ligaments-dev/PulseNOCV1.0
PulseNOCV1.0 is a 0.8 billion parameter Qwen3-based causal language model developed by ligaments-dev, fine-tuned from unsloth/qwen3-0.6b-unsloth-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training. It features a 32768 token context length, making it suitable for tasks requiring efficient processing of longer sequences.
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Model Overview
PulseNOCV1.0 is a 0.8 billion parameter language model developed by ligaments-dev, built upon the Qwen3 architecture. It was fine-tuned from the unsloth/qwen3-0.6b-unsloth-bnb-4bit base model, leveraging Unsloth and Huggingface's TRL library for accelerated training.
Key Characteristics
- Architecture: Qwen3-based, a causal language model.
- Parameter Count: 0.8 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a substantial context window of 32768 tokens, enabling it to process and understand longer inputs.
- Training Efficiency: Benefited from Unsloth, which facilitated a 2x faster training process compared to standard methods.
Potential Use Cases
Given its efficient training and substantial context length, PulseNOCV1.0 could be well-suited for applications requiring:
- Text generation: Creating coherent and contextually relevant text.
- Long-form content analysis: Processing and summarizing extensive documents or conversations.
- Efficient deployment: Its smaller parameter count makes it more amenable to resource-constrained environments.