December 11, 2024 | 10:00 AM - 2:30 PM PT
SmallCon is the first virtual conference dedicated to exploring the potential of Small Language Models (SLMs) in production environments. Industry leaders from prominent tech companies share insights, best practices, and real-world implementation experiences.
- Models under 3-4B parameters
- Deployable on laptops/mobile devices
- Sub-second inference times (0.1s target)
- Focused on specific, bounded tasks
- Cost-efficient scaling capabilities
- LoRA adaptation (60+ adapters per GPU)
- Hybrid deployment architectures
- API-first approaches
- Private VPC + managed scaling
- Continuous fine-tuning pipelines
- 10x cost reduction vs traditional approaches
- 8% higher F1 scores
- 80% higher throughput
- $20 per training cycle
- 85% organizational adoption rates
- Solar LLM family (Upstage)
- Hamba Language Model (1.5B params)
- Agent Force (Salesforce)
- Gretel Navigator
- Guardrails validation framework
- Keynote on SLM Future
- Enterprise Implementation Case Study
- Customer Service Analytics
- GenAI Future Panel
- Agentforce Platform Deep Dive
- Production AI Panel
- Synthetic Data Generation
- Solar LLMs Implementation
- Continuous Fine-tuning
- Model Evaluation Best Practices
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Initial Phase (2022-2023):
- API-based access
- Individual model scaling
- Basic prompting and RAG
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Current Phase (2024):
- Multi-adapter architectures
- Hybrid deployment models
- Continuous fine-tuning
- Integrated validation
- Human-in-the-loop evaluation
- Fact-checking modules
- Automated guardrails
- Continuous monitoring
- Performance metrics tracking
- Linear scaling with adapters
- Shared infrastructure
- Pay-per-call models
- Efficient fine-tuning
- Resource pooling
- Synthetic data generation
- Document processing pipelines
- Versioned datasets
- Quality-focused curation
- Privacy-preserving techniques
- Meta
- Hugging Face
- Mistral AI
- Salesforce
- Upstage
- NVIDIA
- DoorDash
- Marsh & McLennan
- Predibase
- Gretel
- Guardrails AI
- Enterprise adoption rates: 85%
- Request volumes: 25M annually
- Time saved: 1M+ hours
- Training costs: ~$20/cycle
- Inference times: 0.1s achieved
The conference highlighted the industry's rapid move toward practical, efficient AI implementations using small language models, with particular emphasis on reliability, cost-effectiveness, and real-world validation strategies.
Analysis of Compute vs. Disk Tradeoffs
In this number transformation problem, we face a classic tradeoff between computation time and disk space (or memory) usage. Here's a breakdown of the different approaches and their tradeoffs:
1. Brute Force (Pure Computation)
2. Full Memoization (In-memory)
3. Disk-Based Memoization
4. Hybrid Approach (Incremental Memoization)
Full Options
Brute Force:
Full In-memory Memoization:
Disk-based Memoization:
Incremental Memoization:
Variations of Incremental Memoization:
Choosing the Right Approach
The best approach depends on the specific constraints of your problem:
By carefully considering these factors, you can choose the most appropriate approach that balances compute time, memory usage, and disk space to efficiently solve the number transformation problem.