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Repository: da03/Internalize_CoT_Step_by_Step
Branch: main
Commit: c2e3891d8d57
Files: 36
Total size: 9.0 MB
Directory structure:
gitextract_g8ic_aaz/
├── .gitattributes
├── LICENSE
├── README.md
├── data/
│ ├── 11_by_11_mult/
│ │ ├── test_bigbench.txt
│ │ ├── train.txt
│ │ └── valid.txt
│ ├── 4_by_4_mult/
│ │ ├── test_bigbench.txt
│ │ ├── train.txt
│ │ └── valid.txt
│ ├── 5_by_5_mult/
│ │ ├── test_bigbench.txt
│ │ ├── train.txt
│ │ └── valid.txt
│ ├── 7_by_7_mult/
│ │ ├── test_bigbench.txt
│ │ ├── train.txt
│ │ └── valid.txt
│ ├── 9_by_9_mult/
│ │ ├── test_bigbench.txt
│ │ ├── train.txt
│ │ └── valid.txt
│ └── gsm8k/
│ ├── test.txt
│ ├── train.txt
│ └── valid.txt
├── logs/
│ ├── 11_by_11_mult_320_tokens_removed/
│ │ └── gpt2/
│ │ └── log.generate
│ ├── 4_by_4_mult/
│ │ └── gpt2/
│ │ └── log.generate
│ ├── 5_by_5_mult/
│ │ └── gpt2/
│ │ └── log.generate
│ ├── 7_by_7_mult/
│ │ └── gpt2/
│ │ └── log.generate
│ ├── 9_by_9_mult/
│ │ └── gpt2/
│ │ └── log.generate
│ └── gsm8k/
│ ├── gpt2/
│ │ └── log.generate
│ ├── gpt2-medium/
│ │ └── log.generate
│ ├── mistral-7B/
│ │ └── log.generate
│ └── phi3-3.8B/
│ └── log.generate
└── src/
├── configuration_model.py
├── data.py
├── generate.py
├── model.py
├── train.py
└── utils.py
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FILE CONTENTS
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FILE: .gitattributes
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data/7_by_7_mult/train.txt filter=lfs diff=lfs merge=lfs -text
data/4_by_4_mult/train.txt filter=lfs diff=lfs merge=lfs -text
data/5_by_5_mult/train.txt filter=lfs diff=lfs merge=lfs -text
data/9_by_9_mult/train.txt filter=lfs diff=lfs merge=lfs -text
data/11_by_11_mult/train.txt filter=lfs diff=lfs merge=lfs -text
data/gsm8k/train.txt filter=lfs diff=lfs merge=lfs -text
================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) 2024 Yuntian Deng
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
================================================
FILE: README.md
================================================
# Internalize CoT Step by Step
This repository contains code to reproduce the results from our paper [From Explicit CoT to Implicit CoT: Learning to Internalize CoT Step by Step](https://arxiv.org/pdf/2405.14838).
## Online Demos
For multiplication, an online demo is available at [gpt2-multiplication](https://huggingface.co/spaces/yuntian-deng/gpt2-multiplication). The inference code and model behind this demo can be accessed in the [app.py](https://huggingface.co/spaces/yuntian-deng/gpt2-multiplication/blob/main/app.py) file. Please note that this online demo uses a slightly different data format than the one used in this repository. Specifically, it uses `=` instead of `<|endoftext|>` as the separator to ensure compatibility with standard Hugging Face transformer generation code, and the `####` symbols in the answer part are removed to provide a cleaner output. Despite these changes, the proposed approach still works effectively, demonstrating the generality of the method. Additionally, the online demo merges data with varying input digit lengths for training, allowing a single model to handle inputs of different digit counts. In contrast, the code in this repository trains one model for each number of digits for controlled experiments.
For GSM8K, an online demo is available at [implicit-cot-math](https://huggingface.co/spaces/yuntian-deng/implicit-cot-math). Note that this online demo internalizes one `</s>` token that separates the CoT from the answer (while keeping the `</s>` token that separates the input from the CoT intact), ensuring compatibility with standard Hugging Face transformer generation code. The final accuracy remains at 52%, nearly the same as the number reported in the paper (51%).
