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assignment_llm_1/assignment_image/install_env_and_run.md

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+# Install Environment (Conda) and Run Scripts
+
+This guide sets up a Conda environment and runs both scripts:
+
+- `assignment_image/code/c1.py` (train + save checkpoint)
+- `assignment_image/code/c1_test.py` (evaluate + error analysis)
+
+First need to enter this folder:
+
+```bash
+cd assignment_llm_1/assignment_image
+```
+
+
+## 1) Create and activate Conda environment
+
+```bash
+conda create -n transformer_hw python=3.10 -y
+conda activate transformer_hw
+python -m pip install --upgrade pip
+```
+
+## 2) Install dependencies
+
+If there is a `requirements.txt` file in this folder, run:
+
+```bash
+pip install -r requirements.txt
+```
+
+
+## 3) Run training script (`c1.py`)
+
+Move to the code directory and run:
+
+```bash
+python code/c1.py
+```
+
+Expected outputs include:
+
+- `saved_model/vit_cifar10_best.pt`
+- `saved_model/vit_cifar10_last.pt`
+
+## 4) Run evaluation script (`c1_test.py`)
+
+After training completes:
+
+```bash
+python code/c1_test.py \
+  --checkpoint-path ./saved_model/vit_cifar10_best.pt \
+  --results-dir ./results
+```
+
+This baseline evaluation run saves:
+
+- `results/baseline_analysis.txt`
+- `results/misclassified_examples_test.png`
+
+## 5) Run optional pre-trained ViT comparison
+
+To run transfer learning and compare baseline vs pre-trained ViT:
+
+```bash
+python code/c1_test.py \
+  --checkpoint-path ./saved_model/vit_cifar10_best.pt \
+  --results-dir ./results \
+  --run-pretrained-experiment
+```
+
+Additional files saved in this mode:
+
+- `results/pretrained_vit_analysis.txt`
+- `results/misclassified_examples_pretrained_vit.png`
+- `results/comparison_report.txt`
+
+## 6) Where data and outputs are saved
+
+- **Dataset download/cache**: `./data`  
+  (both `c1.py` and `c1_test.py` load CIFAR-10 from this folder by default)
+- **Model checkpoints from training**: `./saved_model`
+- **Evaluation artifacts/reports**: `./results` (or the path passed with `--results-dir`)
+- **Default checkpoint used by evaluation**: `./saved_model/vit_cifar10_best.pt`
+
+### Quick path summary
+
+- Training command: `python code/c1.py`
+- Baseline evaluation: `python code/c1_test.py --checkpoint-path ./saved_model/vit_cifar10_best.pt --results-dir ./results`
+- Baseline + transfer comparison: `python code/c1_test.py --checkpoint-path ./saved_model/vit_cifar10_best.pt --results-dir ./results --run-pretrained-experiment`
+
+

assignment_llm_1/assignment_image/requirements.txt

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+aiohappyeyeballs==2.6.1
+aiohttp==3.13.3
+aiosignal==1.4.0
+anyio==4.12.1
+async-timeout==5.0.1
+attrs==25.4.0
+certifi==2026.1.4
+charset-normalizer==3.4.4
+click==8.3.1
+contourpy==1.3.2
+cycler==0.12.1
+datasets==4.5.0
+dill==0.4.0
+exceptiongroup==1.3.1
+filelock==3.20.0
+fonttools==4.61.1
+frozenlist==1.8.0
+fsspec==2025.10.0
+h11==0.16.0
+hf-xet==1.2.0
+httpcore==1.0.9
+httpx==0.28.1
+huggingface_hub==1.4.1
+idna==3.11
+Jinja2==3.1.6
+joblib==1.5.3
+kiwisolver==1.4.9
+MarkupSafe==2.1.5
+matplotlib==3.10.8
+mpmath==1.3.0
+multidict==6.7.1
+multiprocess==0.70.18
+networkx==3.4.2
+numpy==2.2.6
+nvidia-cublas-cu11==11.11.3.6
+nvidia-cuda-cupti-cu11==11.8.87
+nvidia-cuda-nvrtc-cu11==11.8.89
+nvidia-cuda-runtime-cu11==11.8.89
+nvidia-cudnn-cu11==9.1.0.70
+nvidia-cufft-cu11==10.9.0.58
+nvidia-curand-cu11==10.3.0.86
+nvidia-cusolver-cu11==11.4.1.48
+nvidia-cusparse-cu11==11.7.5.86
+nvidia-nccl-cu11==2.21.5
+nvidia-nvtx-cu11==11.8.86
+packaging @ file:///home/task_176104877067765/conda-bld/packaging_1761049113113/work
+pandas==2.3.3
+pillow==12.0.0
+propcache==0.4.1
+pyarrow==23.0.0
+pyparsing==3.3.2
+python-dateutil==2.9.0.post0
+pytz==2025.2
+PyYAML==6.0.3
+requests==2.32.5
+scikit-learn==1.7.2
+scipy==1.15.3
+shellingham==1.5.4
+six==1.17.0
+sympy==1.14.0
+threadpoolctl==3.6.0
+torch==2.7.1+cu118
+torchaudio==2.7.1+cu118
+torchvision==0.22.1+cu118
+tqdm==4.67.3
+triton==3.3.1
+typer-slim==0.21.1
+typing_extensions==4.15.0
+tzdata==2025.3
+urllib3==2.6.3
+xxhash==3.6.0
+yarl==1.22.0

