Image-Text-to-Text
Transformers
Safetensors
autonomous-driving
vision-language-model
visual-grounding
chain-of-thought
trajectory-prediction
waymo
Instructions to use hzxbzp/test-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hzxbzp/test-models with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="hzxbzp/test-models")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("hzxbzp/test-models", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use hzxbzp/test-models with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "hzxbzp/test-models" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hzxbzp/test-models", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/hzxbzp/test-models
- SGLang
How to use hzxbzp/test-models 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 "hzxbzp/test-models" \ --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": "hzxbzp/test-models", "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 "hzxbzp/test-models" \ --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": "hzxbzp/test-models", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use hzxbzp/test-models with Docker Model Runner:
docker model run hf.co/hzxbzp/test-models
Commit History
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Card: add the six remaining checkpoints (Alpamayo-R1, Impromptu-VLA, two traj_only)
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Add impromptu-vla-7b/traj_only (v2 best checkpoint)
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Add impromptu-vla-7b/stage2 (v2 best checkpoint)
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Add alpamayo-r1-10b/traj_only (v2 best checkpoint)
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Add alpamayo-r1-10b/stage2 (v2 best checkpoint)
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Add alpamayo-1.5-10b/traj_only (v2 best checkpoint)
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Add qwen2.5-vl-7b/traj_only (v2 best checkpoint)
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Card: drop the Step column from the checkpoint table
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Card: trim to checkpoint table, input/output format and licenses
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Add model card
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Remove dev_metrics.json - weights only, no evaluation outputs
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Add alpamayo-1.5-10b/stage2 (v2 best checkpoint)
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Add cosmos-reason2-8b/traj_only (v2 best checkpoint)
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Add cosmos-reason2-8b/stage2 (v2 best checkpoint)
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Add qwen3-vl-8b/traj_only (v2 best checkpoint)
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Add qwen3-vl-8b/stage2 (v2 best checkpoint)
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Add qwen2.5-vl-7b/stage2 (v2 best checkpoint)
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Add autovla-3b/traj_only (v2 best checkpoint)
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Add autovla-3b/stage2 (v2 best checkpoint)
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Add cosmos-reason2-2b/traj_only (v2 best checkpoint)
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Add cosmos-reason2-2b/stage2 (v2 best checkpoint)
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initial commit
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