## Prerequisites
* [PyTorch](https://pytorch.org/get-started/locally/)
* [transformers](https://github.com/huggingface/transformers) (`pip install transformers`)
## Data & Pretrained Models & Logs
All dataset files and log files during inference are included in this repository, with the exception of large training files maintained under Git LFS. Model checkpoints are stored on Google Drive. The folder containing all model checkpoints and datasets can be found at [this link](https://drive.google.com/drive/folders/1_riWn76CiUWcc-fB6KL__PSBF5zstF1T?usp=sharing).
* 4 X 4 Mult - GPT-2 (Acc: 1.00): [data](data/4_by_4_mult/) [model](https://drive.google.com/drive/folders/1cCqJmmwJA7wg0Q64f51WgASSnLVJ4ahO?usp=sharing) [log](logs/4_by_4_mult/gpt2/log.generate)
* 5 X 5 Mult - GPT-2 (Acc: 1.00): [data](data/5_by_5_mult/) [model](https://drive.google.com/drive/folders/1r80h5rLgAa1NpAzIknuvHB92nDT2j0Hz?usp=sharing) [log](logs/5_by_5_mult/gpt2/log.generate)
* 7 X 7 Mult - GPT-2 (Acc: 0.95): [data](data/7_by_7_mult/) [model](https://drive.google.com/drive/folders/1a8jbCV9d9o243qqsxKvgnwDVT27PfJry?usp=sharing) [log](logs/7_by_7_mult/gpt2/log.generate)
* 9 X 9 Mult - GPT-2 (Acc: 0.99): [data](data/9_by_9_mult/) [model](https://drive.google.com/drive/folders/1WQn00xYt_fuFaw-hsl9u-QavmBn8NUaD?usp=sharing) [log](logs/9_by_9_mult/gpt2/log.generate)
* 11 X 11 Mult - GPT-2 (Acc: 0.74): [data](data/11_by_11_mult/) [model](https://drive.google.com/drive/folders/1JuHal52yxN0oO0Q_FMhjp6Enr54wAVy6?usp=sharing) (partially internalized) [log](logs/11_by_11_mult_320_tokens_removed/gpt2/log.generate)
* GSM8K - GPT-2 (Acc: 0.30): [data](data/gsm8k/) [model](https://drive.google.com/drive/folders/1IUQ26DNqDrX0mfTSm19gqG5k1FxLZsr2?usp=sharing) [log](logs/gsm8k/gpt2/log.generate)
* GSM8K - GPT-2 Medium (Acc: 0.35): [data](data/gsm8k/) [model](https://drive.google.com/drive/folders/1-mJ8h9O8ztx0iWJxtgcP4LEJlxj6Cyln?usp=sharing) [log](logs/gsm8k/gpt2-medium/log.generate)
* GSM8K - Phi-3 3.8B (Acc: 0.31): [data](data/gsm8k/) [model](https://drive.google.com/drive/folders/1Jm1sk62WhDX2exiQmRKI53aW5jKXAZE9?usp=sharing) [log](logs/gsm8k/phi3-3.8B/log.generate)
* GSM8K - Mistral 7B (Acc: 0.51): [data](data/gsm8k/) [model](https://drive.google.com/drive/folders/1azfzWxf2jy1H7XAe-dAhtYFTPuh7tfmd?usp=sharing) [log](logs/gsm8k/mistral-7B/log.generate)
## Additional Datasets
We have included more multiplication datasets than those used in the paper to encourage future research that might yield even better results. The folder containing all datasets can be found at [this link](https://drive.google.com/drive/folders/1dbQYoZNhu4h7aQzp6b6s3CUOd2pMX0sB?usp=sharing).
