jackf857/qwen3-8b-base-sft-hh-harmless-4xh200-batch-64

Model Overview

This model, jackf857/qwen3-8b-base-sft-hh-harmless-4xh200-batch-64, is an 8 billion parameter language model derived from the Qwen/Qwen3-8B-Base architecture. It has undergone a crucial fine-tuning process using the Anthropic/hh-rlhf (helpful and harmless) dataset, which is designed to improve the model's ability to generate responses that are both helpful and non-toxic.

Key Characteristics

• Base Model: Qwen3-8B-Base, providing a robust foundation for language understanding and generation.
• Fine-tuning Objective: Enhanced for harmlessness and helpfulness through training on the Anthropic/hh-rlhf dataset.
• Parameter Count: 8 billion parameters, offering a balance between performance and computational efficiency.
• Context Length: Supports a context window of 32,768 tokens, enabling the processing of longer inputs and generating more coherent, extended outputs.

Training Details

The model was trained with a learning rate of 2e-05, a total batch size of 64, and utilized a cosine learning rate scheduler with a 0.1 warmup ratio over 1 epoch. The training achieved a validation loss of 1.5309, indicating effective learning from the safety-focused dataset.

Intended Use Cases

This model is particularly well-suited for applications requiring:

• Safer AI Interactions: Generating responses that adhere to ethical guidelines and avoid harmful content.
• Helpful Conversational Agents: Developing chatbots or virtual assistants that provide constructive and aligned information.
• Content Moderation: Assisting in filtering or identifying potentially harmful text.

Due to its specific fine-tuning, it aims to provide more reliable and responsible outputs compared to base models.