Llama-2-70b-instruct
| Property | Value |
|---|---|
| Developer | Upstage |
| Base Model | LLaMA-2 |
| License | CC BY-NC-4.0 |
| Training Infrastructure | A100x8 * 4 |
| Context Length | 10k+ tokens |
What is Llama-2-70b-instruct?
Llama-2-70b-instruct is a state-of-the-art language model developed by Upstage, built upon Meta's LLaMA-2 architecture. This model has been specifically fine-tuned on Orca-style datasets, achieving remarkable performance across various benchmarks including ARC-Challenge, HellaSwag, MMLU, and TruthfulQA. With its impressive average score of 72.3 on the Open LLM Leaderboard, it represents a significant advancement in instruction-tuned language models.
Implementation Details
The model leverages advanced techniques including dynamic rope scaling for handling extended context lengths beyond 10k tokens. It's implemented using HuggingFace Transformers and can be deployed using both 16-bit and 8-bit quantization for efficient inference.
- Utilizes DeepSpeed and HuggingFace Trainer/Accelerate for training
- Supports dynamic context length handling through rope_scaling
- Implements a specific prompt template for system, user, and assistant interactions
- Optimized for running on A100 GPUs
Core Capabilities
- Benchmark Performance: 70.9% on ARC-Challenge, 87.5% on HellaSwag
- Extended context handling (10k+ tokens)
- Multi-turn conversation support
- Instruction-following capabilities
- MT-Bench score of 7.24375
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its exceptional performance on benchmark tests and its ability to handle extremely long context windows, making it particularly suitable for complex, multi-turn conversations and detailed analysis tasks.
Q: What are the recommended use cases?
The model is ideal for instruction-following tasks, complex reasoning, and scenarios requiring extended context understanding. It's particularly well-suited for research and non-commercial applications due to its CC BY-NC-4.0 license.
