Alelcv27/Llama3.1-8B-INST-Math2
TEXT GENERATIONConcurrency Cost:1Model Size:8BQuant:FP8Ctx Length:8kTool Calling:SupportedPublished:Jun 7, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
Alelcv27/Llama3.1-8B-INST-Math2 is an 8 billion parameter Llama 3.1 instruction-tuned model developed by Alelcv27. This model was fine-tuned using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is designed for general instruction-following tasks, leveraging the Llama 3.1 architecture.
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Model Overview
Alelcv27/Llama3.1-8B-INST-Math2 is an 8 billion parameter instruction-tuned language model based on the Llama 3.1 architecture. Developed by Alelcv27, this model was fine-tuned from unsloth/Llama-3.1-8B-Instruct-unsloth-bnb-4bit.
Key Capabilities
- Efficient Fine-tuning: The model was trained 2x faster by utilizing Unsloth and Huggingface's TRL library, indicating an optimized training process.
- Instruction Following: As an instruction-tuned variant, it is designed to understand and execute a wide range of user prompts and instructions.
- Llama 3.1 Foundation: Benefits from the robust capabilities and performance characteristics inherent to the Llama 3.1 base model.
Good For
- General Purpose AI Applications: Suitable for various tasks requiring instruction adherence, such as question answering, content generation, and conversational AI.
- Resource-Efficient Deployment: The 8 billion parameter size makes it a viable option for applications where computational resources are a consideration, especially given its optimized training methodology.
- Further Experimentation: Provides a solid base for developers looking to build upon a Llama 3.1 instruction-tuned model with efficient training origins.