rbelanec/train_qnli_42_1773765556
TEXT GENERATIONConcurrency Cost:1Model Size:1BQuant:BF16Ctx Length:32kPublished:Mar 17, 2026License:llama3.2Architecture:Transformer Warm
The rbelanec/train_qnli_42_1773765556 model is a 1 billion parameter Llama-3.2-1B-Instruct variant, fine-tuned by rbelanec on the QNLI dataset. This model is specifically optimized for Question-answering Natural Language Inference (QNLI) tasks, demonstrating a final validation loss of 0.1074 after 5 epochs of training. It is designed for applications requiring precise inference capabilities on question-answering pairs.
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Model Overview
This model, rbelanec/train_qnli_42_1773765556, is a specialized 1 billion parameter language model derived from meta-llama/Llama-3.2-1B-Instruct. It has undergone fine-tuning specifically on the QNLI (Question-answering Natural Language Inference) dataset.
Key Capabilities
- Question-answering Natural Language Inference: The model is fine-tuned to determine if a given text snippet contains the answer to a question, or if it supports or contradicts a hypothesis based on a question.
- Performance: Achieved a validation loss of 0.1074 on the QNLI evaluation set after 5 training epochs, indicating strong performance on its target task.
- Training Details: Trained with a learning rate of 5e-05, batch sizes of 8, and utilized the AdamW optimizer with a cosine learning rate scheduler.
When to Use This Model
- QNLI Tasks: Ideal for applications requiring high accuracy in natural language inference related to question-answering.
- Resource-Efficient Inference: As a 1 billion parameter model, it offers a more lightweight solution compared to larger models while still providing specialized performance for QNLI.