The following instructions assume you are running from this directory (you may need to cd to this directory).

Download Data

First, you need to download the splits.json file containing information about all the splits, as well as the train.jsonl candidate selected by McGill-NLP/MiniLM-L6-DMR:

from huggingface_hub import snapshot_download

# splits.json
snapshot_download(
    repo_id="McGill-NLP/WebLINX-full", repo_type="dataset", allow_patterns="splits.json", local_dir="./wl_data/"
)

# candidates files
snapshot_download(
    repo_id="McGill-NLP/WebLINX-full",
    repo_type="dataset",
    allow_patterns="candidates/*.jsonl",
    local_dir="./wl_data/"
)

Download the full dataset (warning: this will take a while):

from huggingface_hub import snapshot_download

snapshot_download(repo_id="McGill-NLP/WebLINX-full", repo_type="dataset", local_dir="./wl_data/")

The default configs (llama/conf/config.yml) assume that the train.jsonl is located at ./wl_data/candidates/train.jsonl. If you want to change the path, you need to modify the config.yml accordingly.

Set `WEBLINX_PROJECT_DIR`

You need to set the WEBLINX_PROJECT_DIR environment variable to the root directory of the WebLINX project. For example, if you have the following directory structure:

export WEBLINX_PROJECT_DIR=/path/to/the/modeling/directory/

# For example, if you are in the modeling directory, you can run:
export WEBLINX_PROJECT_DIR=$(pwd)

Install Dependencies

You need to install the dependencies by running the following command:

pip install -r requirements.txt

However, due to flash-attention requiring torch to be pre-installed, it has to be install right after everything else has been installed:

pip install wheel
# Regular install
pip install "flash-attn>=2.3.0"
# IF you have limited RAM, you can try this:
MAX_JOBS=4 pip install "flash-attn>=2.3.0" --no-build-isolation
# If you have issues with nvcc, try this:
FLASH_ATTENTION_SKIP_CUDA_BUILD=TRUE pip install "flash-attn>=2.3.0" --no-build-isolation

Optional: Symbolic linking to `WebLINX-full`

If you downloaded WebLINX-full data in a different location (e.g. different disk) from your weblinx/modeling directory, you might consider using symbolic link to avoid having to change the config.yml files. You should do something like:

ln -s /location/of/your/full/data /location/of/project/weblinx/modeling/wl_data

For example, if your data is located at /mnt/research/scratch/users/jdoe/WebLINX-full but your cloned weblinx repository is at ~/dev/weblinx, then you'd run:

ln -s /mnt/research/scratch/users/jdoe/WebLINX-full/* ~/dev/weblinx/modeling/wl_data

Which corresponds to the data.base_dir specified in config.yml, which is "${project_dir}/wl_data/demonstrations/".

Dense Markup Ranking (DMR)

Train DMR

You can train the model by running the following command (it will automatically use the hydra config from conf/):

export CUDA_VISIBLE_DEVICES="0" # Set the GPU device you want to use

# Finetune MiniLM-L6-DMR (Default)
python -m dmr.train

# Finetune variant gte or bge
python -m dmr.train +variant=gte
python -m dmr.train +variant=bge

Results will be saved in ./results and checkpoints in ./checkpoints.

Inference for DMR

You need to specify which eval.split you want to evaluate on. For example, to evaluate on the iid split, you can run the following command:

export CUDA_VISIBLE_DEVICES="0" # Set the GPU device you want to use

# On just one
python -m dmr.eval eval.split=valid

# On multiple splits (e.g. test_iid, test_vis)
python -m dmr.eval eval.split=test_iid,test_web,test_geo,test_cat,test_vis

# Or for bge, gte
python -m dmr.eval +variant=gte eval.split=test_iid,test_web,test_geo,test_cat,test_vis
python -m dmr.eval +variant=bge eval.split=test_iid,test_web,test_geo,test_cat,test_vis

