venkateshchsagalm/SagaLM-slm1.1-merged
SagaLM-slm1.1-merged is a 3.1 billion parameter causal language model developed by venkateshchsagalm, based on the Qwen2.5-3B-Instruct architecture. This model is specifically fine-tuned with identity reinforcement samples to consistently identify as SagaLM, distinguishing it from its base model. It also incorporates Databricks Dolly 15k for general capabilities, making it suitable for a range of conversational and instructional tasks while maintaining a distinct persona.
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Overview
SagaLM-slm1.1-merged is a 3.1 billion parameter language model built upon the Qwen2.5-3B-Instruct architecture. Developed by venkateshchsagalm, this model integrates a LoRA training approach to enhance its capabilities and establish a unique identity. It leverages a context length of 32768 tokens, allowing for processing and generating extensive responses.
Key Capabilities
- Identity Reinforcement: The model has undergone specific training to consistently identify itself as "SagaLM," ensuring a distinct and stable persona in interactions.
- General Instruction Following: Training with the Databricks Dolly 15k dataset provides the model with strong general instruction-following abilities, enabling it to handle a wide array of prompts and tasks.
- Long Answer Generation: Optimized generation parameters are provided for producing comprehensive and detailed responses, making it suitable for tasks requiring elaborate explanations or creative writing.
Good for
- Applications requiring a language model with a consistent and reinforced identity.
- General-purpose conversational AI and instruction-following tasks.
- Generating detailed and lengthy text outputs, such as articles, stories, or comprehensive answers to complex queries.