Instructions to use robot-learning-group47/eval3_lora_fixed_Chengming with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use robot-learning-group47/eval3_lora_fixed_Chengming with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolVLM2-2.2B-Instruct") model = PeftModel.from_pretrained(base_model, "robot-learning-group47/eval3_lora_fixed_Chengming") - Transformers
How to use robot-learning-group47/eval3_lora_fixed_Chengming with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="robot-learning-group47/eval3_lora_fixed_Chengming") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("robot-learning-group47/eval3_lora_fixed_Chengming", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use robot-learning-group47/eval3_lora_fixed_Chengming with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "robot-learning-group47/eval3_lora_fixed_Chengming" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "robot-learning-group47/eval3_lora_fixed_Chengming", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/robot-learning-group47/eval3_lora_fixed_Chengming
- SGLang
How to use robot-learning-group47/eval3_lora_fixed_Chengming 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 "robot-learning-group47/eval3_lora_fixed_Chengming" \ --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": "robot-learning-group47/eval3_lora_fixed_Chengming", "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 "robot-learning-group47/eval3_lora_fixed_Chengming" \ --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": "robot-learning-group47/eval3_lora_fixed_Chengming", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use robot-learning-group47/eval3_lora_fixed_Chengming with Docker Model Runner:
docker model run hf.co/robot-learning-group47/eval3_lora_fixed_Chengming
| { | |
| "checkpoint": "/home/shadeform/checkpoint/eval3_phase2_TOY", | |
| "data_dir": "/home/shadeform/data/eval3_position_qa_3frames", | |
| "output_dir": "/home/shadeform/outputs/vlm_lora/eval3_phase2_TOY_position_qa_3frames", | |
| "train_examples": 216, | |
| "eval_examples": 54, | |
| "epochs": 5, | |
| "batch_size": 1, | |
| "grad_accum": 8, | |
| "lr": 5e-05, | |
| "global_step": 135, | |
| "eval": { | |
| "examples": 54, | |
| "correct": 54, | |
| "accuracy": 1.0, | |
| "grouped": { | |
| "target_person": { | |
| "Barack Obama": { | |
| "correct": 18, | |
| "total": 18, | |
| "accuracy": 1.0, | |
| "prediction_counts": { | |
| "left": 6, | |
| "middle": 6, | |
| "right": 6 | |
| } | |
| }, | |
| "Taylor Swift": { | |
| "correct": 18, | |
| "total": 18, | |
| "accuracy": 1.0, | |
| "prediction_counts": { | |
| "left": 6, | |
| "middle": 6, | |
| "right": 6 | |
| } | |
| }, | |
| "Yann LeCun": { | |
| "correct": 18, | |
| "total": 18, | |
| "accuracy": 1.0, | |
| "prediction_counts": { | |
| "left": 6, | |
| "middle": 6, | |
| "right": 6 | |
| } | |
| } | |
| }, | |
| "ground_truth": { | |
| "left": { | |
| "correct": 18, | |
| "total": 18, | |
| "accuracy": 1.0, | |
| "prediction_counts": { | |
| "left": 18 | |
| } | |
| }, | |
| "middle": { | |
| "correct": 18, | |
| "total": 18, | |
| "accuracy": 1.0, | |
| "prediction_counts": { | |
| "middle": 18 | |
| } | |
| }, | |
| "right": { | |
| "correct": 18, | |
| "total": 18, | |
| "accuracy": 1.0, | |
| "prediction_counts": { | |
| "right": 18 | |
| } | |
| } | |
| } | |
| }, | |
| "output_csv": "/home/shadeform/outputs/vlm_lora/eval3_phase2_TOY_position_qa_3frames/eval_predictions.csv" | |
| } | |
| } | |