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

# Model Card for Model ID

```python
import torch
from torch import nn
import random
from transformers import AutoModelForCausalLM, AutoTokenizer

# make sure the model doesn't generate mask tokens
bias = torch.zeros(34048)
bias[32000:] = -100
model.lm_head.bias = nn.Parameter(bias)

# --------------------------------------------------------------------------------
# Generation without masking
input_ids = tokenizer("Once upon a time, in a land far far away...", return_tensors='pt').input_ids
print(input_ids)
# tensor([[    1,  5713,  3714,   264,   727, 28725,   297,   264,  2533,  2082,
#          2082,  1753,  1101]])
output = model.generate(input_ids, max_new_tokens=64)
print(tokenizer.decode(output[0]))
# '<s> Once upon a time, in a land far far away...\n\nThere was a magical place called Disneyland.\n\nIt was a place where dreams came true, where fairy tales became reality, and where magic was all around.\n\nBut one day, something terrible happened.\n\nThe magic began to fade.\n\nThe fairy tales became dull, the'

# --------------------------------------------------------------------------------
# replace "far far" with two random indices instead (anything after 32k up to 34,048)
# the model should pick up that two repeating words after "Once upon a time, in a land-"
# and before "away" would probably be "far far"

input_ids[input_ids==2082] = 32_001
print(input_ids)
# tensor([[    1,  5713,  3714,   264,   727, 28725,   297,   264,  2533, 32001,
#         32001,  1753,  1101]])
output = model.generate(input_ids, max_new_tokens=64)
print(tokenizer.decode(output[0]))
# '<s> Once upon a time, in a land<ID-000001><ID-000001> away...\n\nOnce upon a time, in a land far, far away, there was a magical kingdom called Flanders. It was a peaceful land, where everyone lived happily ever after.\n\nBut one day, a terrible thing happened. A terrible, terrible thing.\n\nA terrible, terrible thing happened.'

# --------------------------------------------------------------------------------
# we can also get rid of everything except "<s>", "Once", "upon", "away", "..."
def create_masked_ids(input_ids, token_offset, ids_to_mask):
    unique_ids = torch.unique(input_ids).tolist()
    unique_id_map = random.sample([i for i in range(2048)], len(unique_ids))

    id_to_shuffled = {id: shuffled for id, shuffled in zip(unique_ids, unique_id_map)}

    def map_to_shuffled(id):
        return id_to_shuffled[id] + token_offset

    shuffled_ids = input_ids.clone().apply_(map_to_shuffled)

    mask = torch.zeros_like(input_ids, dtype=torch.bool)
    for id_to_mask in ids_to_mask:
        mask |= (input_ids == id_to_mask)

    masked_ids = torch.where(mask, input_ids, shuffled_ids)

    return masked_ids

masked_ids = create_masked_ids(input_ids, 32_000, [1, 5713, 3714, 1753, 1101])
print(masked_ids)
# tensor([[    1,  5713,  3714, 33048, 34032, 32238, 32016, 33048, 33013, 33299,
#         33299,  1753,  1101]])

output = model.generate(masked_ids, max_new_tokens=64)
print(tokenizer.decode(output[0]))
# '<s> Once upon<ID-000418><ID-0007F0><ID-0000EE><ID-000010><ID-000418><ID-0003F5><ID-000513><ID-000513> away...\n\nOnce upon a time, there was a young man named Alex. He was a very curious young man, and loved to explore the world around him. One day, he stumbled upon a magical book called "The Book of Secrets." This book contained all sorts of secrets about the world, and Alex was fasc'

```

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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