Instructions to use ccore/opt-350m-open-data-understanding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ccore/opt-350m-open-data-understanding with Transformers:

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

pipe = pipeline("text-generation", model="ccore/opt-350m-open-data-understanding")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ccore/opt-350m-open-data-understanding")
model = AutoModelForCausalLM.from_pretrained("ccore/opt-350m-open-data-understanding")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ccore/opt-350m-open-data-understanding with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ccore/opt-350m-open-data-understanding"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ccore/opt-350m-open-data-understanding",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ccore/opt-350m-open-data-understanding

SGLang

How to use ccore/opt-350m-open-data-understanding 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 "ccore/opt-350m-open-data-understanding" \
    --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": "ccore/opt-350m-open-data-understanding",
		"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 "ccore/opt-350m-open-data-understanding" \
        --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": "ccore/opt-350m-open-data-understanding",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ccore/opt-350m-open-data-understanding with Docker Model Runner:
```
docker model run hf.co/ccore/opt-350m-open-data-understanding
```

OPT_350_open_data_understanding

Description

This model has been trained to understand and respond to any content inserted after the [PAPER] tag. It uses advanced language modeling techniques to understand the context, structure, and underlying goals of the input text.

How to use

To interact with this template, place your text after the [PAPER] tag. The model will process the text and respond accordingly. For example:

[PAPER] Your text here...

Example

[PAPER] We present a scalable method to build a high-quality instruction-following language model...

The model will understand and respond to your text according to its context and content.

Comprehension Sections

[UNDERSTANDING]

This section provides a detailed analysis and decomposition of the inserted text, facilitating the understanding of the content.

[QUESTIONS AND ANSWERS]

This section addresses questions and answers that could arise based on the text provided.

[OBJECTION AND REPLY]

This section addresses any objections and responses that could arise from analysis of the text.

Common questions

What can this model do?
- This model can understand and respond to any text placed after the [PAPER] tag.
Is a specific format necessary?
- No, the model is quite flexible regarding the text format.
How does this model perform?
- The model outperforms other LLaMa-based models on the Alpaca leaderboard, demonstrating a highly effective alignment.

Warnings

This model was trained on a diverse corpus, but may still have bias or limitations.
Continuous validation of the model and its output is essential.

Contact and Support

For more information, visit Hugging Face.

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Model tree for ccore/opt-350m-open-data-understanding

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facebook/opt-350m

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(155)

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opt-350m-open-data-understanding