Image-Text-to-Text
Transformers
Safetensors
gemma3
llama-factory
full
Generated from Trainer
conversational
text-generation-inference
Instructions to use HappyCorpse/ChatShield with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HappyCorpse/ChatShield with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="HappyCorpse/ChatShield") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("HappyCorpse/ChatShield") model = AutoModelForImageTextToText.from_pretrained("HappyCorpse/ChatShield") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use HappyCorpse/ChatShield with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HappyCorpse/ChatShield" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HappyCorpse/ChatShield", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/HappyCorpse/ChatShield
- SGLang
How to use HappyCorpse/ChatShield 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 "HappyCorpse/ChatShield" \ --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": "HappyCorpse/ChatShield", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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 "HappyCorpse/ChatShield" \ --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": "HappyCorpse/ChatShield", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use HappyCorpse/ChatShield with Docker Model Runner:
docker model run hf.co/HappyCorpse/ChatShield
| { | |
| "best_global_step": null, | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 3.0, | |
| "eval_steps": 500, | |
| "global_step": 48, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.625, | |
| "grad_norm": 6.109557766701803, | |
| "learning_rate": 9.78800299954203e-06, | |
| "loss": 1.8311, | |
| "step": 10 | |
| }, | |
| { | |
| "epoch": 1.25, | |
| "grad_norm": 3.414783530388519, | |
| "learning_rate": 7.604701702439652e-06, | |
| "loss": 0.4788, | |
| "step": 20 | |
| }, | |
| { | |
| "epoch": 1.875, | |
| "grad_norm": 3.300993172504628, | |
| "learning_rate": 4.091815745102818e-06, | |
| "loss": 0.3025, | |
| "step": 30 | |
| }, | |
| { | |
| "epoch": 2.5, | |
| "grad_norm": 2.496541962568354, | |
| "learning_rate": 1.04251755785373e-06, | |
| "loss": 0.1666, | |
| "step": 40 | |
| }, | |
| { | |
| "epoch": 3.0, | |
| "step": 48, | |
| "total_flos": 9109050359808.0, | |
| "train_loss": 0.597065252562364, | |
| "train_runtime": 152.3178, | |
| "train_samples_per_second": 19.696, | |
| "train_steps_per_second": 0.315 | |
| } | |
| ], | |
| "logging_steps": 10, | |
| "max_steps": 48, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 3, | |
| "save_steps": 500, | |
| "stateful_callbacks": { | |
| "TrainerControl": { | |
| "args": { | |
| "should_epoch_stop": false, | |
| "should_evaluate": false, | |
| "should_log": false, | |
| "should_save": true, | |
| "should_training_stop": true | |
| }, | |
| "attributes": {} | |
| } | |
| }, | |
| "total_flos": 9109050359808.0, | |
| "train_batch_size": 8, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |