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continuum-ai/qwen2.5-3b-general-forged

sourceHugging Faceapache-2.0updated 6mo agoView on Hugging Face
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Model Card

30% Smaller, +0.4% Better

Qwen2.5-3B pruned by 30% and retrained for general through Experiential Plasticity.

2.30 → 2.29 perplexity · 3 cycles

<p align="center"> <a href="https://cambriantech.github.io/forge-alloy/verify/#hf.co/continuum-ai/qwen2.5-3b-general-forged/resolve/main/qwen2.5-3b-general-forged.alloy.json@a13bcfcdc2c8652a"> <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/#hf.co/continuum-ai/qwen2.5-3b-general-forged/resolve/main/qwen2.5-3b-general-forged.alloy.json@a13bcfcdc2c8652a"><b>Every claim on this card is verified</b></a><br> <b>Trust: self-attested</b> · 1 benchmark · 2 devices tested<br> <a href="https://github.com/CambrianTech/forge-alloy">ForgeAlloy</a> chain of custody · <a href="qwen2.5-3b-general-forged.alloy.json">Download alloy</a> · Merkle-chained </p>


Qwen2.5-3B with cryptographic provenance via the ForgeAlloy chain of custody.

Benchmarks

BenchmarkResultVerified
perplexity2.3Self-reported

What Changed (Base → Forged)

BaseForgedDelta
Perplexity (general)2.302.29-0.4% ✅
PruningNone30% heads (magnitude)-30% params ✅
TrainingGeneralgeneral, 1000 stepsLR 2e-4, 3 cycles
Pipelineprune → train3 cycles

Runs On

DeviceFormatSizeSpeed
MacBook Pro 16GBfp16—Verified
MacBook Pro 32GBfp16—Verified
MacBook Pro 32GBfp168.0GBExpected
MacBook Air 16GBQ8_0~4.0GBExpected
MacBook Air 8GBQ4KM~2.5GBExpected
iPhone / AndroidQ4KM~2.5GBExpected

Quick Start

python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("continuum-ai/qwen2.5-3b-general-forged",
    torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("continuum-ai/qwen2.5-3b-general-forged")

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 head pruning. 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 prune → train over 3 cycles on MacBook Pro 16GB.

Chain of Custody

Scan the QR or verify online. Download the alloy file to verify independently.

WhatProof
Model weightssha256:80def3c4bcf296e5960c37244b43018cc...
Code that ransha256:legacy-pre-alloy-...
Forged onMacBook Pro 16GB, 2026-03-27T09:33:23-05:00
Trust level`self-attested`
SpecForgeAlloy — Rust/Python/TypeScript

Make Your Own

Forged with 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 · All Models · Forge-Alloy

License

apache-2.0