nikinetrahutama/afx-issue-llama-chat-model
The nikinetrahutama/afx-issue-llama-chat-model is a 7 billion parameter Llama-based chat model, developed by nikinetrahutama. This model was trained using 4-bit quantization (nf4) with double quantization and bfloat16 compute dtype, leveraging PEFT for efficient fine-tuning. It is optimized for chat-based applications, providing a compact yet capable solution for conversational AI tasks.
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Model Overview
The nikinetrahutama/afx-issue-llama-chat-model is a 7 billion parameter language model fine-tuned for chat applications. It is built upon the Llama architecture, making it a suitable choice for conversational AI tasks.
Training Details
This model was trained using a specific bitsandbytes quantization configuration to optimize for efficiency and performance. Key aspects of its training include:
- Quantization Method:
bitsandbyteswithload_in_4bit: True - Quantization Type:
nf4(NormalFloat 4-bit) - Double Quantization: Enabled (
bnb_4bit_use_double_quant: True) - Compute Data Type:
bfloat16 - Framework: PEFT (Parameter-Efficient Fine-Tuning) version 0.5.0.dev0 was utilized, indicating an efficient fine-tuning approach.
These training parameters suggest a focus on reducing memory footprint and improving inference speed while maintaining model quality, which is beneficial for deployment in resource-constrained environments.
Use Cases
Given its Llama base and chat-oriented fine-tuning, this model is well-suited for:
- Developing conversational agents and chatbots.
- Interactive dialogue systems.
- Applications requiring efficient language understanding and generation in a chat format.