The 40.9 base / 36.0 student numbers are methodology validation, not tier-leading absolute quality. The artifact's value is the structural finding (cross-architecture portability + +8.0 swing from calibration alignment), not the absolute number.
+- **Hardware**: NVIDIA GeForce RTX 5090
+- **Forge tool**: [Continuum](https://github.com/CambrianTech/continuum) Factory + [sentinel-ai](https://github.com/CambrianTech/sentinel-ai)
+## 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`](https://huggingface.co/continuum-ai/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
+
+Scan the QR or [verify online](https://cambriantech.github.io/forge-alloy/verify/#bba0a92ff0c8bebb). Download the [alloy file](olmoe-1b-7b-compacted-5b.alloy.json) to verify independently.
+
+| What | Proof |
+|------|-------|
+| Forged on | NVIDIA GeForce RTX 5090, ? |
+| Published | [huggingface](https://huggingface.co/continuum-ai/olmoe-1b-7b-compacted-5b) — 2026-04-08T16:36:55.037319+00:00 |
+| Trust level | [`self-attested`](https://github.com/CambrianTech/forge-alloy/blob/main/docs/ATTESTATION.md) |
+| Spec | [ForgeAlloy](https://github.com/CambrianTech/forge-alloy) — Rust/Python/TypeScript |
+
+## Make Your Own
+
+Forged with [Continuum](https://github.com/CambrianTech/continuum) — a distributed AI world that runs on your hardware.
+
+<p align="center">
+<a href="https://github.com/CambrianTech/continuum"><img src="https://raw.githubusercontent.com/CambrianTech/continuum/main/docs/images/factory.png" alt="Continuum Model Factory" width="400"/></a>
+</p>
+
+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.
+
+[GitHub](https://github.com/CambrianTech/continuum) · [All Models](https://huggingface.co/continuum-ai) · [Forge-Alloy](https://github.com/CambrianTech/forge-alloy)
+
+## License
+
+apache-2.0