Moving generated DMR results to `wl_data/candidates`

The scores.jsonl and results.json files will be saved at the cfg.eval.result_dir variable in modeling/dmr/conf/config.yml, which is by default ${project_dir}/results/${project_name}/${model.name}/${eval.split}, which should by default resolve to /path/to/weblinx/modeling/results/dmr/sentence-transformers/all-MiniLM-L6-v2/train for the train split, .../valid for the valid split, etc. However, since the next steps assumes you have a directory like wl_data/candidates/<split>.json, you need to manually move it. For example, you could run:

# Change the following paths to match your setup
orig_dir="/path/to/weblinx/modeling/results/dmr/sentence-transformers/all-MiniLM-L6-v2"

# This is the directory where the candidates are stored
new_dir="/path/to/wl_data/candidates"

# You need to move the train split if you plan to use it for training the action model
mv $orig_dir/train/scores.jsonl $new_dir/train.jsonl

# You can move valid and test IID splits as well
mv $orig_dir/valid/scores.jsonl $new_dir/valid.jsonl
mv $orig_dir/test_iid/scores.jsonl $new_dir/test_iid.jsonl

# You can move the other OOD test splits as well, after you have run the evaluation
mv $orig_dir/test_web/scores.jsonl $new_dir/test_web.jsonl
mv $orig_dir/test_geo/scores.jsonl $new_dir/test_geo.jsonl
mv $orig_dir/test_cat/scores.jsonl $new_dir/test_cat.jsonl
mv $orig_dir/test_vis/scores.jsonl $new_dir/test_vis.jsonl

Alternatively, you can also update config.yml to save the results in the correct directory, by overriding candidates:

# ...
candidates:
  # ...
  model: "sentence-transformers/all-MiniLM-L6-v2"
  path: ${project_dir}/results/${project_name}/${model.name}/${eval.split}

Action Model

Train LLaMA

You can train the model by running the following command (it will automatically use the hydra config from conf/):

export CUDA_VISIBLE_DEVICES="0" # Set the GPU device you want to use

# Finetune 1.3b variant
python -m llama.train +variant="ft_1.3b"

# Finetune 2.7b variant
python -m llama.train +variant="ft_2.7b"

# For 7b, you will need to use fsdp in accelerate to train on 4 GPUs with 48GB VRAM
export CUDA_VISIBLE_DEVICES="0,1,2,3"
accelerate launch --use_fsdp --config_file llama/accelerate/fsdp_7b.yaml -m llama.train +variant="ft_7b"

# For LLaMA-3-8b-Instruct, you will need to use fsdp in accelerate to train on 4 GPUs with 48GB VRAM
export CUDA_VISIBLE_DEVICES="4,5,6,7"
accelerate launch --use_fsdp --config_file llama/accelerate/fsdp_7b.yaml -m llama.train +variant="ft_llama3_8b_instruct"

# For 13b, you need 6 GPUs with 48GB VRAM
export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5"
accelerate launch --use_fsdp --config_file llama/accelerate/fsdp_13b.yaml -m llama.train +variant="ft_13b"

Results will be saved in ./results and checkpoints in ./checkpoints.

Run LLaMA on Evaluation Splits

You need to specify which eval.split you want to evaluate on. For example, to evaluate on the iid split, you can run the following command:

export CUDA_VISIBLE_DEVICES="0" # Set the GPU device you want to use

# On just one split
python -m llama.eval +variant="ft_1.3b" eval.split=valid

# On multiple splits (e.g. test_iid, test_vis)
python -m llama.eval -m +variant="ft_2.7b" eval.split=test_iid,test_web,test_geo,test_cat,test_vis

# Evaluating llama-3-8b-instruct on all splits
python -m llama.eval -m +variant="ft_llama3_8b_instruct" eval.split=valid,test_iid,test_web,test_geo,test_cat,test_vis

Evaluation

To run the evaluation metrics, you can use the following command (from this directory):

python -m weblinx.eval -d results -b ./wl_data/demonstrations

In this case, -b is the base directory for the demonstrations, and -d is the directory containing the results (generated above by the llama.eval script). This will automatically run the evaluation metrics and save the results in the results/aggregated_scores.json directory. If you are only interested in the overall score for a split (e.g. valid), you can find look for the following entry in the aggregated score file (as an example):

// ...
  {
    "split": "valid",
    "intent": "overall",
    "metric": "overall",
    "model_name": "princeton-nlp/Sheared-LLaMA-1.3B",
    "project_name": "llama_ft",
    "score": 0.21667765869744438,
    "unconditional_score": 0.15307513104251605
  },
// ...

