gyanbhambhani/oss-jev-qwen05-decision-3k
The gyanbhambhani/oss-jev-qwen05-decision-3k model is a 0.5 billion parameter instruction-tuned language model, fine-tuned from Qwen/Qwen2.5-0.5B-Instruct. It was trained with a learning rate of 2e-05 over 5400 steps, achieving a validation loss of 0.6189. This model is suitable for tasks requiring a compact, efficient language model with a 32768 token context length, building upon the Qwen2.5 architecture.
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Overview
The gyanbhambhani/oss-jev-qwen05-decision-3k is a compact, instruction-tuned language model with 0.5 billion parameters. It is a fine-tuned variant of the Qwen/Qwen2.5-0.5B-Instruct base model, leveraging the Qwen2.5 architecture. This model was trained for 5400 steps using a learning rate of 2e-05 and achieved a validation loss of 0.6189.
Key Characteristics
- Base Model: Fine-tuned from Qwen/Qwen2.5-0.5B-Instruct.
- Parameter Count: 0.5 billion parameters, making it suitable for resource-constrained environments.
- Context Length: Supports a substantial context window of 32768 tokens.
- Training Details: Utilized an AdamW optimizer with specific hyperparameters, including a learning rate of 2e-05 and a batch size of 2.
Good for
- Efficient Inference: Its small size allows for faster inference and deployment on edge devices or applications with limited computational resources.
- Instruction-Following Tasks: Benefits from its instruction-tuned base, making it capable of understanding and executing various commands.
- Applications Requiring Long Context: The 32768 token context length is advantageous for tasks involving extensive text processing or understanding long documents.