* 6 X 6 Mult: [data](https://drive.google.com/drive/folders/1FkGl3r4nxJZfbmh_cfNIrdC6GVFqeE_N?usp=sharing)
* 8 X 8 Mult: [data](https://drive.google.com/drive/folders/1F_k292AiCTSSHyLSejQYhApdnQzs57F7?usp=sharing)
* 10 X 10 Mult: [data](https://drive.google.com/drive/folders/15CDOwZIHioEmgno8XlJ1xHqvAtyorBz_?usp=sharing)
* 12 X 12 Mult: [data](https://drive.google.com/drive/folders/1WBwVezO4IdtQAsndebprjiBHq7qiCwFH?usp=sharing)
* 13 X 13 Mult: [data](https://drive.google.com/drive/folders/1XLBOxh-wQZFXMZn5CDuHnj4fmhA0iV4M?usp=sharing)
* 14 X 14 Mult: [data](https://drive.google.com/drive/folders/1mmZH03btFnUFg94rGs5OdUA6Usajb8rq?usp=sharing)
* 15 X 15 Mult: [data](https://drive.google.com/drive/folders/1jw4lTvzoGhnFIec5QCsukKK14_sUWE9w?usp=sharing)
* 16 X 16 Mult: [data](https://drive.google.com/drive/folders/1V8dl6mOfgsdTvraY7S7ypx8fQ76a4aAr?usp=sharing)
* 17 X 17 Mult: [data](https://drive.google.com/drive/folders/1-tepTXJqjJcFbzPrpSI12_lhMTHOEn2T?usp=sharing)
* 18 X 18 Mult: [data](https://drive.google.com/drive/folders/1vc3aCAz7ypv2G2RgpSjKQRVqew0kxe8Q?usp=sharing)
* 19 X 19 Mult: [data](https://drive.google.com/drive/folders/1lL88kkGBI6umMVMs7LB0xw6CHvifcSFF?usp=sharing)
* 20 X 20 Mult: [data](https://drive.google.com/drive/folders/1dRav5OysRX2L-nOpgYpi0fOpDEbgtU_f?usp=sharing)
## Usage
We use 9 X 9 Mult with GPT-2 as an example. We assume that the working directory is `Internalize_CoT_Step_by_Step` throughout this document.
### Data Format
The format of training, validation, and test files is as follows:
```
[input 1]||[chain-of-thought 1] #### [output 1]
[input 2]||[chain-of-thought 2] #### [output 3]
[input 3]||[chain-of-thought 2] #### [output 3]
...
```
For example, the first line from the 4 X 4 Mult test set in [data/4_by_4_mult/test_bigbench.txt](data/4_by_4_mult/test_bigbench.txt) is:
```
9 1 7 3 * 9 4 3 3||1 7 4 3 3 + 0 6 7 8 4 1 ( 1 3 2 2 8 1 ) + 0 0 7 5 1 1 1 ( 1 3 9 7 9 2 1 ) + 0 0 0 7 5 1 1 1 #### 1 3 9 4 5 4 2 1
```
In this example, the input is `9 1 7 3 * 9 4 3 3` (corresponding to `3719*3349`, note that we reversed the digits), the chain-of-thought is `1 7 4 3 3 + 0 6 7 8 4 1 ( 1 3 2 2 8 1 ) + 0 0 7 5 1 1 1 ( 1 3 9 7 9 2 1 ) + 0 0 0 7 5 1 1 1`, and the output is `1 3 9 4 5 4 2 1` (corresponding to `12454931`).
Note that the chain-of-thought steps are only used for training, not for generation.
### Training
To train the model, run the following commands. The example uses 9 X 9 Mult with GPT-2:
```
export D=9
export FOLDER=data/${D}_by_${D}_mult/
export MODEL=gpt2
export EPOCHS=200
export LR=5e-5
export BSZ=32
export ACCUMULATE=1
export REMOVE_PER_EPOCH=8
export REMOVE_ALL_WHEN_REMOVE_BEYOND=inf
export REMOVAL_SMOOTHING_LAMBDA=4
export REMOVAL_SIDE=left
export PRETRAIN_EPOCHS=0
export SEED=3456
export SAVE=train_models/${D}_by_${D}_mult/gpt2
mkdir -p $SAVE
TOKENIZERS_PARALLELISM=false CUDA_VISIBLE_DEVICES=0 stdbuf -oL -eL python src/train.py \
--model ${MODEL} \
--train_path ${FOLDER}/train.txt \
--val_path ${FOLDER}/valid.txt \
--epochs ${EPOCHS} \
--lr ${LR} \
--batch_size ${BSZ} \
--accumulate ${ACCUMULATE} \
--remove_per_epoch ${REMOVE_PER_EPOCH} \
--remove_all_when_remove_beyond ${REMOVE_ALL_WHEN_REMOVE_BEYOND} \
--removal_smoothing_lambda ${REMOVAL_SMOOTHING_LAMBDA} \
--removal_side ${REMOVAL_SIDE} \
--pretrain_epochs ${PRETRAIN_EPOCHS} \
--seed ${SEED} \
--reset_optimizer \
--save_model ${SAVE} \
> ${SAVE}/log.train 2>&1
```
### Generation & Evaluation
Here we use a pretrained model as an example. Download the folder `models/9_by_9_mult/gpt2`, then the following command will run inference and evaluate both accuracy and throughput, logged in file `generation_logs/9_by_9_mult/log.generate`.
