pvdhihihi/qwen-1.7b-sft
The pvdhihihi/qwen-1.7b-sft model is a 2 billion parameter language model, fine-tuned from Qwen/Qwen3-1.7B-Base. It was specifically trained on the HuggingFaceH4/deita-10k-v0-sft dataset, indicating an optimization for supervised fine-tuning tasks. This model is designed for applications requiring a compact yet capable language model for instruction-following or specific dialogue generation.
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Model Overview
The pvdhihihi/qwen-1.7b-sft model is a 2 billion parameter language model derived from the Qwen/Qwen3-1.7B-Base architecture. It has undergone supervised fine-tuning (SFT) using the HuggingFaceH4/deita-10k-v0-sft dataset.
Key Characteristics
- Base Model: Qwen3-1.7B-Base, a compact yet capable foundation model.
- Fine-tuning: Supervised fine-tuning on a specific instruction dataset, suggesting improved instruction-following capabilities.
- Parameter Count: 2 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a context length of 32768 tokens, enabling processing of longer inputs.
Training Details
The model was trained with a learning rate of 3e-05, a batch size of 32, and utilized a cosine learning rate scheduler with a 0.1 warmup ratio over 1 epoch. The training was distributed across 2 GPUs.
Potential Use Cases
This model is likely suitable for applications where a smaller, instruction-tuned model is beneficial, such as:
- Instruction Following: Generating responses based on explicit instructions.
- Dialogue Systems: Creating conversational agents for specific domains.
- Text Generation: Producing coherent and contextually relevant text for various tasks.
- Resource-Constrained Environments: Deploying LLM capabilities where computational resources are limited.