varuneshv/SARGLLM
SARGLLM is a 4 billion parameter Qwen3-based instruction-tuned causal language model developed by varuneshv. This model was finetuned using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is designed for general instruction-following tasks, leveraging its efficient training methodology.
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SARGLLM: An Efficiently Finetuned Qwen3 Model
SARGLLM is a 4 billion parameter instruction-tuned model built upon the Qwen3 architecture. Developed by varuneshv, this model distinguishes itself through its highly optimized training process.
Key Capabilities & Features
- Base Model: Finetuned from
unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit, indicating a strong foundation for instruction-following tasks. - Optimized Training: Utilizes Unsloth and Huggingface's TRL library, resulting in a 2x faster finetuning process. This efficiency allows for quicker iteration and deployment.
- Context Length: Supports a substantial context length of 32768 tokens, enabling it to process and generate longer, more complex responses.
When to Use This Model
SARGLLM is suitable for developers and researchers looking for a capable 4B parameter model that benefits from efficient training techniques. Its Qwen3 base and instruction-tuned nature make it a strong candidate for various natural language processing applications requiring robust instruction following.