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dimajyg
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Model Details
Model Provider
dimajyg
Model Tree
openbmb/MiniCPM5-2B
Input Modalities
Output Modalities
Supported Functionality
| train | AllNLI 200k + ANLI r3 + WANLI, 1 epoch, LoRA r=16, fp32, A100 80 GB, 71.2 min |
| MNLI-m 2k seed 42 | acc 0.8885, ECE 15-bin 0.0104, Brier 0.082 |
| smoke guitar P(ent) | 0.940 |
| smoke refund noul yes | 0.462 (weak; hard-mix run is in progress) |
Hard-mix continue (ANLI r1/r2, SciTail, QNLI, FEVER, policy/numbers) will overwrite these files. Check eval.json.
python
import torchfrom transformers import AutoModelForSequenceClassification, AutoTokenizerfrom peft import PeftModel BASE = "openbmb/MiniCPM5-2B"ADAPTER = "dimajyg/homemade-jev"ID2LABEL = {0: "contradiction", 1: "entailment", 2: "neutral"} tok = AutoTokenizer.from_pretrained(ADAPTER, use_fast=True)if tok.pad_token is None: tok.pad_token = tok.eos_tokenmodel = AutoModelForSequenceClassification.from_pretrained( BASE, num_labels=3, id2label=ID2LABEL, label2id={v: k for k, v in ID2LABEL.items()}, torch_dtype=torch.float32,)model = PeftModel.from_pretrained(model, ADAPTER)model.eval() def nli(premise, hypothesis): text = f"Premise: {premise}\nHypothesis: {hypothesis}" batch = tok(text, return_tensors="pt", truncation=True, max_length=256, return_token_type_ids=False) with torch.no_grad(): p = torch.softmax(model(**batch).logits.float(), dim=-1)[0] return {ID2LABEL[i]: round(float(p[i]), 4) for i in range(3)}Space (CPU Basic, slow): https://huggingface.co/spaces/dimajyg/homemade-jev
Do not treat softmax as calibrated for a new domain. Merge of LoRA into base is not in this repo (Air 16 GB).
import torchfrom transformers import AutoModelForSequenceClassification, AutoTokenizerfrom peft import PeftModel BASE = "openbmb/MiniCPM5-2B"ADAPTER = "dimajyg/homemade-jev"ID2LABEL = {0: "contradiction", 1: "entailment", 2: "neutral"} tok = AutoTokenizer.from_pretrained(ADAPTER, use_fast=True)if tok.pad_token is None: tok.pad_token = tok.eos_tokenmodel = AutoModelForSequenceClassification.from_pretrained( BASE, num_labels=3, id2label=ID2LABEL, label2id={v: k for k, v in ID2LABEL.items()}, torch_dtype=torch.float32,)model = PeftModel.from_pretrained(model, ADAPTER)model.eval() def nli(premise, hypothesis): text = f"Premise: {premise}\nHypothesis: {hypothesis}" batch = tok(text, return_tensors="pt", truncation=True, max_length=256, return_token_type_ids=False) with torch.no_grad(): p = torch.softmax(model(**batch).logits.float(), dim=-1)[0] return {ID2LABEL[i]: round(float(p[i]), 4) for i in range(3)}