Text Generation
MLX
Safetensors
qwen2
0.5b
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
mobile
multilingual
ollama
on-device
optimized
pruned
qwen
qwen2.5
raspberry-pi
reproducible
versatile
Instructions to use continuum-ai/qwen2.5-0.5b-general-forged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use continuum-ai/qwen2.5-0.5b-general-forged with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("continuum-ai/qwen2.5-0.5b-general-forged") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use continuum-ai/qwen2.5-0.5b-general-forged with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "continuum-ai/qwen2.5-0.5b-general-forged" --prompt "Once upon a time"
Correct qwen2.5-0.5b-general-forged.alloy.json pass@1 to canonical evalplus convention (v1.0.0)
66b4faf verified | { | |
| "name": "qwen2.5-0.5b-general-forged", | |
| "version": "1.0.0", | |
| "description": "Forged Qwen2.5-0.5B for general domain via per-layer head pruning + LoRA recovery training. This alloy was retroactively synthesized from forging_results.json on 2026-04-08 \u2014 the forge run itself executed at 2026-03-27T08:22:03-05:00. The published model weights are unchanged; this alloy adds the missing forge-alloy provenance envelope so the artifact participates in the chain-of-custody system.", | |
| "author": "continuum-ai", | |
| "tags": [ | |
| "general", | |
| "forged", | |
| "experiential-plasticity", | |
| "forge-alloy", | |
| "alloy-backfilled" | |
| ], | |
| "license": "apache-2.0", | |
| "source": { | |
| "baseModel": "Qwen/Qwen2.5-0.5B", | |
| "architecture": "qwen2" | |
| }, | |
| "stages": [ | |
| { | |
| "type": "prune", | |
| "strategy": "magnitude", | |
| "level": 0.3, | |
| "notes": "Legacy strategy name from forging_results.json: 'combined'. Mapped to canonical 'magnitude' for the alloy schema. The actual forge run used the legacy code path; this alloy is a retroactive provenance record, not a re-execution recipe." | |
| }, | |
| { | |
| "type": "train", | |
| "domain": "general", | |
| "steps": 1000, | |
| "learningRate": "2e-4", | |
| "dataset": "wikitext-2" | |
| } | |
| ], | |
| "cycles": 3, | |
| "results": { | |
| "completedAt": "2026-03-27T08:22:03-05:00", | |
| "baselinePerplexity": 2.83, | |
| "finalPerplexity": 2.92, | |
| "improvementPct": -3.18, | |
| "benchmarks": [ | |
| { | |
| "name": "perplexity", | |
| "metrics": { | |
| "baseline": 2.83, | |
| "final": 2.92, | |
| "improvement_pct": -3.18, | |
| "dataset": "wikitext-2" | |
| } | |
| } | |
| ], | |
| "hardwareVerified": [ | |
| { | |
| "device": "Phone / Raspberry Pi", | |
| "format": "Q4_K_M", | |
| "verified": false | |
| }, | |
| { | |
| "device": "MacBook Air 8GB", | |
| "format": "Q4_K_M", | |
| "verified": false | |
| } | |
| ], | |
| "samples": [], | |
| "integrity": { | |
| "trustLevel": "self-attested", | |
| "code": { | |
| "runner": "sentinel-ai/forge_model (legacy pre-\u00a74.1.3.1 path)", | |
| "version": "2.x", | |
| "binaryHash": "sha256:legacy-pre-alloy-schema" | |
| }, | |
| "modelHash": "sha256:3f1fd0b9b5b70d7869fd9a48162641f7652c693cd01d5bc2b1781321e5786c64", | |
| "fileHashes": [ | |
| { | |
| "filename": "model.safetensors", | |
| "sha256": "b0103eceb715ea1bd27bea2cafbd1b201f8a24b5c81d3228c9555220abd4d756", | |
| "size": 988097824 | |
| } | |
| ], | |
| "datasets": [ | |
| { | |
| "name": "wikitext-2", | |
| "hash": "sha256:not-pinned-legacy" | |
| } | |
| ], | |
| "attestedAt": "2026-03-27T08:22:03-05:00" | |
| } | |
| } | |
| } | |