spar-project/Llama-3.2-3B-Instruct-mlp-layers
TEXT GENERATIONConcurrency Cost:1Model Size:3.2BQuant:BF16Ctx Length:32kPublished:Mar 25, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
The spar-project/Llama-3.2-3B-Instruct-mlp-layers is a 3.2 billion parameter instruction-tuned Llama model developed by spar-project. This model was finetuned from unsloth/Llama-3.2-3B-Instruct using Unsloth and Huggingface's TRL library, enabling 2x faster training. It features a 32768 token context length, making it suitable for applications requiring efficient processing of longer sequences.
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Overview
The spar-project/Llama-3.2-3B-Instruct-mlp-layers is a 3.2 billion parameter instruction-tuned language model. It is based on the Llama architecture and was developed by spar-project, building upon the unsloth/Llama-3.2-3B-Instruct model.
Key Characteristics
- Efficient Training: This model was finetuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process compared to standard methods.
- Instruction-Tuned: Designed to follow instructions effectively, making it suitable for a variety of conversational and task-oriented applications.
- Context Length: Features a substantial context window of 32768 tokens, allowing it to process and understand longer inputs and generate coherent, extended responses.
Good For
- Resource-Efficient Applications: Its 3.2 billion parameter size makes it a good choice for deployments where computational resources are a consideration, while still offering strong performance.
- Instruction Following: Ideal for tasks requiring the model to adhere to specific prompts and instructions, such as question answering, summarization, and content generation.
- Long Context Tasks: Well-suited for use cases that benefit from a large context window, including analyzing lengthy documents, maintaining extended conversations, or generating detailed narratives.