JustVugg
1ae22a6135
Engine (c/glm.c): MLA attention with compressed KV, sigmoid noaux_tc router, int8/int4/int2 quant kernels (AVX2), per-layer LRU expert cache + pinned hot-store, batch-union MoE, native MTP speculative decoding (lossless), multi-stop + official chat template, RAM auto-budget from MemAvailable. Tokenizer: byte-level BPE in C. Tooling: coli CLI, disk-safe FP8→int4 converter, tiny-random oracle validation (TF 32/32, greedy 20/20). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
18 lines
841 B
Python
18 lines
841 B
Python
"""Genera il riferimento con transformers (greedy) e lo salva. Va lanciato da solo (usa ~13GB)."""
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import json, torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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PROMPT = "The capital of France is"
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N = 12
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tok = AutoTokenizer.from_pretrained("allenai/OLMoE-1B-7B-0924")
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ids = tok(PROMPT, return_tensors="pt").input_ids
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model = AutoModelForCausalLM.from_pretrained("allenai/OLMoE-1B-7B-0924",
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torch_dtype=torch.bfloat16, low_cpu_mem_usage=True).eval()
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with torch.no_grad():
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out = model.generate(ids, max_new_tokens=N, do_sample=False)
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full = out[0].tolist()
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json.dump({"prompt": PROMPT, "prompt_ids": ids[0].tolist(), "full_ids": full,
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"text": tok.decode(full)}, open("ref.json", "w"))
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print("RIFERIMENTO salvato:", repr(tok.decode(full)))
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print("full_ids:", full)
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