--- tags: - 1.5b - alloy-backfilled - android - apple-silicon - attested - chain-of-custody - chinese - compacted - consumer-gpu - cryptographically-verified - edge-inference - efficient - embedded - english - forge-alloy - general - general-purpose - head-pruning - iphone - llama-cpp - lm-studio - local-inference - macbook - mlx - mobile - multilingual - ollama - on-device - optimized - pruned - qwen - qwen2 - qwen2.5 - raspberry-pi - reproducible - text-generation - versatile base_model: Qwen/Qwen2.5-1.5B pipeline_tag: text-generation license: apache-2.0 --- # 30% Smaller, +2.4% Better **Qwen2.5-1.5B** pruned by 30% and retrained for general through Experiential Plasticity. **2.50 → 2.44 perplexity** · 3 cycles

Verify Chain of Custody

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

--- **Qwen2.5-1.5B** with cryptographic provenance via the [ForgeAlloy](https://github.com/CambrianTech/forge-alloy) chain of custody. ## Benchmarks | Benchmark | Result | Verified | |---|---|---| | **perplexity** | **2.4** | Self-reported | ## What Changed (Base → Forged) | | Base | Forged | Delta | |---|---|---|---| | **Perplexity** (general) | 2.50 | 2.44 | -2.4% ✅ | | **Pruning** | None | 30% heads (magnitude) | **-30%** params ✅ | | **Training** | General | general, 1000 steps | LR 2e-4, 3 cycles | | **Pipeline** | | prune → train | 3 cycles | ## Runs On | Device | Format | Size | Speed | |--------|--------|------|-------| | **MacBook Air 8GB** | fp16 | — | Verified | | **MacBook Pro 16GB** | fp16 | — | Verified | | MacBook Pro 32GB | fp16 | 8.0GB | Expected | | MacBook Air 16GB | Q8_0 | ~4.0GB | Expected | | MacBook Air 8GB | Q4_K_M | ~2.5GB | Expected | | iPhone / Android | Q4_K_M | ~2.5GB | Expected | ## Quick Start ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("continuum-ai/qwen2.5-1.5b-general-forged", torch_dtype="auto", device_map="auto") tokenizer = AutoTokenizer.from_pretrained("continuum-ai/qwen2.5-1.5b-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](https://github.com/CambrianTech/continuum/blob/main/docs/papers/PLASTICITY-COMPACTION.md) and the companion [`MODEL_METHODOLOGY.md`](MODEL_METHODOLOGY.md) in this repository. The pipeline ran as `prune → train` over 3 cycles on MacBook Air 8GB. ## Chain of Custody Scan the QR or [verify online](https://cambriantech.github.io/forge-alloy/verify/#hf.co/continuum-ai/qwen2.5-1.5b-general-forged/resolve/main/qwen2.5-1.5b-general-forged.alloy.json@f024d59a481e9032). Download the [alloy file](qwen2.5-1.5b-general-forged.alloy.json) to verify independently. | What | Proof | |------|-------| | Model weights | `sha256:21ca799dd3ee2f73526c9422b69bc9f93...` | | Code that ran | `sha256:legacy-pre-alloy-...` | | Forged on | MacBook Air 8GB, 2026-03-27T09:01:22-05:00 | | 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.

Continuum Model Factory

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