miniclaus-qw1.5B-UNAMGS
| Property | Value |
|---|---|
| Parameter Count | 1.78B |
| Model Type | Text Generation |
| Base Model | Qwen/Qwen2.5-1.5B-Instruct |
| License | Qwen License |
| Framework | Transformers 4.45.2 |
| Training Dataset | Magpie-Pro-MT-300K-v0.1 |
What is miniclaus-qw1.5B-UNAMGS?
miniclaus-qw1.5B-UNAMGS is a sophisticated language model derived from Qwen2.5-1.5B-Instruct, enhanced through careful fine-tuning using the Magpie-Pro-MT-300K-v0.1 dataset. The model implements MGS & UNA (MLP) techniques, achieving an impressive validation loss of 0.7193.
Implementation Details
The model was trained using a distributed multi-GPU setup across 8 devices, with a total batch size of 128. Training utilized the Adam optimizer with carefully tuned parameters (betas=0.9,0.999, epsilon=1e-08) over a single epoch, showing consistent improvement in loss metrics throughout the training process.
- BF16 tensor type for optimal performance
- Available in GGUF format for efficient deployment
- Trained using the Axolotl framework
- Implements advanced MGS & UNA techniques
Core Capabilities
- Text generation and conversational tasks
- Optimized for English language processing
- Efficient performance with 1.78B parameters
- Supports text-generation-inference endpoints
Frequently Asked Questions
Q: What makes this model unique?
The model combines the power of Qwen2.5 architecture with MGS & UNA techniques, achieving strong performance despite its relatively compact size of 1.78B parameters. The implementation of Magpie-Pro dataset training makes it particularly effective for specialized text generation tasks.
Q: What are the recommended use cases?
The model is well-suited for conversational AI applications, text generation tasks, and scenarios requiring efficient deployment due to its optimized size and GGUF format availability. It's particularly effective for English language processing tasks requiring a balance of performance and resource efficiency.
