tikeape/Llama-3.2-3B-Hunter-Alpha-Distill
TEXT GENERATIONConcurrency Cost:1Model Size:3.2BQuant:BF16Ctx Length:32kPublished:Mar 15, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Cold
The tikeape/Llama-3.2-3B-Hunter-Alpha-Distill is a 3.2 billion parameter Llama-based causal language model developed by tikeape, featuring a 32768 token context length. This model was fine-tuned from unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is designed for efficient deployment and performance, leveraging optimized training techniques for Llama architectures.
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Model Overview
The tikeape/Llama-3.2-3B-Hunter-Alpha-Distill is a 3.2 billion parameter language model, fine-tuned by tikeape. It is based on the Llama architecture and offers a substantial context length of 32768 tokens, making it suitable for processing longer sequences of text.
Key Characteristics
- Base Model: Fine-tuned from
unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit. - Optimized Training: The model was trained using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process compared to standard methods.
- Efficiency: The use of Unsloth suggests an emphasis on efficient resource utilization during training and potentially during inference, making it a good candidate for environments with computational constraints.
Potential Use Cases
- Efficient Inference: Its optimized training and relatively compact size (3.2B parameters) make it suitable for applications requiring faster inference times or deployment on less powerful hardware.
- Long Context Tasks: The 32768 token context length allows for handling tasks that involve extensive documents, detailed conversations, or complex code analysis.
- Instruction Following: As it's fine-tuned from an instruct model, it is likely well-suited for tasks requiring precise instruction adherence and conversational AI.