Trendyol-LLM-7b-base-v0.1
| Property | Value |
|---|---|
| Parameter Count | 6.84B |
| Model Type | Text Generation |
| Architecture | LLaMA2-based Transformer |
| License | Apache 2.0 |
| Languages | Turkish, English |
What is Trendyol-LLM-7b-base-v0.1?
Trendyol-LLM-7b-base-v0.1 is an advanced bilingual language model developed by Trendyol, built upon the LLaMA2 7B architecture. This model represents a significant achievement in bringing powerful language capabilities to both Turkish and English text generation tasks. Fine-tuned on 10 billion tokens using LoRA (Low-Rank Adaptation) methodology, it offers an efficient and powerful solution for various text generation applications.
Implementation Details
The model implements a sophisticated LoRA training approach with carefully tuned hyperparameters, including a learning rate of 2e-4, LoRA rank of 64, and alpha of 128. It utilizes specific trainable components including q_proj, v_proj, k_proj, o_proj, gate_proj, down_proj, and up_proj, optimizing performance while maintaining efficiency.
- Maximum sequence length of 1024 tokens
- FP16 precision for optimal performance
- Implements dropout rate of 0.05 for regularization
- Includes specialized modules for embed_tokens and lm_head
Core Capabilities
- Bilingual text generation in Turkish and English
- Auto-regressive language modeling
- Optimized transformer architecture for efficient processing
- Support for various text generation tasks
- Integration with popular deep learning frameworks
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized bilingual capabilities and efficient implementation using LoRA fine-tuning, making it particularly valuable for Turkish and English language tasks while maintaining reasonable computational requirements.
Q: What are the recommended use cases?
The model is well-suited for text generation tasks, content creation, and language processing applications that require understanding of both Turkish and English. However, users should implement appropriate safety measures and human oversight, especially for public-facing applications.
