sugavahan/Sentinel_tanglish_model
TEXT GENERATIONConcurrency Cost:1Model Size:8BQuant:FP8Ctx Length:8kPublished:Apr 22, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
Sentinel_tanglish_model by sugavahan is an 8 billion parameter instruction-tuned causal language model, fine-tuned from unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit. It was developed using Unsloth and Huggingface's TRL library, enabling 2x faster training. This model is optimized for tasks requiring efficient processing within an 8192-token context window, making it suitable for applications where rapid deployment and performance are key.
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Sentinel_tanglish_model Overview
Sentinel_tanglish_model is an 8 billion parameter instruction-tuned language model developed by sugavahan. It is fine-tuned from the unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit base model, leveraging the Unsloth library for accelerated training.
Key Capabilities
- Efficient Fine-tuning: Utilizes Unsloth for 2x faster training, making it suitable for rapid iteration and deployment.
- Llama 3.1 Base: Benefits from the strong foundational capabilities of the Llama 3.1 architecture.
- Instruction Following: Designed to follow instructions effectively, making it versatile for various NLP tasks.
Good for
- Rapid Prototyping: Ideal for developers needing to quickly fine-tune and deploy Llama 3.1-based models.
- Resource-Efficient Applications: Suitable for scenarios where faster training and efficient model deployment are critical.
- Instruction-based Tasks: Excels in applications requiring the model to understand and execute specific instructions.