Instructions to use HireSignal-5DS-Team/hiresignal-qwen3-4b-matcher-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use HireSignal-5DS-Team/hiresignal-qwen3-4b-matcher-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B-Instruct-2507") model = PeftModel.from_pretrained(base_model, "HireSignal-5DS-Team/hiresignal-qwen3-4b-matcher-lora") - Notebooks
- Google Colab
- Kaggle
HireSignal Matcher LoRA for Qwen/Qwen3-4B-Instruct-2507
- Base revision:
cdbee75f17c01a7cc42f958dc650907174af0554; 4-bit NF4 (double quant), fp16 compute; LoRA r=16, alpha=32, targets ['q_proj', 'k_proj', 'v_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj']. - Task: one job requirement vs one anonymised CV -> JSON {decision, basis, evidence, explanation} (Matcher prompt p1).
- Training data: {"bench_synthetic_sft": 505, "contractnli": 272} examples; human labels on public Djinni data (MIT), ContractNLI (CC BY 4.0, Koreeda & Manning 2021), synthetic bench-v1. No private CVs. Data is not included in this repo.
- Steps 50, best checkpoint /kaggle/working/qwen3-4b-hs-lora/checkpoint-50. Evaluation: see comparison.csv.
- Screening aid only: no automatic hiring decisions; MISSING/not_documented means absence of evidence.
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Qwen/Qwen3-4B-Instruct-2507