Instructions to use luodian/Flamingo-Llama2-Chat7B-CC3M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use luodian/Flamingo-Llama2-Chat7B-CC3M with Transformers:

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

pipe = pipeline("text-generation", model="luodian/Flamingo-Llama2-Chat7B-CC3M")

# Load model directly
from transformers import AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("luodian/Flamingo-Llama2-Chat7B-CC3M", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use luodian/Flamingo-Llama2-Chat7B-CC3M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "luodian/Flamingo-Llama2-Chat7B-CC3M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "luodian/Flamingo-Llama2-Chat7B-CC3M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/luodian/Flamingo-Llama2-Chat7B-CC3M

SGLang

How to use luodian/Flamingo-Llama2-Chat7B-CC3M 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 "luodian/Flamingo-Llama2-Chat7B-CC3M" \
    --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": "luodian/Flamingo-Llama2-Chat7B-CC3M",
		"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 "luodian/Flamingo-Llama2-Chat7B-CC3M" \
        --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": "luodian/Flamingo-Llama2-Chat7B-CC3M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use luodian/Flamingo-Llama2-Chat7B-CC3M with Docker Model Runner:
```
docker model run hf.co/luodian/Flamingo-Llama2-Chat7B-CC3M
```

TLDR: We trained a Flamingo with Llama2-Chat7B as LLM on CC3M in less than 5 hours using just 4 A100s.

The model showed promising zero-shot captioning skills. High-quality captioning data really helps fast alignment.

You could test it via following code. Be sure to visit Otter to get necessary Flamingo/Otter models.

from flamingo.modeling_flamingo import FlamingoForConditionalGeneration
flamingo_model = FlamingoForConditionalGeneration.from_pretrained("luodian/Flamingo-Llama2-Chat7B-CC3M", device_map=auto)
prompt = "<image>an image of"
simple_prompt = "<image>"

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