```
export D=9
export FOLDER=data/${D}_by_${D}_mult/
export MODEL=models/${D}_by_${D}_mult/gpt2
export BSZ=1
export SAVE=generation_logs/${D}_by_${D}_mult/gpt2
mkdir -p $SAVE
TOKENIZERS_PARALLELISM=false CUDA_VISIBLE_DEVICES=0 stdbuf -oL -eL python src/generate.py \
--from_pretrained ${MODEL} \
--test_path ${FOLDER}/test_bigbench.txt \
--batch_size ${BSZ} \
> ${SAVE}/log.generate 2>&1&
```
### Command for GSM8K
```
export FOLDER=data/gsm8k
export MODEL=mistralai/Mistral-7B-v0.1
export EPOCHS=80
export LR=1e-5
export BSZ=16
export ACCUMULATE=2
export REMOVE_PER_EPOCH=8
export REMOVE_ALL_WHEN_REMOVE_BEYOND=39
export MAX_LEN_TRAIN=150
export REMOVAL_SMOOTHING_LAMBDA=4
export REMOVAL_SIDE=left
export PRETRAIN_EPOCHS=0
export SEED=1234
export SAVE=train_models/gsm8k
mkdir -p $SAVE
TOKENIZERS_PARALLELISM=false CUDA_VISIBLE_DEVICES=0 stdbuf -oL -eL python src/train.py \
--model ${MODEL} \
--train_path ${FOLDER}/train.txt \
--val_path ${FOLDER}/valid.txt \
--epochs ${EPOCHS} \
--lr ${LR} \
--batch_size ${BSZ} \
--accumulate ${ACCUMULATE} \
--remove_per_epoch ${REMOVE_PER_EPOCH} \
--remove_all_when_remove_beyond ${REMOVE_ALL_WHEN_REMOVE_BEYOND} \
--removal_smoothing_lambda ${REMOVAL_SMOOTHING_LAMBDA} \
--removal_side ${REMOVAL_SIDE} \
--pretrain_epochs ${PRETRAIN_EPOCHS} \
--seed ${SEED} \
--reset_optimizer \
--bf16 \
--max_len_train ${MAX_LEN_TRAIN} \
--save_model ${SAVE} \
> ${SAVE}/log.train 2>&1
```
================================================
FILE: data/11_by_11_mult/test_bigbench.txt
================================================
4 8 7 7 1 8 4 2 8 1 1 * 1 2 1 5 1 3 4 3 6 6 2||4 8 7 7 1 8 4 2 8 1 1 0 + 0 8 6 5 5 3 6 9 4 6 3 2 0 ( 4 6 4 3 7 1 1 2 3 8 4 2 0 ) + 0 0 4 8 7 7 1 8 4 2 8 1 1 0 ( 4 6 8 1 5 9 2 0 8 0 3 4 1 0 ) + 0 0 0 0 2 9 8 8 0 4 2 1 9 5 0 ( 4 6 8 1 7 8 1 9 8 4 5 5 0 6 0 ) + 0 0 0 0 4 8 7 7 1 8 4 2 8 1 1 0 ( 4 6 8 1 1 7 9 6 0 3 0 8 8 7 1 0 ) + 0 0 0 0 0 2 5 3 3 5 4 4 7 4 5 3 0 ( 4 6 8 1 1 9 4 0 4 8 4 2 6 2 7 3 0 ) + 0 0 0 0 0 0 6 3 1 1 7 2 9 9 2 7 4 0 ( 4 6 8 1 1 9 0 4 5 9 1 5 5 2 0 1 5 0 ) + 0 0 0 0 0 0 0 2 5 3 3 5 4 4 7 4 5 3 0 ( 4 6 8 1 1 9 0 6 0 3 5 0 0 7 7 5 0 4 0 ) + 0 0 0 0 0 0 0 0 4 0 7 6 0 9 8 4 9 0 7 0 ( 4 6 8 1 1 9 0 6 4 3 2 7 0 6 6 0 0 5 7 0 ) + 0 0 0 0 0 0 0 0 0 4 0 7 6 0 9 8 4 9 0 7 0 ( 4 6 8 1 1 9 0 6 4 7 2 4 7 6 5 9 4 4 8 7 0 ) + 0 0 0 0 0 0 0 0 0 0 8 6 5 5 3 6 9 4 6 3 2 0 #### 4 6 8 1 1 9 0 6 4 7 0 1 3 2 9 5 4 9 4 1 3 0
