Qwen-2.5-7B-DTF
| Property | Value |
|---|---|
| Base Model | Qwen-2.5-7B |
| Parameter Count | 7 Billion |
| Training Dataset | 75M tokens from DTF posts |
| Model URL | Hugging Face |
What is Qwen-2.5-7B-DTF?
Qwen-2.5-7B-DTF is a specialized language model that builds upon the Qwen2.5-7B architecture, fine-tuned specifically for DTF content using unsloth's low rank adaptation technique. The model incorporates a merged adapter and has been trained on a carefully curated dataset of DTF posts, filtered for optimal length between 1,000 and 128,000 tokens.
Implementation Details
The model utilizes LoRA (Low-Rank Adaptation) with specific hyperparameters including a rank of 32, targeting key projection layers (q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj). The training process employed advanced optimization techniques with AdamW 8-bit optimizer and cosine learning rate scheduling.
- Training Duration: ~8.5 hours on A100 80GB GPU, ~33.5 hours on RTX 3090ti
- Batch Size: 8 with 16 gradient accumulation steps
- Learning Rate: 5e-5 with cosine scheduling
- Weight Decay: 4e-2
- Training Epochs: 2
Core Capabilities
- Specialized DTF content generation and understanding
- Efficient processing with merged adapter weights
- Optimized for content between 1,000 and 128,000 tokens
- Enhanced performance through low-rank adaptation
Frequently Asked Questions
Q: What makes this model unique?
The model's uniqueness lies in its specialized training on DTF content combined with unsloth's low-rank adaptation technique, offering optimized performance for DTF-specific tasks while maintaining the base capabilities of Qwen-2.5-7B.
Q: What are the recommended use cases?
This model is best suited for DTF-related content generation, analysis, and processing tasks, particularly where understanding of DTF-specific context and language patterns is crucial.
