Text Generation
Transformers
PyTorch
English
infimm-vicuna
multimodal
text
image
image-to-text
conversational
custom_code
Instructions to use Infi-MM/infimm-vicuna13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
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README.md
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@@ -123,7 +123,7 @@ The model is trained on a mixture of image-text pairs and unstructured multimoda
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| [LAION](https://huggingface.co/datasets/laion/laion2B-en) | Image-Text Pairs | - | 115M | 115M | 1 |
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| [COYO](https://github.com/kakaobrain/coyo-dataset) | Image-Text Pairs | - | 238M | 238M | 1 |
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| [LAION-COCO](https://laion.ai/blog/laion-coco/) | Image-Text Pairs | - | 140M | 140M | 1 |
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| [PMD\*](https://huggingface.co/datasets/facebook/pmd) | Image-Text Pairs | - | 20M | 1
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\*PMD is only used in models with 13B LLMs, not the 7B Zephyr model.
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| 123 |
| [LAION](https://huggingface.co/datasets/laion/laion2B-en) | Image-Text Pairs | - | 115M | 115M | 1 |
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| 124 |
| [COYO](https://github.com/kakaobrain/coyo-dataset) | Image-Text Pairs | - | 238M | 238M | 1 |
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| [LAION-COCO](https://laion.ai/blog/laion-coco/) | Image-Text Pairs | - | 140M | 140M | 1 |
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| [PMD\*](https://huggingface.co/datasets/facebook/pmd) | Image-Text Pairs | - | 20M | 20M | 1 |
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\*PMD is only used in models with 13B LLMs, not the 7B Zephyr model.
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