5 0 5 4 9 2 1 1 7 7 5 * 0 9 4 0 1 3 4 5 4 0 9||0 0 0 0 0 0 0 0 0 0 0 0 + 0 5 4 5 0 5 6 1 0 4 9 1 5 ( 0 5 4 5 0 5 6 1 0 4 9 1 5 ) + 0 0 0 2 0 8 7 1 5 4 8 0 3 2 ( 0 5 4 7 0 3 4 3 5 8 7 2 8 2 ) + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ( 0 5 4 7 0 3 4 3 5 8 7 2 8 2 0 ) + 0 0 0 0 5 0 5 4 9 2 1 1 7 7 5 0 ( 0 5 4 7 5 3 9 7 4 1 9 3 5 0 6 0 ) + 0 0 0 0 0 5 1 5 3 8 8 3 3 1 3 7 1 ( 0 5 4 7 5 8 0 3 8 9 7 7 8 1 9 7 1 ) + 0 0 0 0 0 0 0 2 0 8 7 1 5 4 8 0 3 2 ( 0 5 4 7 5 8 0 5 8 7 5 9 3 6 7 8 4 2 ) + 0 0 0 0 0 0 0 5 2 5 2 7 4 6 5 5 8 8 2 ( 0 5 4 7 5 8 0 0 1 3 8 6 8 2 3 4 3 1 3 ) + 0 0 0 0 0 0 0 0 0 2 0 8 7 1 5 4 8 0 3 2 ( 0 5 4 7 5 8 0 0 1 5 8 4 6 4 8 8 1 2 6 2 ) + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ( 0 5 4 7 5 8 0 0 1 5 8 4 6 4 8 8 1 2 6 2 0 ) + 0 0 0 0 0 0 0 0 0 0 5 4 5 0 5 6 1 0 4 9 1 5 #### 0 5 4 7 5 8 0 0 1 5 3 9 1 5 3 5 3 2 0 2 2 5
0 3 7 8 8 7 7 7 9 4 4 * 1 1 4 9 7 5 4 9 2 1 3||0 3 7 8 8 7 7 7 9 4 4 0 + 0 0 3 7 8 8 7 7 7 9 4 4 0 ( 0 3 0 6 7 6 5 5 7 4 9 4 0 ) + 0 0 0 2 9 4 5 1 1 1 9 9 7 1 ( 0 3 0 8 6 1 1 7 8 5 8 4 8 1 ) + 0 0 0 0 7 5 8 9 0 0 0 8 4 0 4 ( 0 3 0 8 3 7 9 6 9 5 8 2 3 2 4 ) + 0 0 0 0 0 1 1 1 2 5 4 4 8 4 1 3 ( 0 3 0 8 3 8 0 8 1 1 3 7 1 7 5 3 ) + 0 0 0 0 0 0 5 6 3 4 9 8 8 8 4 2 2 ( 0 3 0 8 3 8 5 4 5 5 2 6 0 6 0 6 2 ) + 0 0 0 0 0 0 0 2 9 4 5 1 1 1 9 9 7 1 ( 0 3 0 8 3 8 5 6 4 0 8 7 1 7 9 5 0 2 ) + 0 0 0 0 0 0 0 0 7 5 8 9 0 0 0 8 4 0 4 ( 0 3 0 8 3 8 5 6 1 6 6 7 2 7 9 3 5 2 4 ) + 0 0 0 0 0 0 0 0 0 6 4 7 7 5 5 5 9 9 8 0 ( 0 3 0 8 3 8 5 6 1 2 1 5 0 3 5 9 4 2 3 1 ) + 0 0 0 0 0 0 0 0 0 0 3 7 8 8 7 7 7 9 4 4 0 ( 0 3 0 8 3 8 5 6 1 2 4 2 9 1 3 7 2 2 8 5 0 ) + 0 0 0 0 0 0 0 0 0 0 0 9 1 6 6 3 3 3 9 4 3 1 #### 0 3 0 8 3 8 5 6 1 2 4 1 1 8 9 0 6 5 7 0 4 1
1 2 9 3 0 7 2 0 4 3 7 * 3 4 7 3 9 0 8 4 7 2 3||3 6 7 1 1 1 8 0 2 0 2 2 + 0 4 8 6 5 1 8 0 1 6 3 9 2 ( 3 0 6 8 6 2 6 1 3 6 5 1 3 ) + 0 0 7 4 4 7 2 9 8 1 8 3 1 5 ( 3 0 3 3 1 0 9 0 2 8 3 5 4 5 ) + 0 0 0 3 6 7 1 1 1 8 0 2 0 2 2 ( 3 0 3 6 7 7 0 2 3 6 4 7 4 7 2 ) + 0 0 0 0 9 8 2 5 3 3 4 2 6 0 6 6 ( 3 0 3 6 6 6 3 7 6 9 8 9 0 8 8 6 ) + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ( 3 0 3 6 6 6 3 7 6 9 8 9 0 8 8 6 0 ) + 0 0 0 0 0 0 8 6 3 1 3 6 1 2 2 7 8 5 ( 3 0 3 6 6 6 1 4 0 1 2 6 2 0 1 4 9 5 ) + 0 0 0 0 0 0 0 4 8 6 5 1 8 0 1 6 3 9 2 ( 3 0 3 6 6 6 1 8 8 7 7 7 0 1 2 0 3 5 3 ) + 0 0 0 0 0 0 0 0 7 4 4 7 2 9 8 1 8 3 1 5 ( 3 0 3 6 6 6 1 8 5 2 2 5 3 0 1 2 1 9 4 5 ) + 0 0 0 0 0 0 0 0 0 2 4 8 7 0 4 5 0 8 6 4 1 ( 3 0 3 6 6 6 1 8 5 4 6 3 1 1 5 7 1 7 1 0 2 ) + 0 0 0 0 0 0 0 0 0 0 3 6 7 1 1 1 8 0 2 0 2 2 #### 3 0 3 6 6 6 1 8 5 4 9 9 8 2 6 8 9 7 3 0 4 2
