mariiaponom/llama_13b_class
The mariiaponom/llama_13b_class model is a Llama-based language model, likely around 13 billion parameters, that was trained using 4-bit quantization with the bitsandbytes library. It utilizes nf4 quantization and bfloat16 compute dtype for efficient training. This model is primarily characterized by its training methodology, focusing on efficient resource utilization through advanced quantization techniques.
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Model Overview
The mariiaponom/llama_13b_class model is a Llama-based language model, distinguished by its specific training methodology. The model was trained using advanced quantization techniques provided by the bitsandbytes library, indicating a focus on computational efficiency and reduced memory footprint during the training process.
Key Training Details
- Quantization: The model leveraged
bitsandbytesfor 4-bit quantization during training, specifically usingnf4(NormalFloat 4-bit) quantization type. - Double Quantization:
bnb_4bit_use_double_quantwas enabled, further optimizing memory usage. - Compute Data Type: Training computations were performed using
bfloat16, balancing precision with performance. - Framework: The PEFT (Parameter-Efficient Fine-Tuning) library, version 0.4.0, was utilized, suggesting that the model might have undergone fine-tuning with efficient adaptation methods.
Good For
- Resource-constrained environments: Models trained with such quantization methods are often suitable for deployment or further fine-tuning in environments with limited GPU memory.
- Understanding efficient training: This model serves as an example of applying advanced quantization techniques to large language models for more efficient training workflows.