W-61/llama3-hh-harmless-qt045-b0p5-20260429-085449
W-61/llama3-hh-harmless-qt045-b0p5-20260429-085449 is an 8 billion parameter language model, fine-tuned from W-61/llama-3-8b-base-sft-hh-harmless-4xh200. This model has been specifically fine-tuned on the Anthropic/hh-rlhf dataset, indicating an optimization for harmlessness and helpfulness in conversational AI. It is designed for applications requiring robust and safe language generation, particularly in human-computer interaction scenarios.
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Model Overview
This model, W-61/llama3-hh-harmless-qt045-b0p5-20260429-085449, is an 8 billion parameter language model derived from W-61/llama-3-8b-base-sft-hh-harmless-4xh200. Its primary distinction lies in its fine-tuning process, which utilized the Anthropic/hh-rlhf dataset.
Key Capabilities
- Harmlessness Optimization: Fine-tuned on the Anthropic/hh-rlhf dataset, suggesting a focus on generating responses that are safe and avoid harmful content.
- Helpfulness: The fine-tuning dataset also implies an emphasis on producing helpful and constructive outputs.
- Base Model: Built upon a Llama 3 8B base, providing a strong foundation for general language understanding and generation tasks.
Training Details
The model underwent a single epoch of training with a learning rate of 5e-07. It utilized a total training batch size of 64 across 4 GPUs, employing an AdamW optimizer and a cosine learning rate scheduler with a 0.1 warmup ratio. The training was conducted using Transformers 4.51.0, Pytorch 2.3.1+cu121, Datasets 2.21.0, and Tokenizers 0.21.4.
Intended Use Cases
This model is particularly suited for applications where the generation of safe, helpful, and non-toxic language is paramount. Potential use cases include:
- Customer support chatbots
- Content moderation tools
- Educational assistants
- General conversational AI requiring robust safety guardrails