mrabhi0505/h2ogpt-16k-codellama-7b-trained-model1
The mrabhi0505/h2ogpt-16k-codellama-7b-trained-model1 is a Code Llama-based model, likely a 7 billion parameter variant, that has undergone further training. It utilizes 4-bit quantization with the bitsandbytes library, specifically employing nf4 quantization and double quantization for efficiency. This model is optimized for tasks benefiting from quantized large language models, particularly those requiring efficient inference on hardware with limited memory.
Loading preview...
Model Overview
The mrabhi0505/h2ogpt-16k-codellama-7b-trained-model1 is a fine-tuned model based on the Code Llama architecture, likely a 7 billion parameter version. Its training process incorporated advanced quantization techniques to optimize for performance and memory efficiency.
Key Training Details
- Quantization Method: The model was trained using
bitsandbytesquantization. - Quantization Type: It leverages 4-bit quantization (
load_in_4bit: True) withnf4quantization type. - Double Quantization: Enhanced efficiency is achieved through
bnb_4bit_use_double_quant: True. - Compute Data Type: The computation during 4-bit quantization was performed using
bfloat16(bnb_4bit_compute_dtype: bfloat16). - Framework: The training utilized PEFT version 0.5.0.
Potential Use Cases
This model is particularly well-suited for scenarios where:
- Efficient Inference is critical, due to its 4-bit quantization.
- Code-related tasks are the primary focus, given its Code Llama base.
- Resource-constrained environments benefit from reduced memory footprint.