adityakc/airesume_extract_model
TEXT GENERATIONConcurrent Unit Cost:1Model Size:0.35BQuant:BF16Context Size:32kPublished:Jul 7, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The adityakc/airesume_extract_model is a 0.35 billion parameter language model developed by adityakc, fine-tuned from LiquidAI/LFM2-350M-Extract. This model is optimized for extraction tasks, leveraging efficient training with Unsloth. It is designed for applications requiring precise information retrieval from text, offering a context length of 32768 tokens.
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Model Overview
The adityakc/airesume_extract_model is a specialized 0.35 billion parameter language model, fine-tuned by adityakc. It is based on the LiquidAI/LFM2-350M-Extract architecture and was trained with significant efficiency improvements using Unsloth and Huggingface's TRL library, achieving a 2x faster training speed.
Key Capabilities
- Efficient Extraction: Optimized for information extraction tasks from textual data.
- Compact Size: With 0.35 billion parameters, it offers a balance between performance and resource efficiency.
- Extended Context: Supports a substantial context length of 32768 tokens, allowing for processing of longer documents.
- Fast Training: Benefits from Unsloth's optimizations for quicker fine-tuning and deployment.
Good For
- Resume Parsing: Ideal for extracting structured information from resumes.
- Document Analysis: Suitable for tasks requiring specific data points to be pulled from various documents.
- Resource-Constrained Environments: Its smaller parameter count makes it viable for deployment where computational resources are limited, while still providing strong extraction capabilities.