W-61/llama3-8b-base-new-method-q_t-0.4-s_star0.6
W-61/llama3-8b-base-new-method-q_t-0.4-s_star0.6 is an 8 billion parameter language model developed by W-61, fine-tuned from llama-3-8b-base-sft-ultrachat-8xh200. This model was trained on the HuggingFaceH4/ultrafeedback_binarized dataset, leveraging a context length of 8192 tokens. It is designed for general language understanding and generation tasks, building upon its base model's capabilities.
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Model Overview
This model, llama3-8b-base-new-method-q_t-0.4-s_star0.6, is an 8 billion parameter language model developed by W-61. It is a fine-tuned variant of the W-61/llama-3-8b-base-sft-ultrachat-8xh200 model, specifically trained on the HuggingFaceH4/ultrafeedback_binarized dataset. The training process involved a learning rate of 5e-07, a total batch size of 128, and was conducted over 1 epoch using a multi-GPU setup.
Key Training Details
- Base Model:
W-61/llama-3-8b-base-sft-ultrachat-8xh200 - Fine-tuning Dataset:
HuggingFaceH4/ultrafeedback_binarized - Parameters: 8 billion
- Context Length: 8192 tokens
- Learning Rate: 5e-07
- Optimizer: AdamW with betas=(0.9, 0.999) and epsilon=1e-08
- Epochs: 1
Intended Use Cases
While specific intended uses and limitations require further information, this model is generally suitable for tasks that benefit from instruction-tuned language models, such as:
- Text generation
- Question answering
- Summarization
- Conversational AI
Further details on specific performance metrics and optimal applications are needed for a comprehensive understanding.