---
library_name: transformers
tags: []
---

# Model Card for Model ID

this model isn't really made for benchmarks, it's worse on everything besides ARC-C and TruthfulQA

| Model                                                        | ARC-C     | HellaSwag | MMLU       | TruthfulQA | Winogrande | GSM8k     |
| ------------------------------------------------------------ | --------- | --------- | ---------- | ---------- | ---------- | --------- |
| [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) | 59.98     | **83.31** | **64.16** | 42.15      | **78.37**  | **37.83** |
| [crumb/92d52f-ame-full-7B](https://hf.co/crumb/92d52f-ame-full-7B) | **61.18** | 81.52     | 63.44      | **42.39**  | 77.58      | 35.41     |

it's got extra tokens which can all equally be used as masks, you can replace all instances of one token in context with one of the extra tokens (`[f'<ID-{i:06X}>' for i in range(2048)]`) to give the model an extra hard time. it was trained with context length 2048 on three separate replacement techniques through a schedule, with 80% of all sequences being completely replaced with the mask tokens near the end of training. it was trained over ~0.5B tokens

> what? how is that useful?

i'm hoping to finetune it further while replacing the entire tokenizer with any number of other tokenizers, all utilizing the unique mask ids, to hopefully build a causal model of any sufficiently long artifact from any domain, for example, the voynich manuscript or an alien artifact

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