Llama-3-8B-Instruct-Gradient-1048k
| Property | Value |
|---|---|
| Parameter Count | 8.03B |
| Context Length | 1048k tokens |
| License | Llama3 |
| Base Model | Meta Llama-3 8B |
| Training Data | 830M tokens for context extension |
What is Llama-3-8B-Instruct-Gradient-1048k?
This model is an enhanced version of Meta's Llama-3 8B that extends the context length from 8k to over 1040k tokens. Developed by Gradient and powered by Crusoe Energy's compute resources, it demonstrates that state-of-the-art language models can effectively handle long contexts with minimal additional training. The model underwent training on 830M tokens for context extension and 1.4B tokens total across all stages.
Implementation Details
The model implements several technical innovations, including NTK-aware interpolation for RoPE theta initialization and progressive training on increasing context lengths. It utilizes the EasyContext Blockwise RingAttention library with custom parallelism implementations, achieving a 33x speedup in model training.
- Progressive context length training: 65K → 262K → 524k → 1048k
- Optimized RoPE theta scheduling for each training phase
- Custom network topology for efficient GPU cluster utilization
- Training data based on augmented SlimPajama dataset
Core Capabilities
- Extended context processing up to 1048k tokens
- Strong performance on retrieval and Q&A tasks
- Improved instruction-following abilities
- Efficient processing of long-form content
- Competitive performance against larger models on standard benchmarks
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to handle extremely long contexts (1048k tokens) while maintaining strong performance, achieved with minimal additional training (< 0.01% of Llama-3's original pre-training data), sets it apart from other models.
Q: What are the recommended use cases?
The model excels at tasks requiring long context understanding, including document analysis, extended conversations, and complex Q&A scenarios. It's particularly suited for applications needing to process large amounts of context while maintaining coherent responses.