assignment_llm_1/assignment_image/results/baseline_analysis.txt

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+Baseline ViT (custom checkpoint) results
+Checkpoint: ./saved_model/vit_cifar10_best.pt
+Test Loss: 0.8916
+Test Accuracy: 68.57%
+
+Per-class accuracy (lower = harder classes):
+  cat        | acc= 49.50% | n=1000
+  bird       | acc= 57.60% | n=1000
+  dog        | acc= 63.70% | n=1000
+  deer       | acc= 64.00% | n=1000
+  airplane   | acc= 69.00% | n=1000
+  horse      | acc= 71.30% | n=1000
+  truck      | acc= 73.10% | n=1000
+  frog       | acc= 77.50% | n=1000
+  ship       | acc= 78.50% | n=1000
+  automobile | acc= 81.50% | n=1000
+
+Top confusion pairs (true -> predicted):
+  cat -> dog: 214 samples
+  dog -> cat: 171 samples
+  bird -> deer: 128 samples
+  truck -> automobile: 108 samples
+  deer -> horse: 100 samples
+  horse -> dog: 92 samples
+  airplane -> ship: 87 samples
+  automobile -> truck: 84 samples

assignment_llm_1/assignment_text/installAndRun.md

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+# Install and Run Guide
+
+This guide explains how to install dependencies and run the IMDB Transformer experiments in `assignment_llm_1/assignment_text`.
+
+First, enter this path using `cd`:
+
+```bash
+cd assignment_llm_1/assignment_text
+```
+
+## What is added in the code
+
+- Model-size experiment support in `assignment_text/code/c1.py`:
+  - `small`: `d_model=64`, `num_heads=4`, `num_layers=1`, `d_ff=128`
+  - `medium`: `d_model=128`, `num_heads=8`, `num_layers=2`, `d_ff=256`
+  - `large`: `d_model=256`, `num_heads=8`, `num_layers=4`, `d_ff=512`
+- Automatic experiment report generation:
+  - `assignment_text/saved_model/transformer_imdb_experiment_report.md`
+- Model-size selection in analysis script:
+  - `python code/c1_analysis.py --model_size small|medium|large ...`
+- Some qualitative error-analysis instances are available in:
+  - `assignment_text/documentation/error_analysis.json`
+
+## 1) Go to the project folder
+
+```bash
+cd ./assignment_llm_1/assignment_text
+```
+
+## 2) Create and activate environment
+
+### Option A: Conda (recommended if you use Conda)
+
+```bash
+conda create -n transformer_hw python=3.10 -y
+conda activate transformer_hw
+python -m pip install --upgrade pip
+```
+
+## 3) Install dependencies
+
+If there is a `requirements.txt` file in this folder, run:
+
+```bash
+pip install -r requirements.txt
+```
+
+
+## 4) Train all model sizes (small, medium, large)
+
+Run training from the `code` directory:
+
+```bash
+python code/c1.py
+```
+
+This will:
+- train `small`, `medium`, and `large` Transformer models,
+- save checkpoints under `assignment_llm_1/assignment_text/saved_model/`,
+- create a Markdown experiment report at:
+  - `assignment_llm_1/assignment_text/saved_model/transformer_imdb_experiment_report.md`
+
+## 5) Evaluate and analyze a selected model size
+
+From the same `code` directory:
+
+```bash
+python code/c1_analysis.py --split test --model_size small --num_examples 5
+python code/c1_analysis.py --split test --model_size medium --num_examples 5
+python code/c1_analysis.py --split test --model_size large --num_examples 5
+```
+
+Arguments:
+- `--split`: dataset split to evaluate (`test` or `train`)
+- `--model_size`: one of `small`, `medium`, `large`
+- `--num_examples`: number of misclassified examples to print
+
+## 6) (Optional) Use a custom checkpoint path directly
+
+If you want to bypass `--model_size`, pass an explicit checkpoint:
+
+```bash
+python code/c1_analysis.py \
+  --split test \
+  --checkpoint ../saved_model/transformer_imdb_large.pt \
+  --num_examples 5
+```
+
+## 7) Expected output files
+
+After running `c1.py`, these files should exist in `assignment_llm_1/assignment_text/saved_model/`:
+- `transformer_imdb_small.pt`
+- `transformer_imdb_medium.pt`
+- `transformer_imdb_large.pt`
+- `transformer_imdb.pt` (summary/compatibility checkpoint)
+- `transformer_imdb_experiment_report.md` (human-readable report)