7 3 2 2 4 9 4 9 8 3 1 * 2 9 2 7 3 9 0 9 8 6 9||4 7 4 4 8 8 9 8 7 7 2 0 + 0 3 3 1 0 8 4 4 5 0 5 2 1 ( 4 0 8 5 8 6 4 3 3 8 7 2 1 ) + 0 0 4 7 4 4 8 8 9 8 7 7 2 0 ( 4 0 2 3 3 1 3 2 3 7 5 0 4 0 ) + 0 0 0 9 5 6 5 9 5 4 6 2 7 9 0 ( 4 0 2 2 9 7 8 1 9 1 2 3 1 0 1 ) + 0 0 0 0 1 1 7 6 2 8 4 8 6 1 4 0 ( 4 0 2 2 0 9 5 8 1 0 7 1 8 1 5 0 ) + 0 0 0 0 0 3 3 1 0 8 4 4 5 0 5 2 1 ( 4 0 2 2 0 2 9 9 1 8 1 6 3 2 0 3 1 ) + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ( 4 0 2 2 0 2 9 9 1 8 1 6 3 2 0 3 1 0 ) + 0 0 0 0 0 0 0 3 3 1 0 8 4 4 5 0 5 2 1 ( 4 0 2 2 0 2 9 2 5 9 1 4 8 6 5 3 6 2 1 ) + 0 0 0 0 0 0 0 0 6 9 8 7 3 5 9 5 1 1 1 1 ( 4 0 2 2 0 2 9 2 1 9 0 2 2 2 5 9 7 3 2 1 ) + 0 0 0 0 0 0 0 0 0 2 2 4 3 5 6 9 6 3 3 8 0 ( 4 0 2 2 0 2 9 2 1 1 3 6 5 7 1 9 4 7 5 9 0 ) + 0 0 0 0 0 0 0 0 0 0 3 3 1 0 8 4 4 5 0 5 2 1 #### 4 0 2 2 0 2 9 2 1 1 6 9 6 7 9 3 9 2 6 4 3 1
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6 3 4 9 9 3 5 1 2 9 4 * 6 1 8 9 1 6 9 2 0 0 5||6 1 6 6 9 3 2 9 2 5 9 2 + 0 6 3 4 9 9 3 5 1 2 9 4 0 ( 6 7 9 0 9 3 6 4 4 7 8 7 0 ) + 0 0 8 8 4 5 9 1 3 2 7 3 9 3 ( 6 7 7 9 3 9 5 6 7 9 5 1 0 4 ) + 0 0 0 4 2 9 4 9 5 8 3 9 2 4 4 ( 6 7 7 3 6 8 0 6 3 8 9 0 3 8 4 ) + 0 0 0 0 6 3 4 9 9 3 5 1 2 9 4 0 ( 6 7 7 3 2 2 5 5 3 2 5 2 5 7 9 0 ) + 0 0 0 0 0 6 1 6 6 9 3 2 9 2 5 9 2 ( 6 7 7 3 2 8 6 1 0 2 9 4 4 0 5 0 3 ) + 0 0 0 0 0 0 4 2 9 4 9 5 8 3 9 2 4 4 ( 6 7 7 3 2 8 0 4 9 6 8 0 3 4 4 3 7 4 ) + 0 0 0 0 0 0 0 2 7 8 8 9 7 0 3 4 8 9 0 ( 6 7 7 3 2 8 0 6 6 5 7 0 1 5 7 7 5 4 1 ) + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ( 6 7 7 3 2 8 0 6 6 5 7 0 1 5 7 7 5 4 1 0 ) + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ( 6 7 7 3 2 8 0 6 6 5 7 0 1 5 7 7 5 4 1 0 0 ) + 0 0 0 0 0 0 0 0 0 0 0 8 1 7 9 9 6 7 0 6 4 2 #### 6 7 7 3 2 8 0 6 6 5 7 8 2 2 7 7 2 2 2 6 4 2
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gitextract_g8ic_aaz/
├── .gitattributes
├── LICENSE
├── README.md
├── data/
│ ├── 11_by_11_mult/
│ │ ├── test_bigbench.txt
│ │ ├── train.txt
│ │ └── valid.txt
│ ├── 4_by_4_mult/
│ │ ├── test_bigbench.txt
│ │ ├── train.txt
│ │ └── valid.txt
│ ├── 5_by_5_mult/
│ │ ├── test_bigbench.txt
│ │ ├── train.txt
│ │ └── valid.txt
│ ├── 7_by_7_mult/
│ │ ├── test_bigbench.txt
│ │ ├── train.txt
│ │ └── valid.txt
│ ├── 9_by_9_mult/
│ │ ├── test_bigbench.txt
│ │ ├── train.txt
│ │ └── valid.txt
│ └── gsm8k/
│ ├── test.txt
