Instructions to use Vijay-1807/OpenEnv-HR-Agent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Vijay-1807/OpenEnv-HR-Agent with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "Vijay-1807/OpenEnv-HR-Agent") - Notebooks
- Google Colab
- Kaggle
OpenEnv-HR-Agent (LoRA adapter)
PEFT LoRA adapter for SentinelHire / OpenEnv HR hiring agent demos.
Base checkpoint: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit (see adapter_config.json).
Use in the app
Set:
SENTINEL_ADAPTER_REPO=Vijay-1807/OpenEnv-HR-Agent
or download this repo snapshot and point SENTINEL_ADAPTER_PATH at the folder containing adapter_model.safetensors.
Training
Trained with GRPO / Unsloth in this project (train_qwen_grpo.py).
Code: github.com/Vijay-1807/OpenEnv-HR-Agent
Reward curve during RL training: see reward_curve.png in this repo.
Limitations
Inference requires a GPU with enough VRAM for the 4-bit base plus adapter, or a configured CPU/offload path. See the GitHub README for Streamlit setup.
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