assignment_llm_1/assignment_text/requirements.txt

@@ -0,0 +1,72 @@
+aiohappyeyeballs==2.6.1
+aiohttp==3.13.3
+aiosignal==1.4.0
+anyio==4.12.1
+async-timeout==5.0.1
+attrs==25.4.0
+certifi==2026.1.4
+charset-normalizer==3.4.4
+click==8.3.1
+contourpy==1.3.2
+cycler==0.12.1
+datasets==4.5.0
+dill==0.4.0
+exceptiongroup==1.3.1
+filelock==3.20.0
+fonttools==4.61.1
+frozenlist==1.8.0
+fsspec==2025.10.0
+h11==0.16.0
+hf-xet==1.2.0
+httpcore==1.0.9
+httpx==0.28.1
+huggingface_hub==1.4.1
+idna==3.11
+Jinja2==3.1.6
+joblib==1.5.3
+kiwisolver==1.4.9
+MarkupSafe==2.1.5
+matplotlib==3.10.8
+mpmath==1.3.0
+multidict==6.7.1
+multiprocess==0.70.18
+networkx==3.4.2
+numpy==2.2.6
+nvidia-cublas-cu11==11.11.3.6
+nvidia-cuda-cupti-cu11==11.8.87
+nvidia-cuda-nvrtc-cu11==11.8.89
+nvidia-cuda-runtime-cu11==11.8.89
+nvidia-cudnn-cu11==9.1.0.70
+nvidia-cufft-cu11==10.9.0.58
+nvidia-curand-cu11==10.3.0.86
+nvidia-cusolver-cu11==11.4.1.48
+nvidia-cusparse-cu11==11.7.5.86
+nvidia-nccl-cu11==2.21.5
+nvidia-nvtx-cu11==11.8.86
+packaging @ file:///home/task_176104877067765/conda-bld/packaging_1761049113113/work
+pandas==2.3.3
+pillow==12.0.0
+propcache==0.4.1
+pyarrow==23.0.0
+pyparsing==3.3.2
+python-dateutil==2.9.0.post0
+pytz==2025.2
+PyYAML==6.0.3
+requests==2.32.5
+scikit-learn==1.7.2
+scipy==1.15.3
+shellingham==1.5.4
+six==1.17.0
+sympy==1.14.0
+threadpoolctl==3.6.0
+torch==2.7.1+cu118
+torchaudio==2.7.1+cu118
+torchvision==0.22.1+cu118
+tqdm==4.67.3
+triton==3.3.1
+typer-slim==0.21.1
+typing_extensions==4.15.0
+tzdata==2025.3
+urllib3==2.6.3
+xxhash==3.6.0
+yarl==1.22.0