│ ├── train.txt
│ └── valid.txt
├── logs/
│ ├── 11_by_11_mult_320_tokens_removed/
│ │ └── gpt2/
│ │ └── log.generate
│ ├── 4_by_4_mult/
│ │ └── gpt2/
│ │ └── log.generate
│ ├── 5_by_5_mult/
│ │ └── gpt2/
│ │ └── log.generate
│ ├── 7_by_7_mult/
│ │ └── gpt2/
│ │ └── log.generate
│ ├── 9_by_9_mult/
│ │ └── gpt2/
│ │ └── log.generate
│ └── gsm8k/
│ ├── gpt2/
│ │ └── log.generate
│ ├── gpt2-medium/
│ │ └── log.generate
│ ├── mistral-7B/
│ │ └── log.generate
│ └── phi3-3.8B/
│ └── log.generate
└── src/
├── configuration_model.py
├── data.py
├── generate.py
├── model.py
├── train.py
└── utils.py
SYMBOL INDEX (33 symbols across 6 files)
FILE: src/configuration_model.py
class ImplicitModelConfig (line 3) | class ImplicitModelConfig(PretrainedConfig):
method __init__ (line 4) | def __init__(
FILE: src/data.py
function extract_answer (line 8) | def extract_answer(text):
function extract_cot (line 18) | def extract_cot(text):
class CoTDataset (line 28) | class CoTDataset(Dataset):
method __init__ (line 29) | def __init__(self, tokenizer, file_path, max_length=-1, max_size=-1):
method __len__ (line 75) | def __len__(self):
method __getitem__ (line 79) | def __getitem__(self, i):
class CoTDataCollator (line 89) | class CoTDataCollator:
method __init__ (line 95) | def __init__(self, tokenizer):
method __call__ (line 98) | def __call__(self, examples):
method _tensorize_batch (line 116) | def _tensorize_batch(self, examples):
FILE: src/generate.py
function evaluate (line 24) | def evaluate(dataloader, tokenizer, device, ctx, model, max_new_tokens):
function main (line 67) | def main():
FILE: src/model.py
class ImplicitModel (line 13) | class ImplicitModel(nn.Module):
method __init__ (line 14) | def __init__(self, config, reinitialize_weights=False):
method forward (line 23) | def forward(self, input_ids, position_ids=None, output_attentions=False):
method compute_loss (line 30) | def compute_loss(self, input_ids, labels, position_ids=None, output_at...
method generate (line 52) | def generate(self, input_ids, max_new_tokens=512, num_beams=1, stop_on...
method from_pretrained (line 118) | def from_pretrained(self, pretrained_path):
method save_pretrained (line 125) | def save_pretrained(self, save_directory):
FILE: src/train.py
function compute_lambda_distribution (line 25) | def compute_lambda_distribution(removal_smoothing_lambda, truncate_lengt...
