The problem IF ASSIST solves

1. Data Security and Privacy
   Challenge: Cloud-based LLMs process sensitive data externally, raising concerns about data leaks, privacy breaches, and compliance with regulations.
   Solution: Our offline LLMs keep data within the user’s controlled environment. No external servers handle the information, ensuring robust security and compliance.

2. Customization and Adaptability
   Challenge: Cloud LLMs offer limited customization options. Users cannot fine-tune models to specific domains or adapt them to unique contexts.
   Solution: Our offline LLMs allow fine-tuning on domain-specific data. Users can tailor the model to their needs, enhancing accuracy and relevance.

3. Network Reliability and Latency
   Challenge: Cloud services rely on internet connectivity. Network outages or slow connections disrupt LLM functionality.
   Solution: Our offline LLMs operate independently of external networks. Users can work seamlessly even in low-bandwidth environments.

4. Cost Savings
   Challenge: Cloud LLMs incur ongoing costs based on usage. Frequent API calls can become expensive.
   Solution: Our offline LLMs eliminate recurring expenses. Users pay upfront for hardware but save significantly in the long run.

Key Features of Our Offline LLMs

Local Deployment:
Users can install our LLMs on their servers, laptops, or edge devices.
No reliance on external APIs or cloud platforms.

Fine-Tuning Capabilities:
Users can fine-tune the model using their own data.
Customization for specific tasks, jargon, or industry-specific language.

Zero Latency:
No delays due to network communication.

Data Isolation:
Sensitive data remains within the user’s environment.
Compliance with data protection regulations.

One-Time Investment:
Upfront hardware costs, but no ongoing fees.
Ideal for organizations seeking cost-effective solutions.

Challenges we ran into

Making it Multilingual. Training the model with our existing model was the major challenge, confusion between whether to choose GGUF or Safe - tensor based model, proper quantisation method or scale, choose the parameter size of models.