Instructions to use NECOUDBFM/Jellyfish-13B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NECOUDBFM/Jellyfish-13B with Transformers:

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

pipe = pipeline("text-generation", model="NECOUDBFM/Jellyfish-13B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NECOUDBFM/Jellyfish-13B")
model = AutoModelForCausalLM.from_pretrained("NECOUDBFM/Jellyfish-13B")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use NECOUDBFM/Jellyfish-13B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NECOUDBFM/Jellyfish-13B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NECOUDBFM/Jellyfish-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/NECOUDBFM/Jellyfish-13B

SGLang

How to use NECOUDBFM/Jellyfish-13B 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 "NECOUDBFM/Jellyfish-13B" \
    --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": "NECOUDBFM/Jellyfish-13B",
		"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 "NECOUDBFM/Jellyfish-13B" \
        --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": "NECOUDBFM/Jellyfish-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use NECOUDBFM/Jellyfish-13B with Docker Model Runner:
```
docker model run hf.co/NECOUDBFM/Jellyfish-13B
```

Update README.md

by chuanxiao1983 - opened Jan 22, 2024

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@@ -37,6 +37,18 @@ More details about the model can be found in the [Jellyfish paper](https://arxiv
 - **License:** Non-Commercial Creative Commons license (CC BY-NC-4.0)
 - **Finetuned from model:** [Open-Orca/OpenOrca-Platypus2-13B](https://huggingface.co/Open-Orca/OpenOrca-Platypus2-13B)
 ## Performance on seen tasks
 |  Task  | Type | Dataset | Non-LLM SoTA<sup>1</sup> | GPT-3.5<sup>2</sup> | GPT-4<sup>2</sup> | Jellyfish-13B-1.1<sup>3</sup>| Jellyfish-13B-Interpreter |

 - **License:** Non-Commercial Creative Commons license (CC BY-NC-4.0)
 - **Finetuned from model:** [Open-Orca/OpenOrca-Platypus2-13B](https://huggingface.co/Open-Orca/OpenOrca-Platypus2-13B)
+## Citation
+If you find our work useful, please give us credit by citing:
+```
+@article{zhang2023jellyfish,
+  title={Jellyfish: A Large Language Model for Data Preprocessing},
+  author={Zhang, Haochen and Dong, Yuyang and Xiao, Chuan and Oyamada, Masafumi},
+  journal={arXiv preprint arXiv:2312.01678},
+  year={2023}
+}
+```
 ## Performance on seen tasks
 |  Task  | Type | Dataset | Non-LLM SoTA<sup>1</sup> | GPT-3.5<sup>2</sup> | GPT-4<sup>2</sup> | Jellyfish-13B-1.1<sup>3</sup>| Jellyfish-13B-Interpreter |