huihui-ai/Llama-3.2-3B-Instruct-abliterated-finetuned
Llama-3.2-3B-Instruct-abliterated-finetuned by huihui-ai is a 3.2 billion parameter instruction-tuned causal language model, fine-tuned from Llama-3.2-3B-Instruct-abliterated. It features a 32768 token context length and shows improved performance on benchmarks like IF_Eval, BBH, and GPQA compared to its base model. This model is designed for general instruction-following tasks, demonstrating enhanced reasoning and question-answering capabilities.
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Model Overview
The huihui-ai/Llama-3.2-3B-Instruct-abliterated-finetuned is a 3.2 billion parameter instruction-tuned language model, building upon the Llama-3.2-3B-Instruct-abliterated base. This fine-tuned version aims to enhance performance across various benchmarks, particularly in instruction-following and reasoning tasks. It maintains a substantial context length of 32768 tokens, making it suitable for processing longer inputs.
Key Capabilities & Performance
This model demonstrates notable improvements in specific evaluation metrics:
- IF_Eval: Achieves 77.80, surpassing both the base
Llama-3.2-3B-Instruct(76.55) andLlama-3.2-3B-Instruct-abliterated(76.76). - BBH (Big-Bench Hard): Scores 42.20, an improvement over the base models (41.81 and 41.86).
- GPQA: Reaches 28.74, outperforming the previous versions (28.39 and 28.41).
While showing gains in these areas, its performance on MMLU Pro and TruthfulQA is slightly lower than the abliterated base model. The model is designed for general instruction-following, where its enhanced reasoning on complex tasks (BBH, GPQA) and instruction adherence (IF_Eval) can be beneficial.
Usage Notes
Users can clear the conversation and retry if the initial output is not satisfactory. The evaluation script used for these benchmarks is available within the model's repository.