Instructions to use akoksal/LongForm-OPT-2.7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use akoksal/LongForm-OPT-2.7B with Transformers:

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

pipe = pipeline("text-generation", model="akoksal/LongForm-OPT-2.7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("akoksal/LongForm-OPT-2.7B")
model = AutoModelForCausalLM.from_pretrained("akoksal/LongForm-OPT-2.7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use akoksal/LongForm-OPT-2.7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "akoksal/LongForm-OPT-2.7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "akoksal/LongForm-OPT-2.7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/akoksal/LongForm-OPT-2.7B

SGLang

How to use akoksal/LongForm-OPT-2.7B 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 "akoksal/LongForm-OPT-2.7B" \
    --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": "akoksal/LongForm-OPT-2.7B",
		"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 "akoksal/LongForm-OPT-2.7B" \
        --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": "akoksal/LongForm-OPT-2.7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use akoksal/LongForm-OPT-2.7B with Docker Model Runner:
```
docker model run hf.co/akoksal/LongForm-OPT-2.7B
```

akoksal commited on Mar 5, 2024

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@@ -44,8 +44,9 @@ datasets:
 ## LongForm-OPT-2.7B
 The LongForm dataset is created by leveraging English corpus examples with reverse instructions. We select a diverse set of human-written documents from existing corpora such as C4 and Wikipedia and generate instructions for the given documents via LLMs. Then, we extend these examples with structured corpora examples such as Stack Exchange and WikiHow and task examples such as question answering, email writing, grammar error correction, story/poem generation, and text summarization.
 Github Repo: https://github.com/akoksal/LongForm
 ### For LongForm OPT and LLaMA models: Use [EOI] to indicate the end of instruction.

 ## LongForm-OPT-2.7B
 The LongForm dataset is created by leveraging English corpus examples with reverse instructions. We select a diverse set of human-written documents from existing corpora such as C4 and Wikipedia and generate instructions for the given documents via LLMs. Then, we extend these examples with structured corpora examples such as Stack Exchange and WikiHow and task examples such as question answering, email writing, grammar error correction, story/poem generation, and text summarization.
 Github Repo: https://github.com/akoksal/LongForm
+![The LongForm dataset](https://github.com/akoksal/LongForm/blob/main/figures/intro_example.jpg?raw=true)
 ### For LongForm OPT and LLaMA models: Use [EOI] to indicate the end of instruction.