Image-Text-to-Text
Transformers
Safetensors
idefics2
Generated from Trainer
text-generation-inference
Instructions to use mqliu/mantis-8b-idefics2_1024 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mqliu/mantis-8b-idefics2_1024 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="mqliu/mantis-8b-idefics2_1024")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("mqliu/mantis-8b-idefics2_1024") model = AutoModelForMultimodalLM.from_pretrained("mqliu/mantis-8b-idefics2_1024", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use mqliu/mantis-8b-idefics2_1024 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mqliu/mantis-8b-idefics2_1024" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mqliu/mantis-8b-idefics2_1024", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mqliu/mantis-8b-idefics2_1024
- SGLang
How to use mqliu/mantis-8b-idefics2_1024 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 "mqliu/mantis-8b-idefics2_1024" \ --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": "mqliu/mantis-8b-idefics2_1024", "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 "mqliu/mantis-8b-idefics2_1024" \ --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": "mqliu/mantis-8b-idefics2_1024", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use mqliu/mantis-8b-idefics2_1024 with Docker Model Runner:
docker model run hf.co/mqliu/mantis-8b-idefics2_1024
Download generation_config.json from mqliu/mantis-8b-idefics2_1024: direct link, hf CLI and curl.
- Browser
- Download file 228 Bytes
-
https://huggingface.co/mqliu/mantis-8b-idefics2_1024/resolve/main/generation_config.json
- Command line
-
hf download hf://mqliu/mantis-8b-idefics2_1024/generation_config.json
-
curl -L -o generation_config.json https://huggingface.co/mqliu/mantis-8b-idefics2_1024/resolve/main/generation_config.json
228 Bytes
| { | |
| "_from_model_config": true, | |
| "bad_words_ids": [ | |
| [ | |
| 32000 | |
| ], | |
| [ | |
| 32001 | |
| ] | |
| ], | |
| "bos_token_id": 1, | |
| "eos_token_id": [ | |
| 2, | |
| 32002 | |
| ], | |
| "pad_token_id": 0, | |
| "transformers_version": "4.42.3" | |
| } | |