Srini18/DeepSeek-R1-Medical-COT
TEXT GENERATIONConcurrency Cost:1Model Size:8BQuant:FP8Ctx Length:32kPublished:Mar 18, 2025License:apache-2.0Architecture:Transformer Open Weights Cold
Srini18/DeepSeek-R1-Medical-COT is an 8 billion parameter Llama-based model developed by Srini18, fine-tuned from unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training. Its specific "Medical-COT" designation suggests an optimization for medical reasoning tasks, likely leveraging Chain-of-Thought capabilities.
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Overview
Srini18/DeepSeek-R1-Medical-COT is an 8 billion parameter language model, fine-tuned by Srini18. It is based on the unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit architecture, indicating its foundation in the Llama family of models.
Key Characteristics
- Parameter Count: 8 billion parameters.
- Base Model: Fine-tuned from
unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit. - Training Efficiency: The model was trained with Unsloth and Huggingface's TRL library, resulting in 2x faster training compared to conventional methods.
- Context Length: Supports a context length of 32768 tokens.
- Medical Focus: The "Medical-COT" in its name suggests a specialized fine-tuning for medical applications, potentially involving Chain-of-Thought (COT) reasoning for complex medical queries.
Potential Use Cases
- Medical Reasoning: Ideal for tasks requiring logical deduction and explanation in a medical context.
- Healthcare AI: Suitable for applications in medical information retrieval, clinical decision support, or medical question answering.
- Efficient Deployment: The use of Unsloth for training implies potential for efficient inference and deployment, making it suitable for resource-constrained environments.