MegaBeam-Mistral-7B-512k
| Property | Value |
|---|---|
| Parameter Count | 7.24B |
| Model Type | Large Language Model |
| Context Length | 524,288 tokens |
| License | Apache 2.0 |
| Base Model | Mistral-7B Instruct-v0.2 |
What is MegaBeam-Mistral-7B-512k?
MegaBeam-Mistral-7B-512k is a state-of-the-art long-context language model that extends the capabilities of Mistral-7B to handle sequences of up to 524,288 tokens. Built on the Mistral-7B Instruct-v0.2 architecture, it represents a significant advancement in processing extended text contexts while maintaining high performance across various benchmarks.
Implementation Details
The model utilizes BF16 tensor types and can be deployed using frameworks like vLLM and Amazon SageMaker's DJL. It's optimized for both standard GPU configurations and high-memory instances, with specific deployment options for different hardware setups.
- Achieves 100% accuracy on the Needle In A Haystack (NIAH) benchmark
- Scores an impressive 88.70 average on RULER benchmark across different context lengths
- Demonstrates robust performance up to 128K tokens, maintaining above 82.8% accuracy
- Supports deployment on various AWS instance types including g5.48xlarge and p4d.24xlarge
Core Capabilities
- Extended Context Processing: Handles documents up to 524,288 tokens
- Multi-task Performance: Excels in retrieval, multi-hop tracing, aggregation, and QA tasks
- Code Understanding: Capable of processing large codebases and Git repositories
- Flexible Deployment: Supports various serving frameworks and cloud infrastructure
Frequently Asked Questions
Q: What makes this model unique?
MegaBeam-Mistral-7B-512k stands out for its exceptional context length handling capabilities while maintaining competitive performance across various benchmarks. It's particularly notable for achieving perfect scores on the NIAH benchmark and maintaining high performance even at extended context lengths.
Q: What are the recommended use cases?
The model excels in scenarios requiring long document processing, such as code repository analysis, extensive document comprehension, and complex question-answering tasks. It's particularly well-suited for developer onboarding, documentation analysis, and processing large technical documents.
