caovanbao68/Llama3-1b-multi-conversation-sft
TEXT GENERATIONConcurrency Cost:1Model Size:1BQuant:BF16Ctx Length:32kPublished:Jan 3, 2026License:apache-2.0Architecture:Transformer Open Weights Warm
The caovanbao68/Llama3-1b-multi-conversation-sft is a 1 billion parameter Llama 3 model, developed by caovanbao68, fine-tuned for multi-turn conversations. It was trained using Unsloth and Huggingface's TRL library, enabling faster fine-tuning. This model is optimized for conversational AI applications, leveraging its compact size and specialized training for efficient deployment.
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Model Overview
The caovanbao68/Llama3-1b-multi-conversation-sft is a 1 billion parameter Llama 3 model, developed by caovanbao68, specifically fine-tuned for multi-turn conversational tasks. It builds upon the unsloth/llama-3.2-1b-instruct-bnb-4bit base model.
Key Capabilities
- Multi-turn Conversation: Specialized training makes it adept at handling and generating coherent responses in extended dialogues.
- Efficient Fine-tuning: The model was fine-tuned using Unsloth and Huggingface's TRL library, which facilitates faster training processes.
- Compact Size: With 1 billion parameters, it offers a balance between performance and computational efficiency, making it suitable for resource-constrained environments.
Good For
- Chatbots and Conversational Agents: Its multi-conversation fine-tuning makes it well-suited for interactive AI applications.
- Edge Device Deployment: The smaller parameter count allows for more efficient deployment on devices with limited computational resources.
- Rapid Prototyping: The use of Unsloth for faster training can accelerate development cycles for conversational AI projects.