| |
| import json, torch, torch.nn as nn |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| from peft import PeftModel |
| from huggingface_hub import snapshot_download |
| G=["lang_core","api_design","data_storage","system_design","cloud_devops","reliability_obs","security","testing_quality","frontend","leadership"] |
| E=["supported","partial","contradicted","insufficient"]; P=["0","1","2","3","4","5","unknown"] |
| T={"g":0.5,"e":2.4,"p":2.8} |
| d=snapshot_download("niko0xdev/skilljev") |
| tok=AutoTokenizer.from_pretrained("AlexWortega/openjev",subfolder="qwen3.5-4b-nli-v5"); tok.padding_side="right" |
| tok.pad_token=tok.pad_token or tok.eos_token |
| full=AutoModelForSequenceClassification.from_pretrained("AlexWortega/openjev",subfolder="qwen3.5-4b-nli-v5",dtype=torch.bfloat16) |
| H=full.score.in_features; bb=PeftModel.from_pretrained(full.base_model,d).eval().to("cuda") |
| heads=nn.ModuleDict({"g":nn.Linear(H,len(G)),"e":nn.Linear(H,len(E)),"p":nn.Linear(H,len(P))}) |
| heads.load_state_dict(torch.load(f"{d}/heads.pt",map_location="cpu")); heads.to("cuda").eval() |
| @torch.no_grad() |
| def predict(requirement, evidence): |
| ev="\n".join(f"[{s}/{c}] {t}" for s,c,t in evidence) or "(no evidence)" |
| x=tok(f"Requirement: {requirement}\nEvidence:\n{ev}",truncation=True,max_length=192,return_tensors="pt").to("cuda") |
| z=bb(**x).last_hidden_state[0,-1].float(); out={} |
| for k,lab,name in [("g",G,"skill_group"),("e",E,"evidence"),("p",P,"proficiency")]: |
| pr=(heads[k](z)/T[k]).softmax(-1); i=int(pr.argmax()); out[name]={"label":lab[i],"confidence":round(float(pr[i]),3)} |
| return out |
| if __name__=="__main__": |
| print(json.dumps(predict("Strong PostgreSQL skills",[("cv","experience","Partitioned a 3TB PostgreSQL table and tuned indexes, cutting queries from 40s to 1s.")]),indent=1)) |
|
|
