Instructions to use AIXI-AIGC/OCR_MLLM_TOY with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AIXI-AIGC/OCR_MLLM_TOY with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AIXI-AIGC/OCR_MLLM_TOY", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("AIXI-AIGC/OCR_MLLM_TOY", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AIXI-AIGC/OCR_MLLM_TOY with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AIXI-AIGC/OCR_MLLM_TOY" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AIXI-AIGC/OCR_MLLM_TOY", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AIXI-AIGC/OCR_MLLM_TOY
- SGLang
How to use AIXI-AIGC/OCR_MLLM_TOY 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 "AIXI-AIGC/OCR_MLLM_TOY" \ --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": "AIXI-AIGC/OCR_MLLM_TOY", "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 "AIXI-AIGC/OCR_MLLM_TOY" \ --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": "AIXI-AIGC/OCR_MLLM_TOY", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AIXI-AIGC/OCR_MLLM_TOY with Docker Model Runner:
docker model run hf.co/AIXI-AIGC/OCR_MLLM_TOY
| from transformers import AutoModelForCausalLM | |
| import torch | |
| from modelscope import ( | |
| snapshot_download, AutoModelForCausalLM, AutoTokenizer, GenerationConfig | |
| ) | |
| import torch | |
| model_id = 'qwen/Qwen-VL-Chat' | |
| revision = 'v1.0.3' | |
| model_dir = snapshot_download(model_id, revision=revision) | |
| model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", trust_remote_code=True, fp16=True).eval() | |
| state_dict = model.state_dict() | |
| save_dict = {} | |
| for k,v in state_dict.items(): | |
| if 'visual' in k: | |
| if 'transformer.visual.proj' not in k: # we don't need the proj layer | |
| save_dict[k.replace('transformer.visual.', '')] = v | |
| torch.save(save_dict, './qwen_clip/pytorch_model.bin') |