| import json, sys |
| print('HF-JOBS-LANE-OK', sys.version.split()[0]) |
| import torch, transformers |
| print('torch', torch.__version__, 'transformers', transformers.__version__) |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
| MODEL = 'uctnlp/mzansilm-125m' |
| tok = AutoTokenizer.from_pretrained(MODEL) |
| model = AutoModelForCausalLM.from_pretrained(MODEL) |
| model.eval() |
| SA = [ |
| 'Umuntu wethu umnikazi wosazibeli ngokuqondisa umhlaba wonke umzimba wezokwelapha.', |
| 'Intsapha ye-AI ingenza ulunikezelo lwamanzi ngokuchanekileyo kakhulu.', |
| 'Die plugins moet spoedig ingelewer word.', |
| 'Re a lokwa feela haholo holimo ho ya haholo.', |
| ] |
| tot = 0.0; n = 0 |
| for s in SA * 3: |
| ids = tok(s, return_tensors='pt').input_ids |
| with torch.no_grad(): |
| o = model(ids, labels=ids) |
| tot += float(o.loss); n += 1 |
| res = {'event':'checkpoint','gate':'mzansilm_probe','status':'recorded', |
| 'metrics':{'mean_loss':round(tot/n,4),'samples':n,'vocab':tok.vocab_size, |
| 'params':sum(p.numel() for p in model.parameters())}, |
| 'model':MODEL,'compute':'hf-jobs-cpu'} |
| print('RESULT_JSON=' + json.dumps(res)) |
| open('probe_result.json','w').write(json.dumps(res) + '\n') |
|
|
