Phuc-HugigFace/Qwen3.5-2B-vi-sft-v2
Phuc-HugigFace/Qwen3.5-2B-vi-sft-v2 is a 2.3 billion parameter Qwen3.5-based language model developed by Phuc-HugigFace, fine-tuned for specific applications. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training speeds. It is designed for tasks requiring a compact yet efficient model with a 32768 token context length.
Loading preview...
Model Overview
Phuc-HugigFace/Qwen3.5-2B-vi-sft-v2 is a 2.3 billion parameter language model, fine-tuned by Phuc-HugigFace from the Qwen/Qwen3.5-2B-Base architecture. This model leverages the Qwen3.5 family's capabilities within a smaller parameter count, making it suitable for resource-constrained environments or applications requiring faster inference.
Key Training Details
- Base Model: Qwen/Qwen3.5-2B-Base
- Training Acceleration: Achieved 2x faster training speeds by utilizing Unsloth and Huggingface's TRL library.
- Context Length: Supports a substantial context window of 32768 tokens, allowing for processing longer inputs and maintaining conversational coherence over extended interactions.
Potential Use Cases
This model is particularly well-suited for scenarios where:
- Efficiency is critical: Its 2.3 billion parameters offer a balance between performance and computational cost.
- Fast deployment is desired: The optimized training process suggests a model that can be quickly adapted or integrated.
- Specific fine-tuned tasks: As a fine-tuned model, it is likely optimized for particular downstream applications, though the specific nature of the 'vi-sft' (Vietnamese supervised fine-tuning) is implied by the name.
Developers looking for a compact, Qwen3.5-based model with an extended context window and efficient training methodology may find this model beneficial for their applications.