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Model Card for Ne0 Model Details Model Name: Ne0 Base Model: Qwen/Qwen2.5-1.5B-Instruct Training Method: LoRA (Low-Rank Adaptation) Task: Text generation for Quantum Calibration Analysis Developer: Shivansh Sagar Pancholi Intended Use This model is fine-tuned to analyze quantum experiment data and provide assessments on DRAG calibration success, optimal parameters, and suggested sweep ranges based on experimental descriptions.
Training Data Fine-tuned on the nvidia/QCalEval dataset (test split), which focuses on quantum calibration evaluation and reasoning.
Training Procedure Quantization: 4-bit (NF4) using BitsAndBytes. LoRA Config: rank=8, alpha=16, target modules: q_proj, v_proj. Optimizer: AdamW Batch Size: 1 (with gradient accumulation of 8) Precision: BF16 compute How to Use---
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("ShivanshSagarPancholi/Ne0")
tokenizer = AutoTokenizer.from_pretrained("ShivanshSagarPancholi/Ne0")
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