Instructions to use Infi-MM/infimm-vicuna13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Infi-MM/infimm-vicuna13b with Transformers:

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

pipe = pipeline("text-generation", model="Infi-MM/infimm-vicuna13b", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Infi-MM/infimm-vicuna13b", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Infi-MM/infimm-vicuna13b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Infi-MM/infimm-vicuna13b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Infi-MM/infimm-vicuna13b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Infi-MM/infimm-vicuna13b

SGLang

How to use Infi-MM/infimm-vicuna13b 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 "Infi-MM/infimm-vicuna13b" \
    --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": "Infi-MM/infimm-vicuna13b",
		"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 "Infi-MM/infimm-vicuna13b" \
        --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": "Infi-MM/infimm-vicuna13b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Infi-MM/infimm-vicuna13b with Docker Model Runner:
```
docker model run hf.co/Infi-MM/infimm-vicuna13b
```

Ye27 commited on Jan 18, 2024

Commit

e21ab9e

verified ·

1 Parent(s): e4dc3aa

Update README.md

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@@ -238,12 +238,14 @@ We select checkpoint after 1 epoch instruction fine-tuning.
 ## Citation
-@misc{infimm-v1,
-title={InfiMM: },
-author={InfiMM Team},
-year={2024}
 }
 ## License
 <a href="https://creativecommons.org/licenses/by-nc/4.0/deed.en">

 ## Citation
+```latex
+@misc{InfiMM,
+      title={InfiMM: Advancing Multimodal Understanding from Flamingo's Legacy through Diverse LLM Integration},
+      author={InfiMM Team},
+      url={https://huggingface.co/Infi-MM/},
+      year={2024}
 }
+```
 ## License
 <a href="https://creativecommons.org/licenses/by-nc/4.0/deed.en">