wesley7137/Neuro-Sci-PyWizCoder-13B-V1-merged
The wesley7137/Neuro-Sci-PyWizCoder-13B-V1-merged model is a 13 billion parameter language model with a 4096 token context length. This model was trained using bitsandbytes 4-bit quantization, specifically nf4, and PEFT 0.4.0. Its primary differentiator and use case are not explicitly detailed in the provided README, which focuses on training configuration.
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Model Overview
This model, wesley7137/Neuro-Sci-PyWizCoder-13B-V1-merged, is a 13 billion parameter language model. The provided information primarily details its training configuration rather than specific capabilities or intended use cases.
Training Details
The model was trained utilizing bitsandbytes for quantization, specifically employing a 4-bit quantization scheme (nf4). Key parameters for this quantization included:
load_in_4bit: Truebnb_4bit_quant_type: nf4bnb_4bit_compute_dtype: float16
Additionally, the training process incorporated the PEFT (Parameter-Efficient Fine-Tuning) framework, with version 0.4.0 being used.
Key Characteristics
- Parameter Count: 13 billion parameters.
- Context Length: 4096 tokens.
- Quantization: Trained with
bitsandbytes4-bit nf4 quantization. - Framework: Utilizes PEFT 0.4.0 for efficient fine-tuning.
Use Cases
Based solely on the provided README, specific optimized use cases are not detailed. Users should infer potential applications based on the model's size and general language model capabilities, considering the training methodology focused on efficient quantization.