# SmallCon 2024: A Virtual Conference for GenAI Builders
Table of Contents
December 11, 2024 | 10:00 AM - 2:30 PM PT
[Overview](#file-smallcon-2024-summary-md) | [Performance Metrics](#file-ai-performance-metrics-md)
About SmallCon
A first-of-its-kind virtual conference focused on small language models (SLMs) and their practical implementation in enterprise environments. The event brought together industry leaders to share insights on deploying, scaling, and optimizing SLMs for production use cases.
Key Metrics & Achievements
- Model Size Target: Under 3-4 billion parameters
- Response Time: Sub-second inference (0.1s)
- Cost Efficiency: 10x reduction vs traditional approaches
- Performance: 8% higher F1 scores, 80% higher throughput
- Enterprise Success: 85% adoption rate, 1M+ hours saved
Featured Technologies
Solar LLM Family (Upstage)
- Solar Mini: Optimized for fine-tuning
- Solar Pro: Single-GPU deployment
[Full Session Details](#file-upstage-solar-llms-document-ai-md)
Hamba Language Model
- 1.5B parameters
- MMLU score: 50
[Panel Discussion](#file-small-models-panel-summary-md)
Agent Force (Salesforce)
- Low-code/no-code platform
- Focus on trust and guardrails
[Technical Deep Dive](#file-manji-ai-agents-md)
Core Sessions
Morning Sessions
Opening Keynote [10:00-10:15 AM PT]
- Speaker: Devvret Rishi (Predibase)
- Focus: The future of small language models
[Session Summary](#file-small-models-panel-summary-md)
Enterprise Implementation [10:15-10:35 AM PT]
- Speaker: Paul Beswick (Marsh & McLennan)
- Achievement: 25M annual requests, 85% adoption
[Session Summary](#file-beswick-fireside-chat-summary-md)
Technical Sessions
- Call Analytics at Scale (Converza) [Session Summary](#file-converza-case-study-summary-md)
- Future of GenAI Panel [Session Summary](#file-future-of-genai-md)
- Agent Force Platform (Salesforce) [Session Summary](#file-manji-ai-agents-md)
- Production AI Panel [Session Summary](#file-evaluating-llms-human-feedback-md)
Lightning Demos
- Synthetic Data (Gretel) [Session Summary](#file-gretel-synthetic-data-md)
- Solar LLMs (Upstage) [Session Summary](#file-upstage-solar-llms-document-ai-md)
- Continuous Fine-Tuning (Predibase) [Session Summary](#file-predibase-incremental-training-md)
- Model Evaluation (Guardrails AI) [Session Summary](#file-guardrails-mitigating-volatility-md)
Key Technical Themes
- Practical SLM implementation
- Fine-tuning and adaptation
- Synthetic data generation
- Model evaluation frameworks
- Continuous deployment
- Human feedback integration
Major Trends
- Shift to production-ready systems
- Focus on agentic workflows
- Emphasis on synthetic data
- Importance of evaluation
- Cost optimization strategies
Technical Priorities
[ ]60+ adapter architectures[ ]Synthetic data generation[ ]Continuous fine-tuning[ ]Evaluation frameworks[ ]Human feedback systems
Participating Organizations
- Meta
- Hugging Face
- Mistral AI
- Salesforce
- NVIDIA
- DoorDash
- Marsh & McLennan
- Predibase
- Gretel
- Guardrails AI
For detailed performance metrics and implementation details, see [Technical Metrics](#file-ai-performance-metrics-md).