TinyLlama-1.1B-Chat-v0.6
| Property | Value |
|---|---|
| Parameter Count | 1.1B |
| License | Apache 2.0 |
| Tensor Type | BF16 |
| Training Data | SlimPajama-627B, StarCoderData, OpenAssistant |
What is TinyLlama-1.1B-Chat-v0.6?
TinyLlama-1.1B-Chat-v0.6 is a compact yet powerful language model that adopts the Llama 2 architecture while maintaining a significantly smaller footprint. This chat-optimized version is specifically fine-tuned on the UltraChat dataset and further aligned using DPO training on UltraFeedback data, making it particularly suitable for conversational applications with limited computational resources.
Implementation Details
The model is built on the same architecture as Llama 2, enabling seamless integration with existing Llama-based projects. It utilizes BF16 precision and requires transformers >= 4.34 for implementation. The training process involved initial pretraining on 3 trillion tokens, followed by specialized chat fine-tuning.
- Compatible with Llama 2 ecosystem
- Optimized for 16 A100-40G GPUs during training
- Implements chat templating for message formatting
- Supports efficient inference with device mapping
Core Capabilities
- Conversational AI applications
- Resource-efficient deployment
- Text generation with customizable parameters
- System prompt compatibility
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its ability to deliver Llama 2-like capabilities in a compact 1.1B parameter package, making it ideal for applications with limited computational resources while maintaining high-quality output.
Q: What are the recommended use cases?
The model is best suited for chatbot applications, text generation tasks, and scenarios where computational efficiency is crucial while maintaining reasonable performance. It's particularly valuable for deployments on edge devices or environments with limited resources.
