Awesome

LLMindCraft

Shaping Language Models with Cognitive Insights

LLMindCraft by XplainMind Lab is licensed under CC BY-NC-ND 4.0.

Downstream Applications

Logo	Repository	Domain	Paper
	https://github.com/chancefocus/PIXIU	Finance	[1]
	https://github.com/Denilah/CoLLaMA	Code
	https://github.com/SteveKGYang/MentalLLaMA	Mental Health	[2]
	https://github.com/chenhan97/TimeLlama	Temporal Reasoning	[3]
	https://github.com/colfeng/CALM	Credit Scoring	[4]
	https://github.com/Dai-shen/LAiW	Legal	[5]
[	https://github.com/zhiweihu1103/AgriMa	Agriculture

[1] Xie, Q., Han, W., Zhang, X., Lai, Y., Peng, M., Lopez-Lira, A. and Huang, J., 2023. PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance. arXiv preprint arXiv:2306.05443. (Accepted by NeurIPS 2023 Dataset and Benchmark Track)

[2] Yang, K., Zhang, T., Kuang, Z., Xie, Q. and Ananiadou, S., 2023. MentalLLaMA: Interpretable Mental Health Analysis on Social Media with Large Language Models. arXiv preprint arXiv:2309.13567.

[3] Yuan, C., Xie, Q., Huang, J. and Ananiadou, S., 2018. Back to the Future: Towards Explainable Temporal Reasoning with Large Language Models.

[4] Feng, D., Dai, Y., Huang, J., Zhang, Y., Xie, Q., Han, W., Lopez-Lira, A. and Wang, H., 2023. Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Models. arXiv preprint arXiv:2310.00566.

[5] Dai, Y., Feng, D., Huang, J., Jia, H., Xie, Q., Zhang, Y., Han, W., Tian, W. and Wang, H., 2023. LAiW: A Chinese Legal Large Language Models Benchmark (A Technical Report). arXiv preprint arXiv:2310.05620.

Docker environment

docker pull tothemoon/llm

This image packages all environments of LLMindCraft.

Fine-tuning in Docker environment

For single node:

docker run --gpus all \
    -d --rm \
    --name llm \
    [-v host_path:container_path] \
    [-w workdir] \
    --entrypoint "/bin/bash -c" \
    tothemoon/llm \
    --cmd "sleep infinity"

while for multiple nodes:

docker run --gpus all --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 \
    --privileged \
    --network host \
    [--env env_variable=value] \
    -d --rm \
    --name llm \
    [-v host_path:container_path] \
    [-v ssh_pub_key:/root/.ssh/authorized_keys] \
    [-w workdir] \
    tothemoon/llm \
    --sshd_port [any_port] --cmd "sleep infinity"

You can also enter the container by

docker exec -it llm /bin/bash

Create New Dataset

Create a new data class in preprocess.py, like:

Your dataset should be created in the following format:

class MedMCQA(InstructionDataset):
    dataset = "MedMCQA"
    task_type = "classification"
    choices = ["A", "B", "C", "D"]
    prompt = """Given a medical context and a multiple choice question related to it, select the correct answer from the four options.
Question: {text}
Options: {options}.
Please answer with A, B, C, or D only.
Answer:
"""

    def fetch_data(self, datum):
        return {
            "text": datum["question"], "options": ', '.join([op+': '+datum[k] for k, op in zip(['opa', 'opb', 'opc', 'opd'], self.choices)]),
            "answer": self.choices[datum["cop"]-1],
        }

In this format:

• dataset: The dataset name
• task_type: Your task type, should be classification or abstractivesummarization (TODO: More task types)
• prompt: The prompt of the task, which should be later used to be filled with the real data

For Classification tasks, additional keys should be defined:

• choices: Set of labels

fetch_data is the interface for fetching the required features from raw data

And you should also append your class in the dictionary:

DATASETS = {
    "MedMCQA": MedMCQA,
}

Finally, you can build and upload the dataset by:

bash preprocess.sh

Note that the parameters in the preprocess.sh should be changed accordingly. For evaluation datasets, -for_eval should be used, while for instruction tuning datasets, it should be omitted.