safetensors qwen2 en ko ja id zh my bn km mn ne ru uz tl pt si vi dataset:FINGU-AI/llm_evaluation_full_dataset_unique_both_directions_7_to_10_scores license:apache-2.0 compressed-tensors region:us
---
datasets:
- FINGU-AI/llm_evaluation_full_dataset_unique_both_directions_7_to_10_scores
language:
- en
- ko
- ja
- id
- zh
- my
- bn
- km
- mn
- ne
- ru
- uz
- tl
- pt
- si
- vi
license: apache-2.0
---
# QWEN2.5-32B-2600s-FP8: Advanced Multilingual Translation Model

## Overview
**FINGU-AI/QWEN2.5-32B-2600s-FP8** is a fine-tuned version of Qwen 2.5 32B, specifically optimized for multilingual translation across **16 different languages**. This model has been extensively fine-tuned to enhance its translation capabilities, making it competitive with high-tier models like 72B in terms of translation accuracy and fluency.

## Fine-Tuning Process
### Data Collection
To improve the model's understanding and translation capabilities, we curated and synthesized a large dataset consisting of:
- High-quality multilingual conversational datasets.
- Real-world dialogues spanning general, business, and technical domains.
- Translated datasets covering diverse linguistic structures and idiomatic expressions.

### Multilingual Enhancement
To advance its translation capabilities, we leveraged:
- **Translation Expansion**: The collected dataset was translated into **16 different languages** to ensure robust multilingual performance.
- **Benchmarking Against High-Tier Models**: We utilized state-of-the-art translation models, including **Gemini** and other top-ranking translation models with high BLEU and COMET scores, to refine our translation quality.
- **Reinforcement Learning with Human Feedback (RLHF)**: Translation outputs were evaluated and iteratively improved based on feedback from native speakers and linguistic experts.

### Training and Optimization
- **Base Model**: Qwen 2.5 32B FP8
- **Fine-Tuning Framework**: LoRA + QLoRA for efficient training
- **Batch Size**: Optimized for multi-GPU environments
- **Precision**: FP8 for efficient computation without sacrificing performance
- **Training Iterations**: Over 2600 steps on **multi-H100 GPUs**

## Key Improvements
- **Enhanced Multilingual Translation**: The model now achieves translation fluency comparable to 72B models across multiple language pairs.
- **Diverse Conversational Understanding**: Improved ability to process and generate accurate translations for various contexts, including business, casual, and formal speech.
- **Optimized for Low-Latency Inference**: Fine-tuned with efficiency in mind, making it suitable for real-time translation applications.

## Performance Evaluation
The model was evaluated using:
- **BLEU, COMET, and chrF scores**: To measure translation quality across multiple languages.
- **Human Evaluation**: Involving bilingual speakers and linguistic professionals to validate accuracy and fluency.
- **Comparisons with SOTA Models**: Benchmarked against high-performance models like GPT-4, Gemini, and LLaMA-3 to ensure top-tier translation quality.

## Usage
This model is suitable for:
- High-quality machine translation across multiple languages
- Conversational AI with multilingual capabilities
- Cross-lingual content generation and customer support
- NLP applications requiring robust and accurate translation

## Limitations
- While translation quality is highly competitive, niche dialects or highly technical documents may require additional fine-tuning.
- Performance may vary slightly depending on the deployment environment and inference settings.

## Citation
If you use this model, please cite:
```
@misc{FINGU-AI-QWEN2.5-32B-2600s-FP8,
  author = {FINGU-AI},
  title = {FINGU-AI/QWEN2.5-32B-2600s-FP8: Advanced Multilingual Translation Model},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/FINGU-AI/QWEN2.5-32B-2600s-FP8}
}
```

---
### License
This model follows the licensing terms of the original Qwen 2.5 32B model. Ensure compliance with regional translation regulations before deploying in production environments.