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Trust: self-attested · 1 benchmark · 2 devices tested
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Qwen2.5-3B with cryptographic provenance via the
ForgeAlloy chain of custody.
1from transformers import AutoModelForCausalLM, AutoTokenizer
2
3model = AutoModelForCausalLM.from_pretrained("continuum-ai/qwen2.5-3b-general-forged",
4 torch_dtype="auto", device_map="auto")
5tokenizer = AutoTokenizer.from_pretrained("continuum-ai/qwen2.5-3b-general-forged")
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 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.
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.
