ClaudioSavelli/FAME_base_llama32-1b-instruct-qa
TEXT GENERATIONConcurrency Cost:1Model Size:1BQuant:BF16Ctx Length:32kPublished:Apr 2, 2026License:otherArchitecture:Transformer Cold
ClaudioSavelli/FAME_base_llama32-1b-instruct-qa is a 1 billion parameter instruction-tuned language model, fine-tuned by ClaudioSavelli for the FAME setting. Based on the Llama-3.2-1B-Instruct architecture, it is designed for question-answering tasks within this specific framework. With a context length of 32768 tokens, this model is optimized for processing and responding to queries in the FAME context.
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Model Overview
ClaudioSavelli/FAME_base_llama32-1b-instruct-qa is a 1 billion parameter instruction-tuned model, specifically fine-tuned for applications within the FAME setting. It is built upon the meta-llama/Llama-3.2-1B-Instruct architecture, indicating its foundation in the Llama family of models.
Key Characteristics
- Parameter Count: 1 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a substantial context window of 32768 tokens, enabling it to process longer inputs and maintain conversational coherence over extended interactions.
- Instruction-Tuned: Optimized through instruction tuning, making it suitable for following specific commands and generating targeted responses.
- FAME Setting Focus: The primary differentiator is its fine-tuning for the FAME (Framework for Automated Model Evaluation) setting, suggesting specialized performance in related tasks.
Potential Use Cases
- Question Answering: Given its instruction-tuned nature and FAME setting optimization, it is well-suited for question-answering tasks within that specific domain.
- Research and Development: Can be used by researchers exploring model performance and applications within the FAME framework, as detailed in its associated paper https://arxiv.org/pdf/2512.15235.