wesamoyo-293M-MoE

language: en
license: apache-2.0
tags:
- moe
- mixture-of-experts
- transformer
- untrained
- base-model
- llm
- language-model
- text-generation
- 293M
- houndtid
- wesamoyo
base_model: false
inference: false
pipeline_tag: text-generation
company: Houndtid Labs
author: Houndtid Labs AI Research Team
contact: [email protected]
---

# Wesamoyo-293M-MoE

**293 Million Parameter MoE Transformer Architecture**  
*Efficient Mixture-of-Experts Foundation Model*

## Overview

Wesamoyo-293M-MoE is a 293-million parameter Mixture-of-Experts transformer architecture. The model ships with **initialized weights** - ready for training from scratch or fine-tuning.

### Key Architecture Features
- **Mixture-of-Experts**: 64 total experts, 6 activated per token
- **MLA Attention**: Multi-head latent attention
- **Extended Context**: 16,384 token sequence length
- **BF16 Precision**: Optimized for training
- **Custom Architecture**: Proprietary transformer design

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## 🚀 Official SDK Installation

Install the official Wesamoyo SDK for seamless model loading:

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## Model Loading

### Method 1: Official SDK (Recommended)

### Method 3: Hugging Face with SDK

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## Architecture Specifications

| Component | Specification | Description |
|-----------|--------------|-------------|
| **Model Type** | MoE Transformer | Mixture-of-Experts design |
| **Total Parameters** | 293 Million | Architecture capacity |
| **Experts** | 64 total, 6 active | MoE routing configuration |
| **Context Window** | 16,384 tokens | Extended sequence processing |
| **Transformer Blocks** | 6 layers | Network depth |
| **Hidden Dimension** | 512 | Feature representation size |
| **Attention Heads** | 8 | Parallel attention computation |
| **Vocabulary Size** | 16,384 tokens | Token dictionary |
| **Precision Support** | BF16 | Training optimization |
| **File Size** | 591 MB | Complete weights file |
| **SDK Version** | wesamoyo 1.0.4 | Official loading package |

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## Training Applications

### Language Processing
- Text generation and completion
- Document summarization
- Content creation and editing

### Research Development
- MoE routing optimization studies
- Attention mechanism experiments
- Training from scratch research
- Distributed training systems

### Educational Use
- Understanding MoE architectures
- Transformer model training
- Custom model development
- Research prototyping

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## Training Requirements

### Hardware Specifications
- **Minimum**: 8GB GPU memory
- **Recommended**: 16GB+ GPU (RTX 4080/4090, A100)
- **Storage**: 10GB+ for datasets
- **Network**: Standard internet connection

### Data Requirements
- **Training**: 1B+ tokens for good results
- **Fine-tuning**: 100K-1M examples
- **Validation**: 5-10% holdout sets

### Training Timeline
- **Setup**: 1-2 hours
- **Training**: 1-7 days (depending on data)
- **Evaluation**: Ongoing validation

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## Quick Start Training with SDK

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## Important Notes

### Model State
- **Weights are initialized** - Ready for training
- **Untrained but functional** - Random but valid parameter values
- **Complete architecture** - All 293M parameters included
- **Community resource** - Open for experimentation

### Efficiency Notes
- **Active Parameters**: ~20M per forward pass (MoE efficiency)
- **Memory Efficient**: 591MB total, optimized loading
- **Training Ready**: Proper initialization for stable training
- **SDK Optimized**: Official package for best performance

### Expected Output
- Untrained models produce coherent but random outputs
- Meaningful generation requires training
- Performance scales with data quality
- Specialization improves with fine-tuning

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## Technical Support

### Official SDK Documentation

### Contact Information
- **Organization**: Houndtid Labs
- **Contact**: [email protected]
- **Repository**: https://huggingface.co/HoundtidLabs/wesamoyo-293M-MoE
- **SDK PyPI**: https://pypi.org/project/wesamoyo/

### License Information
- **SDK Package**: Apache 2.0 License
- **Model Weights**: Available for research and commercial use
- **Architecture**: Proprietary design, SDK access provided

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## Version Information

**Current Version**: 1.0  
**Release Date**: 18 January 2026  
**Architecture**: Wesamoyo Transformer (293M MoE)  
**Official SDK**: wesamoyo 1.0.5  
**Status**: Base model - initialized weights ready for training

*Use the official `wesamoyo` SDK for seamless loading and training experience.*

Documentation updated: 18 January 2026