tsavage68/chat_500STEPS_1e7rate_SFT
The tsavage68/chat_500STEPS_1e7rate_SFT model is a 7 billion parameter Llama-2-7b-chat-hf variant, fine-tuned over 500 steps with a low learning rate of 1e-07. This model is based on the Llama 2 architecture and is optimized for chat-based applications. Its training process, characterized by a small learning rate and specific batching, suggests a focus on refining conversational abilities from its Llama-2-chat base.
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Model Overview
The tsavage68/chat_500STEPS_1e7rate_SFT is a 7 billion parameter language model, derived from the meta-llama/Llama-2-7b-chat-hf architecture. It has undergone a specific fine-tuning process, characterized by 500 training steps with a very low learning rate of 1e-07, suggesting a focused refinement of its conversational capabilities.
Key Training Details
- Base Model: meta-llama/Llama-2-7b-chat-hf
- Parameters: 7 billion
- Context Length: 4096 tokens
- Training Steps: 500
- Learning Rate: 1e-07
- Optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- Batch Size: 4 (train), 1 (eval) with 2 gradient accumulation steps, totaling an effective batch size of 8.
- Loss: Achieved a final validation loss of 1.4297, indicating a stable training progression.
Potential Use Cases
Given its fine-tuning from a chat-optimized Llama 2 base, this model is likely suitable for:
- Conversational AI: Enhancing dialogue systems and chatbots.
- Interactive Applications: Powering applications requiring natural language interaction.
- Further Fine-tuning: Serving as a refined base for more specialized chat-oriented tasks.