JoaoReiz/Llama3.2_3B_Paramopama
TEXT GENERATIONConcurrency Cost:1Model Size:3.2BQuant:BF16Ctx Length:32kPublished:Apr 6, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
JoaoReiz/Llama3.2_3B_Paramopama is a 3.2 billion parameter Llama-based instruction-tuned language model developed by JoaoReiz. This model was finetuned from unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit using Unsloth and Huggingface's TRL library, enabling faster training. It is designed for general language generation tasks, leveraging its efficient training methodology.
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Model Overview
JoaoReiz/Llama3.2_3B_Paramopama is a 3.2 billion parameter Llama-based language model, developed by JoaoReiz. It is an instruction-tuned model, building upon the unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit base.
Key Characteristics
- Architecture: Based on the Llama family of models.
- Parameter Count: Features 3.2 billion parameters, offering a balance between performance and computational efficiency.
- Training Efficiency: This model was finetuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process compared to standard methods.
- Context Length: Supports a context length of 32768 tokens.
Potential Use Cases
- General Text Generation: Suitable for a wide range of language generation tasks due to its instruction-tuned nature.
- Efficient Deployment: Its relatively smaller size (3.2B parameters) combined with efficient training makes it a candidate for applications where faster inference or reduced resource consumption is critical.