Behind the scene, this will use the weblinx.eval.auto_eval_and_save function to run the evaluation metrics. If you want more control, you can also use that weblinx.eval.auto_eval_and_save function directly if you prefer; for an example, check out weblinx/eval/__main__.py.

Note that it might be slow the first time you run, because it reads a lot of demonstrations and load millions of files. However, a demo-level cache is automatically created (see ./.cache/demonstrations), so the next time you run it, it should be much faster.

More models

Flan-T5

export CUDA_VISIBLE_DEVICES="0" # Set the GPU device you want to use

# Base
python -m flan.train
python -m flan.eval -m eval.split=valid,test_iid,test_web,test_geo,test_cat,test_vis


# Large
python -m modeling.flan.train model.name=google/flan-t5-large
python -m modeling.flan.eval -m model.name=google/flan-t5-large eval.split=valid,test_iid,test_web,test_geo,test_cat,test_vis

# XL
python -m modeling.flan.train +variant=ft_xl
python -m modeling.flan.eval +variant=ft_xl eval.split=valid,test_iid,test_web,test_geo,test_cat,test_vis

MindAct

export CUDA_VISIBLE_DEVICES="0" # Set the GPU device you want to use

# Base
python -m modeling.flan.train +variant=ft_mindact model.size=base
python -m modeling.flan.eval -m +variant=ft_mindact model.size=base eval.split=valid,test_iid,test_web,test_geo,test_cat,test_vis

# Large
python -m modeling.flan.train +variant=ft_mindact model.size=large
python -m modeling.flan.eval -m +variant=ft_mindact model.size=large eval.split=valid,test_iid,test_web,test_geo,test_cat,test_vis

# XL
python -m modeling.flan.train +variant=ft_mindact_xl
python -m modeling.flan.eval -m +variant=ft_mindact_xl eval.split=valid,test_iid,test_web,test_geo,test_cat,test_vis

Pix2Act

First, you will need to download the tff file for the Arial font (aka Arial.TFF) and place it at ${project_dir}/modeling/fonts/Arial.TTF. On Windows, you can find it at C:\windows\fonts\. On Linux, you can find alternative fonts at /usr/share/fonts/truetype/.

export CUDA_VISIBLE_DEVICES="0" # Set the GPU device you want to use

# Base
python -m modeling.pix2act.train
python -m modeling.pix2act.eval eval.split=valid,test_iid,test_web,test_geo,test_cat,test_vis

# Large
python -m modeling.pix2act.train +variant=ft_large
python -m modeling.pix2act.eval +variant=ft_large eval.split=valid,test_iid,test_web,test_geo,test_cat,test_vis

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README.md

README.md

Download Data

Set `WEBLINX_PROJECT_DIR`

Install Dependencies

Optional: Symbolic linking to `WebLINX-full`

Dense Markup Ranking (DMR)

Train DMR

Inference for DMR

Moving generated DMR results to `wl_data/candidates`

Action Model

Train LLaMA

Run LLaMA on Evaluation Splits

Evaluation

More models

Flan-T5

MindAct

Pix2Act

Files

README.md

Latest commit

History

README.md

File metadata and controls

Download Data

Set WEBLINX_PROJECT_DIR

Install Dependencies

Optional: Symbolic linking to WebLINX-full

Dense Markup Ranking (DMR)

Train DMR

Inference for DMR

Moving generated DMR results to wl_data/candidates

Action Model

Train LLaMA

Run LLaMA on Evaluation Splits

Evaluation

More models

Flan-T5

MindAct

Pix2Act

Set `WEBLINX_PROJECT_DIR`

Optional: Symbolic linking to `WebLINX-full`

Moving generated DMR results to `wl_data/candidates`