Falcon-7B-Instruct
| Property | Value |
|---|---|
| Parameter Count | 7.22B |
| License | Apache 2.0 |
| Developer | TII (Technology Innovation Institute) |
| Training Data | 250M tokens mixture of instruct/chat datasets |
| Architecture | Causal decoder-only with FlashAttention |
What is falcon-7b-instruct?
Falcon-7B-Instruct is a powerful language model fine-tuned specifically for instruction-following and chat applications. Built upon the strong foundation of Falcon-7B, this model has been optimized through training on a carefully curated mixture of chat and instruct datasets totaling 250M tokens.
Implementation Details
The model employs a sophisticated architecture featuring 32 layers with a dimensional model size of 4544 and utilizes advanced techniques like FlashAttention and multiquery attention mechanisms. It requires a minimum of 16GB memory for inference and is optimized for PyTorch 2.0.
- Implements rotary positional embeddings
- Uses parallel attention/MLP with single layer norm
- Supports sequence lengths up to 2048 tokens
- Trained on AWS SageMaker using 32 A100 40GB GPUs
Core Capabilities
- Excels at chat and instruction-following tasks
- Supports both English and French languages
- Outperforms comparable open-source models in its class
- Optimized for efficient inference with FlashAttention
Frequently Asked Questions
Q: What makes this model unique?
The model combines the strong performance of Falcon-7B with specialized fine-tuning on instruction datasets, making it particularly effective for chat and instruction-following tasks while maintaining efficient inference through FlashAttention architecture.
Q: What are the recommended use cases?
The model is best suited for chat applications, instruction-following tasks, and general text generation. However, it's not recommended for production use without proper risk assessment or for further fine-tuning.
