Instructions to use MYTH-Lab/VW-LMM-Vicuna-pif-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MYTH-Lab/VW-LMM-Vicuna-pif-7b with Transformers:

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

pipe = pipeline("text-generation", model="MYTH-Lab/VW-LMM-Vicuna-pif-7b")

# Load model directly
from transformers import AutoProcessor, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("MYTH-Lab/VW-LMM-Vicuna-pif-7b")
model = AutoModelForCausalLM.from_pretrained("MYTH-Lab/VW-LMM-Vicuna-pif-7b")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MYTH-Lab/VW-LMM-Vicuna-pif-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MYTH-Lab/VW-LMM-Vicuna-pif-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MYTH-Lab/VW-LMM-Vicuna-pif-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MYTH-Lab/VW-LMM-Vicuna-pif-7b

SGLang

How to use MYTH-Lab/VW-LMM-Vicuna-pif-7b 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 "MYTH-Lab/VW-LMM-Vicuna-pif-7b" \
    --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": "MYTH-Lab/VW-LMM-Vicuna-pif-7b",
		"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 "MYTH-Lab/VW-LMM-Vicuna-pif-7b" \
        --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": "MYTH-Lab/VW-LMM-Vicuna-pif-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MYTH-Lab/VW-LMM-Vicuna-pif-7b with Docker Model Runner:
```
docker model run hf.co/MYTH-Lab/VW-LMM-Vicuna-pif-7b
```

Potentialts commited on Mar 14, 2024

Commit

98d3f30

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@@ -1,5 +1,6 @@
 ---
 inference: false
 ---
 # VW-LMM Model Card
@@ -26,3 +27,17 @@ https://github.com/pengts/VW-LMM
 Llama 2 is licensed under the LLAMA 2 Community License,
 Copyright (c) Meta Platforms, Inc. All Rights Reserved.

 ---
 inference: false
+library_name: transformers
 ---
 # VW-LMM Model Card
 Llama 2 is licensed under the LLAMA 2 Community License,
 Copyright (c) Meta Platforms, Inc. All Rights Reserved.
+## Citation
+If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil:.
+```BibTeX
+@misc{peng2024multimodal,
+      title={Multi-modal Auto-regressive Modeling via Visual Words},
+      author={Tianshuo Peng and Zuchao Li and Lefei Zhang and Hai Zhao and Ping Wang and Bo Du},
+      year={2024},
+      eprint={2403.07720},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```