Instructions to use p208p2002/gpt2-medium-babi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use p208p2002/gpt2-medium-babi with Transformers:

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

pipe = pipeline("text-generation", model="p208p2002/gpt2-medium-babi")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("p208p2002/gpt2-medium-babi")
model = AutoModelForCausalLM.from_pretrained("p208p2002/gpt2-medium-babi")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use p208p2002/gpt2-medium-babi with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "p208p2002/gpt2-medium-babi"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "p208p2002/gpt2-medium-babi",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/p208p2002/gpt2-medium-babi

SGLang

How to use p208p2002/gpt2-medium-babi 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 "p208p2002/gpt2-medium-babi" \
    --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": "p208p2002/gpt2-medium-babi",
		"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 "p208p2002/gpt2-medium-babi" \
        --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": "p208p2002/gpt2-medium-babi",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use p208p2002/gpt2-medium-babi with Docker Model Runner:
```
docker model run hf.co/p208p2002/gpt2-medium-babi
```

Fine tune and evaluate transformer model on facebook's bAbi tasks.

Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks

Training Code: p208p2002/bAbi-tasks-with-transformer-model

task_no	task_name	score
qa1	single-supporting-fact	100
qa2	two-supporting-facts	99.4
qa3	three-supporting-facts	62.0
qa4	two-arg-relations	100
qa5	three-arg-relations	96.7
qa6	yes-no-questions	100
qa7	counting	100
qa8	lists-sets	97.7
qa9	simple-negation	100
qa10	indefinite-knowledge	100
qa11	basic-coreference	100
qa12	conjunction	100
qa13	compound-coreference	100
qa14	time-reasoning	100
qa15	basic-deduction	100
qa16	basic-induction	100
qa17	positional-reasoning	100
qa18	size-reasoning	100
qa19	path-finding	100
qa20	agents-motivations	100

# Please use with the follow template

INPUT_TEMPLATE = """
Context:
{context}

Question:
{question}

Answer:
{answer}
"""

input_text = INPUT_TEMPLATE.format_map({
    "context":context,
    "question":question,
    "answer":answer
}).strip()

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Dataset used to train p208p2002/gpt2-medium-babi

Collection including p208p2002/gpt2-medium-babi

Babi Models

Collection

3 items • Updated Mar 26, 2024

Paper for p208p2002/gpt2-medium-babi

Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks

Paper • 1502.05698 • Published Feb 19, 2015