alimandela/logistics-extractor-gemma3-270m
TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:0.3BQuant:BF16Context Size:32kPublished:Jul 24, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The alimandela/logistics-extractor-gemma3-270m is a 0.3 billion parameter Gemma3-based causal language model developed by alimandela. This instruction-tuned model was fine-tuned using Unsloth and Huggingface's TRL library, enabling faster training. It is specifically designed for logistics extraction tasks, leveraging its compact size and optimized training for efficient performance in specialized applications.
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Model Overview
The alimandela/logistics-extractor-gemma3-270m is a compact 0.3 billion parameter language model, fine-tuned from the unsloth/gemma-3-270m-it base model. Developed by alimandela, this model is specifically optimized for logistics extraction tasks.
Key Capabilities
- Efficient Fine-tuning: The model was fine-tuned using Unsloth and Huggingface's TRL library, which enabled a 2x faster training process.
- Specialized for Logistics: Its training is geared towards extracting information relevant to logistics, making it suitable for domain-specific applications.
- Compact Size: With 0.3 billion parameters, it offers a balance between performance and computational efficiency, ideal for deployment in resource-constrained environments or for tasks requiring quick inference.
Good For
- Logistics Data Extraction: Ideal for parsing and extracting key information from logistics-related texts.
- Efficient Deployment: Its small parameter count makes it suitable for applications where computational resources are limited or fast inference is critical.
- Domain-Specific NLP: Users looking for a specialized model for a particular niche within logistics will find this model beneficial.