Alqarni/trained-llama
TEXT GENERATIONConcurrency Cost:1Model Size:8BQuant:FP8Ctx Length:32kTool Calling:SupportedPublished:May 29, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
Alqarni/trained-llama is an 8 billion parameter Llama 3.1-based causal language model developed by Alqarni. 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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Model Overview
Alqarni/trained-llama is an 8 billion parameter instruction-tuned language model, developed by Alqarni. It is based on the Llama 3.1 architecture and was finetuned from unsloth/llama-3.1-8b-instruct-unsloth-bnb-4bit.
Key Characteristics
- Efficient Training: This model was trained with Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process compared to standard methods.
- Llama 3.1 Base: Built upon the robust Llama 3.1-8B-Instruct foundation, it inherits strong general-purpose language understanding and generation capabilities.
- Instruction-Tuned: The model is designed to follow instructions effectively, making it suitable for a wide range of conversational and task-oriented applications.
Potential Use Cases
- General Instruction Following: Ideal for tasks requiring the model to understand and execute specific commands or prompts.
- Rapid Prototyping: Its efficient training methodology suggests potential for quick adaptation to new datasets or specific domain requirements.
- Conversational AI: Suitable for chatbots, virtual assistants, and other interactive applications where instruction adherence is crucial.