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JackFram/llama-68m
llama-68m is a text generation model from JackFram. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
This is a LLaMA-like model with only 68M parameters trained on Wikipedia and part of the C4-en and C4-realnewslike datasets.
Downloads · 30 days
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From the Hugging Face model README
This is a LLaMA-like model with only 68M parameters trained on Wikipedia and part of the C4-en and C4-realnewslike datasets.
No evaluation has been conducted yet, so use it with care.
The model is mainly developed as a base Small Speculative Model in the SpecInfer paper.
| Category | Benchmark | Metric | Score / Value | Status |
|---|---|---|---|---|
| Linguistics & Grammar | BLiMP | Accuracy | 70.57% | Success |
| Commonsense & Reasoning | PIQA | Normalized Accuracy | 59.25% | Success |
| BoolQ | Accuracy | 57.71% | Success | |
| COPA | Accuracy | 53.00% | Success | |
| WinoGrande | Accuracy | 50.59% | Success | |
| HellaSwag | Normalized Accuracy | 29.04% | Success | |
| RACE | Accuracy | 25.36% | Success | |
| CommonsenseQA | Accuracy | 19.82% | Success | |
| Academic & Knowledge | SciQ | Normalized Accuracy | 57.80% | Success |
| ARC-Easy | Normalized Accuracy | 35.98% | Success | |
| OpenBookQA | Normalized Accuracy | 25.60% | Success | |
| MMLU | Accuracy | 22.96% | Success | |
| ARC-Challenge | Normalized Accuracy | 22.87% | Success | |
| Language Modeling | TriviaQA | Accuracy | TriviaQA Standard | Success |
| LAMBADA | Accuracy | 13.24% | Success | |
| C4-Perplexity | Word Perplexity | 205.79 | Success | |
| WikiText-2 | Word Perplexity | 306.79 | Success |
Notes on Failed Tasks: The
ArithmeticandSocialIQAbenchmarks failed during execution due to runtime pipeline incompatibilities, yielding no score. Total evaluation runtime was 44.74 minutes.
To cite the model, please use
@misc{miao2023specinfer,
title={SpecInfer: Accelerating Generative LLM Serving with Speculative Inference and Token Tree Verification},
author={Xupeng Miao and Gabriele Oliaro and Zhihao Zhang and Xinhao Cheng and Zeyu Wang and Rae Ying Yee Wong and Zhuoming Chen and Daiyaan Arfeen and Reyna Abhyankar and Zhihao Jia},
year={2023},
eprint={2305.09781},
archivePrefix={arXiv},
primaryClass={cs.CL}
}