Instructions to use AlexWortega/taskGPT2-xl-v0.2a with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AlexWortega/taskGPT2-xl-v0.2a with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="AlexWortega/taskGPT2-xl-v0.2a")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AlexWortega/taskGPT2-xl-v0.2a")
model = AutoModelForCausalLM.from_pretrained("AlexWortega/taskGPT2-xl-v0.2a")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use AlexWortega/taskGPT2-xl-v0.2a with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AlexWortega/taskGPT2-xl-v0.2a"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AlexWortega/taskGPT2-xl-v0.2a",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AlexWortega/taskGPT2-xl-v0.2a

SGLang

How to use AlexWortega/taskGPT2-xl-v0.2a with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "AlexWortega/taskGPT2-xl-v0.2a" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AlexWortega/taskGPT2-xl-v0.2a",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "AlexWortega/taskGPT2-xl-v0.2a" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AlexWortega/taskGPT2-xl-v0.2a",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AlexWortega/taskGPT2-xl-v0.2a with Docker Model Runner:
```
docker model run hf.co/AlexWortega/taskGPT2-xl-v0.2a
```

taskGPT2-xl v0.2a

Model Summary

I finetuned GPT2 on text2code, cot, math and FLAN tasks, on some tasks its performs better than GPT-JT

I create a collection of open techniques and datasets to build taskGPT2-xl:

The model was trained on a large collection of diverse data, including Chain-of-Thought (CoT) not yet, FLAN dataset, Natural-Instructions (NI) dataset .

Quick Start

from transformers import pipeline
pipe = pipeline(model='AlexWortega/taskGPT2-xl')
pipe('''"I love this!" Is it positive? A:''')

from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("taskGPT2-xl")
model = AutoModelForCausalLM.from_pretrained("taskGPT2-xl")

License

The weights of taskGPT2-xl are licensed under version 2.0 of the Apache License.

Training Details

I used datasets from huggingface:

strategyqa_train
aqua_train
qed_train

Hyperparameters

I used Novograd with a learning rate of 2e-5 and global batch size of 6 (3 for each data parallel worker). I use both data parallelism and pipeline parallelism to conduct training. During training, we truncate the input sequence to 512 tokens, and for input sequence that contains less than 512 tokens, we concatenate multiple sequences into one long sequence to improve the data efficiency.

References

#Metrics

SOON

BibTeX entry and citation info

@article{
  title={GPT2xl is underrated task solver},
  author={Nickolich Aleksandr, Karina Romanova, Arseniy Shahmatov, Maksim Gersimenko},
  year={2023}
}

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