Instructions to use sugiv/Spoonbill-GarudaOtterFlamingoAreFriends-7B-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sugiv/Spoonbill-GarudaOtterFlamingoAreFriends-7B-Chat with Transformers:

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

pipe = pipeline("text-generation", model="sugiv/Spoonbill-GarudaOtterFlamingoAreFriends-7B-Chat")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("sugiv/Spoonbill-GarudaOtterFlamingoAreFriends-7B-Chat", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sugiv/Spoonbill-GarudaOtterFlamingoAreFriends-7B-Chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sugiv/Spoonbill-GarudaOtterFlamingoAreFriends-7B-Chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sugiv/Spoonbill-GarudaOtterFlamingoAreFriends-7B-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sugiv/Spoonbill-GarudaOtterFlamingoAreFriends-7B-Chat

SGLang

How to use sugiv/Spoonbill-GarudaOtterFlamingoAreFriends-7B-Chat 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 "sugiv/Spoonbill-GarudaOtterFlamingoAreFriends-7B-Chat" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sugiv/Spoonbill-GarudaOtterFlamingoAreFriends-7B-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "sugiv/Spoonbill-GarudaOtterFlamingoAreFriends-7B-Chat" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sugiv/Spoonbill-GarudaOtterFlamingoAreFriends-7B-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sugiv/Spoonbill-GarudaOtterFlamingoAreFriends-7B-Chat with Docker Model Runner:
```
docker model run hf.co/sugiv/Spoonbill-GarudaOtterFlamingoAreFriends-7B-Chat
```

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Dubbed Spoonbill Garuda version used instruction tuned sugiv/garuda-from-llama2-7B-chat as languade model. The above said Spoonbill Garuda is also vision-language model which was also trained on visual instruction datasets (limited from Otter).

Please refer to license of Llama2 from which Garuda was derived with Alpaca dataset.
The above is also fine-tuned on visual instruction tuning datasets derived from Otter's datasets.
https://ai.meta.com/llama/license/
@software{anas_awadalla_2023_7733589, author = {Awadalla, Anas and Gao, Irena and Gardner, Joshua and Hessel, Jack and Hanafy, Yusuf and Zhu, Wanrong and Marathe, Kalyani and Bitton, Yonatan and Gadre, Samir and Jitsev, Jenia and Kornblith, Simon and Koh, Pang Wei and Ilharco, Gabriel and Wortsman, Mitchell and Schmidt, Ludwig}, title = {OpenFlamingo}, month = mar, year = 2023, publisher = {Zenodo}, version = {v0.1.1}, doi = {10.5281/zenodo.7733589}, url = {https://doi.org/10.5281/zenodo.7733589} }
@article{li2023otter, title={Otter: A Multi-Modal Model with In-Context Instruction Tuning}, author={Li, Bo and Zhang, Yuanhan and Chen, Liangyu and Wang, Jinghao and Yang, Jingkang and Liu, Ziwei}, journal={arXiv preprint arXiv:2305.03726}, year={2023} }

@article{li2023mimicit, title={MIMIC-IT: Multi-Modal In-Context Instruction Tuning}, author={Bo Li and Yuanhan Zhang and Liangyu Chen and Jinghao Wang and Fanyi Pu and Jingkang Yang and Chunyuan Li and Ziwei Liu}, year={2023}, eprint={2306.05425}, archivePrefix={arXiv}, primaryClass={cs.CV} }

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Dataset used to train sugiv/Spoonbill-GarudaOtterFlamingoAreFriends-7B-Chat

Papers for sugiv/Spoonbill-GarudaOtterFlamingoAreFriends-7B-Chat

MIMIC-IT: Multi-Modal In-Context Instruction Tuning

Paper • 2306.05425 • Published Jun 8, 2023 • 12

Otter: A Multi-Modal Model with In-Context Instruction Tuning

Paper • 2305.03726 • Published May 5, 2023 • 7