llama-68m
| Property | Value |
|---|---|
| Parameter Count | 68M |
| Training Data | Wikipedia, C4-en, C4-realnewslike |
| Author | JackFram |
| Paper | SpecInfer Paper |
| Model Hub | Hugging Face |
What is llama-68m?
llama-68m is a lightweight language model architected similarly to LLaMA but with significantly fewer parameters. Developed specifically as a base Small Speculative Model for the SpecInfer research, it represents an efficient approach to language model deployment and experimentation.
Implementation Details
The model was trained on a diverse dataset combination including Wikipedia and portions of the C4-en and C4-realnewslike datasets. Its compact size of 68M parameters makes it particularly suitable for research in speculative inference and efficient model serving.
- Trained on multiple high-quality datasets
- Optimized for speculative inference research
- Implements LLaMA-like architecture at a smaller scale
Core Capabilities
- Functions as a base Small Speculative Model
- Suitable for research in efficient model serving
- Demonstrates potential for accelerated inference through speculation
Frequently Asked Questions
Q: What makes this model unique?
The model's primary uniqueness lies in its specific design for speculative inference research, combining the powerful LLaMA architecture with a very compact parameter count of 68M.
Q: What are the recommended use cases?
While no formal evaluation has been conducted, the model is primarily intended for research purposes, especially in the context of speculative inference and token tree verification as described in the SpecInfer paper.
