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--- |
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library_name: transformers |
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tags: [] |
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--- |
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# Model Card for Model ID |
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```python |
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import torch |
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from torch import nn |
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import random |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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# make sure the model doesn't generate mask tokens |
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bias = torch.zeros(34048) |
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bias[32000:] = -100 |
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model.lm_head.bias = nn.Parameter(bias) |
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# -------------------------------------------------------------------------------- |
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# Generation without masking |
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input_ids = tokenizer("Once upon a time, in a land far far away...", return_tensors='pt').input_ids |
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print(input_ids) |
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# tensor([[ 1, 5713, 3714, 264, 727, 28725, 297, 264, 2533, 2082, |
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# 2082, 1753, 1101]]) |
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output = model.generate(input_ids, max_new_tokens=64) |
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print(tokenizer.decode(output[0])) |
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# '<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' |
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# -------------------------------------------------------------------------------- |
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# replace "far far" with two random indices instead (anything after 32k up to 34,048) |
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# the model should pick up that two repeating words after "Once upon a time, in a land-" |
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# and before "away" would probably be "far far" |
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input_ids[input_ids==2082] = 32_001 |
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print(input_ids) |
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# tensor([[ 1, 5713, 3714, 264, 727, 28725, 297, 264, 2533, 32001, |
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# 32001, 1753, 1101]]) |
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output = model.generate(input_ids, max_new_tokens=64) |
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print(tokenizer.decode(output[0])) |
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# '<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.' |
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# -------------------------------------------------------------------------------- |
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# we can also get rid of everything except "<s>", "Once", "upon", "away", "..." |
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def create_masked_ids(input_ids, token_offset, ids_to_mask): |
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unique_ids = torch.unique(input_ids).tolist() |
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unique_id_map = random.sample([i for i in range(2048)], len(unique_ids)) |
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id_to_shuffled = {id: shuffled for id, shuffled in zip(unique_ids, unique_id_map)} |
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def map_to_shuffled(id): |
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return id_to_shuffled[id] + token_offset |
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shuffled_ids = input_ids.clone().apply_(map_to_shuffled) |
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mask = torch.zeros_like(input_ids, dtype=torch.bool) |
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for id_to_mask in ids_to_mask: |
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mask |= (input_ids == id_to_mask) |
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masked_ids = torch.where(mask, input_ids, shuffled_ids) |
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return masked_ids |
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masked_ids = create_masked_ids(input_ids, 32_000, [1, 5713, 3714, 1753, 1101]) |
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print(masked_ids) |
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# tensor([[ 1, 5713, 3714, 33048, 34032, 32238, 32016, 33048, 33013, 33299, |
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# 33299, 1753, 1101]]) |
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output = model.generate(masked_ids, max_new_tokens=64) |
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print(tokenizer.decode(output[0])) |
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# '<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' |
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``` |
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this model isn't really made for benchmarks, it's worse on everything besides ARC-C and TruthfulQA |
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| Model | ARC-C | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8k | |
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| ------------------------------------------------------------ | --------- | --------- | ---------- | ---------- | ---------- | --------- | |
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| [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** | |
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| [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 | |
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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 |
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> what? how is that useful? |
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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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## Model Details |
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### Model Description |
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. |
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- **Developed by:** [More Information Needed] |
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- **Funded by [optional]:** [More Information Needed] |
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- **Model type:** [More Information Needed] |
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- **Language(s) (NLP):** [More Information Needed] |
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- **License:** [More Information Needed] |
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- **Finetuned from model [optional]:** [More Information Needed] |
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### Model Sources [optional] |
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- **Repository:** [More Information Needed] |
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## Uses |
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### Direct Use |
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[More Information Needed] |
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### Downstream Use [optional] |
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### Out-of-Scope Use |
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[More Information Needed] |
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## Bias, Risks, and Limitations |
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[More Information Needed] |
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### Recommendations |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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[More Information Needed] |
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## Training Details |
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### Training Data |
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[More Information Needed] |
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### Training Procedure |
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#### Preprocessing [optional] |
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[More Information Needed] |
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#### Training Hyperparameters |
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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#### Speeds, Sizes, Times [optional] |
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[More Information Needed] |
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## Evaluation |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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[More Information Needed] |
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#### Factors |
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[More Information Needed] |
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#### Metrics |
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[More Information Needed] |
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### Results |
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#### Summary |
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## Model Examination [optional] |
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## Environmental Impact |
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). |
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- **Hardware Type:** [More Information Needed] |
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- **Carbon Emitted:** [More Information Needed] |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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[More Information Needed] |
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### Compute Infrastructure |
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#### Hardware |
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#### Software |
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## Citation [optional] |
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**BibTeX:** |
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**APA:** |
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## Glossary [optional] |
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## More Information [optional] |
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## Model Card Authors [optional] |
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## Model Card Contact |
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