Overview

TechNamu/Namu-1.7B is a 2 billion parameter language model developed by TechNamu, fine-tuned from the unsloth/qwen3-1.7b-base-unsloth-bnb-4bit base model. This model leverages the Unsloth library and Huggingface's TRL library, which facilitated a 2x faster training process compared to standard methods. It is designed for efficient performance while handling a substantial context length of 40960 tokens.

Key Capabilities

• Efficient Training: Achieved 2x faster training through the integration of Unsloth and Huggingface's TRL library.
• Extended Context: Supports a context window of 40960 tokens, suitable for tasks requiring processing of long inputs.
• Qwen3 Architecture: Built upon the Qwen3 model family, inheriting its foundational language understanding capabilities.

Good for

• Applications requiring a balance of performance and efficiency in a smaller parameter count.
• Tasks that benefit from a large context window, such as summarization of long documents or complex conversational AI.
• Developers looking for a model fine-tuned with optimized training techniques for faster iteration.