function evaluate (line 39) | def evaluate(dataloader, tokenizer, device, ctx, model, max_new_tokens, ...
function main (line 130) | def main():
FILE: src/utils.py
function save_model (line 5) | def save_model(model, tokenizer, model_dir):
function batch_ids (line 12) | def batch_ids(input_ids_list, pad_token_id, device, dtype):
function get_sep_position (line 22) | def get_sep_position(input_ids, sep_id, skip=0):
class DoubleEOSStoppingCriteria (line 36) | class DoubleEOSStoppingCriteria(StoppingCriteria):
method __init__ (line 37) | def __init__(self, eos_token_id):
method __call__ (line 42) | def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTen...
class DoubleEOSLogitsProcessor (line 50) | class DoubleEOSLogitsProcessor(LogitsProcessor):
method __init__ (line 51) | def __init__(self, eos_token_id):
method __call__ (line 56) | def __call__(self, input_ids, scores):
Condensed preview — 36 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (9,549K chars).
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{
"path": "src/data.py",
"chars": 5721,
"preview": "from dataclasses import dataclass\nimport os\nimport copy\nimport torch\nfrom torch.utils.data import Dataset\nfrom torch.nn."
},
{
"path": "src/generate.py",
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"preview": "import math\nimport time\nimport torch\nfrom torch.utils.data import DataLoader\nfrom transformers import AdamW\nimport argpa"
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{
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"preview": "import os\nimport torch\nimport torch.nn as nn\n\nfrom torch.nn import CrossEntropyLoss\nfrom transformers import AutoModelFo"
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"preview": "import math\nimport argparse\nimport os\nimport sys\nimport tqdm\nimport inspect\nimport logging\nimport random\nimport torch\n\nf"
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{
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"preview": "import torch\nfrom transformers import StoppingCriteria, LogitsProcessor\n\n\ndef save_model(model, tokenizer, model_dir):\n "
}
]
About this extraction
This page contains the full source code of the da03/Internalize_CoT_Step_by_Step GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 36 files (9.0 MB), approximately 2.4M tokens, and a symbol index with 33 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
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