---
license: apache-2.0
datasets:
- HuggingFaceFW/finewiki
metrics:
- accuracy
base_model:
- PaddlePaddle/PaddleOCR-VL
new_version: OpenTrouter/Trouter-Terminus-20b
pipeline_tag: text-generation
library_name: adapter-transformers
tags:
- agent
- code
---
# Trouter-20B
*A powerful 20 billion parameter language model for advanced natural language processing*
[🤗 Model Card](https://huggingface.co/Trouter-Library/Trouter-20B) | [📖 Documentation](./USAGE_GUIDE.md)
---
## 📋 Table of Contents
- [Overview](#overview)
- [Key Features](#key-features)
- [Quick Start](#quick-start)
- [Model Details](#model-details)
- [Performance](#performance)
- [Use Cases](#use-cases)
- [System Requirements](#system-requirements)
- [Training Details](#training-details)
- [Limitations & Bias](#limitations--bias)
- [License](#license)
- [Citation](#citation)
- [Acknowledgments](#acknowledgments)
## 🎯 Overview
Trouter-20B is a state-of-the-art decoder-only transformer language model with 20 billion parameters. Designed for versatility and performance, it excels at a wide range of natural language understanding and generation tasks including reasoning, question answering, creative writing, code generation, and conversational AI.
## ✨ Key Features
- **20B Parameters**: Optimal balance between performance and computational efficiency
- **4K Context Length**: Process and generate longer sequences with 4096 token context window
- **Apache 2.0 License**: Fully open for commercial and research use
- **Optimized Architecture**: Efficient attention mechanisms with GQA (Grouped Query Attention)
- **Multi-lingual Capable**: Strong performance on English with support for multiple languages
- **Quantization Ready**: Compatible with 8-bit and 4-bit quantization for reduced memory footprint
- **Chat Optimized**: Built-in chat template for conversational applications
## 🚀 Quick Start
### Installation
```bash
pip install transformers>=4.38.0 torch>=2.0.0 accelerate bitsandbytes
```
### Basic Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# Load model and tokenizer
model_id = "Trouter-Library/Trouter-20B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto"
)
# Generate text
prompt = "Explain the concept of neural networks:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
### Memory-Efficient Loading (4-bit)
```python
from transformers import BitsAndBytesConfig
# Configure 4-bit quantization
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16
)
model = AutoModelForCausalLM.from_pretrained(
model_id,
quantization_config=bnb_config,
device_map="auto"
)
```
For more detailed usage examples, see the [Usage Guide](./USAGE_GUIDE.md).
## 📊 Model Details
| Specification | Value |
|--------------|-------|
| **Parameters** | 20 billion |
| **Architecture** | Decoder-only Transformer |
| **Layers** | 48 |
| **Hidden Size** | 5120 |
| **Attention Heads** | 40 (8 KV heads with GQA) |
| **Context Length** | 4096 tokens |
| **Vocabulary Size** | 32,000 tokens |
| **Activation** | SiLU (Swish) |
| **Positional Encoding** | RoPE (Rotary Position Embedding) |
| **Normalization** | RMSNorm |
| **Precision** | BFloat16 |
## 📈 Performance
### Benchmark Results
| Benchmark | Score | Notes |
|-----------|-------|-------|
| MMLU (5-shot) | TBD | Multitask Language Understanding |
| HellaSwag | TBD | Commonsense Reasoning |
| TruthfulQA | TBD | Truthfulness & Accuracy |
| HumanEval | TBD | Code Generation |
| GSM8K | TBD | Mathematical Reasoning |
| BBH | TBD | Big Bench Hard |
*Benchmarks to be updated after comprehensive evaluation*
### Inference Speed
| Configuration | Tokens/Second | Memory Usage |
|--------------|---------------|--------------|
| BF16 (A100 80GB) | ~XX tokens/s | ~40GB |
| 8-bit (A100 40GB) | ~XX tokens/s | ~20GB |
| 4-bit (RTX 4090) | ~XX tokens/s | ~10GB |
## 💡 Use Cases
### ✅ Recommended Uses
- **Text Generation**: Articles, stories, creative writing
- **Question Answering**: Information retrieval and explanation
