Millian/felia-7b-title
TEXT GENERATIONConcurrency Cost:1Model Size:7BQuant:FP8Ctx Length:4kLicense:llama2Architecture:Transformer Open Weights Cold
Millian/felia-7b-title is a 7 billion parameter language model developed by Millian, featuring a 4096-token context length. This model was trained using 4-bit quantization with double quantization enabled, optimizing for efficient deployment and inference. While specific capabilities are not detailed, its training configuration suggests a focus on resource-efficient performance for general language tasks.
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Overview
Millian/felia-7b-title is a 7 billion parameter language model with a 4096-token context window. Developed by Millian, this model's training process highlights a strong emphasis on efficiency through advanced quantization techniques.
Key Training Details
- Quantization: The model was trained using
bitsandbytes4-bit quantization (bnb_4bit_quant_type: fp4). - Double Quantization:
bnb_4bit_use_double_quantwas enabled, further reducing memory footprint during training and potentially for inference. - Compute Data Type: Training utilized
float32for computation, ensuring precision during the quantization process. - Frameworks: PEFT version 0.5.0.dev0 was used, indicating a parameter-efficient fine-tuning approach.
Good For
- Resource-constrained environments: The 4-bit quantization with double quantization makes it suitable for deployment where memory and computational resources are limited.
- General language tasks: As a 7B parameter model, it is likely capable of a wide range of natural language understanding and generation tasks, though specific optimizations are not detailed.
- Developers interested in efficient model deployment: The training methodology provides insights into optimizing large language models for practical use.