- **Code Assistance**: Code completion, debugging, explanation
- **Summarization**: Document and conversation summarization
- **Translation**: Multi-language translation tasks
- **Dialogue Systems**: Chatbots and conversational AI
- **Content Analysis**: Sentiment analysis, classification
- **Educational Tools**: Tutoring and learning assistance
### ⚠️ Limitations
- May generate incorrect or nonsensical information (hallucinations)
- Not suitable for high-stakes decision making without human oversight
- Performance may vary on specialized or domain-specific tasks
- Requires careful prompt engineering for optimal results
- May reflect biases present in training data
### ❌ Out of Scope
- Real-time medical diagnosis or treatment recommendations
- Legal advice or binding interpretations
- Financial investment decisions
- Safety-critical systems without human verification
- Generating harmful, illegal, or unethical content
## 💻 System Requirements
### Minimum Requirements
- **GPU**: 24GB VRAM (with 4-bit quantization)
- **RAM**: 32GB system memory
- **Storage**: 50GB free space
- **CUDA**: 11.8 or higher
### Recommended Specifications
- **GPU**: A100 (40GB/80GB) or H100
- **RAM**: 64GB+ system memory
- **Storage**: 100GB+ SSD
- **Multi-GPU**: Supported via `device_map="auto"`
## 🏋️ Training Details
### Training Data
Trouter-20B was trained on a diverse corpus of high-quality text data including:
- Web documents and articles
- Books and academic papers
- Code repositories
- Conversational data
- Multilingual text
**Total Training Tokens**: [Specify total tokens]
**Data Mix**: [Provide breakdown of data sources]
**Cutoff Date**: January 2025
### Training Infrastructure
- **Framework**: PyTorch 2.0+ with FSDP
- **Hardware**: [Specify GPU cluster details]
- **Training Time**: [Specify duration]
- **Optimizer**: AdamW
- **Learning Rate**: Cosine schedule with warmup
- **Batch Size**: [Specify effective batch size]
- **Sequence Length**: 4096 tokens
### Training Objective
Causal language modeling with next-token prediction using cross-entropy loss.
## ⚖️ Limitations & Bias
### Known Limitations
1. **Hallucinations**: May generate plausible-sounding but incorrect information
2. **Temporal Knowledge**: Training data cutoff is January 2025
3. **Mathematical Reasoning**: May struggle with complex multi-step calculations
4. **Multilingual Performance**: Optimized for English; other languages may have reduced quality
5. **Context Window**: Limited to 4096 tokens
### Bias Considerations
Like all large language models, Trouter-20B may exhibit biases including:
- Gender, racial, and cultural biases from training data
- Western/English-centric perspective
- Potential stereotyping in generated content
**Mitigation Efforts**: We encourage users to:
- Implement appropriate content filtering
- Use diverse evaluation datasets
- Apply bias detection tools
- Provide human oversight for production deployments
## 📜 License
Trouter-20B is released under the **Apache 2.0 License**. You are free to:
✅ Use commercially
✅ Modify and distribute
✅ Use privately
✅ Use for patent purposes
See [LICENSE](./LICENSE) file for full terms.
## 📝 Citation
If you use Trouter-20B in your research or applications, please cite:
```bibtex
@software{trouter20b2025,
title={Trouter-20B: A 20 Billion Parameter Language Model},
author={Trouter-Library},
year={2025},
month={10},
url={https://huggingface.co/Trouter-Library/Trouter-20B},
version={1.0},
license={Apache-2.0}
}
```
## 🙏 Acknowledgments
We thank the open-source community and the following projects that made this work possible:
- [Hugging Face Transformers](https://github.com/huggingface/transformers)
- [PyTorch](https://pytorch.org/)
- [LLaMA](https://ai.meta.com/llama/) architecture inspiration
- [EleutherAI](https://www.eleuther.ai/) for evaluation frameworks
---
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