Instructions to use not-lain/training_the_moon with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use not-lain/training_the_moon with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="not-lain/training_the_moon", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("not-lain/training_the_moon", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use not-lain/training_the_moon with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "not-lain/training_the_moon" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "not-lain/training_the_moon", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/not-lain/training_the_moon
- SGLang
How to use not-lain/training_the_moon 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 "not-lain/training_the_moon" \ --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": "not-lain/training_the_moon", "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 "not-lain/training_the_moon" \ --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": "not-lain/training_the_moon", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use not-lain/training_the_moon with Docker Model Runner:
docker model run hf.co/not-lain/training_the_moon
Training in progress, step 16
Browse files- config.json +16 -0
- model.safetensors +3 -0
- runs/Jun05_22-07-22_158-101-18-255/events.out.tfevents.1717625245.158-101-18-255.10600.0 +3 -0
- runs/Jun05_22-08-15_158-101-18-255/events.out.tfevents.1717625298.158-101-18-255.10600.1 +3 -0
- runs/Jun05_22-08-15_158-101-18-255/events.out.tfevents.1717625310.158-101-18-255.10600.2 +3 -0
- runs/Jun05_22-09-36_158-101-18-255/events.out.tfevents.1717625377.158-101-18-255.10761.0 +3 -0
- training_args.bin +3 -0
config.json
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{
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"_name_or_path": "vikhyatk/moondream2",
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"architectures": [
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"Moondream"
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],
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"auto_map": {
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"AutoConfig": "configuration_moondream.MoondreamConfig",
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"AutoModelForCausalLM": "vikhyatk/moondream2--moondream.Moondream"
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},
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"model_type": "moondream1",
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"text_config": {
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"model_type": "phi"
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},
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"torch_dtype": "float16",
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"transformers_version": "4.41.2"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 3733912224
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runs/Jun05_22-07-22_158-101-18-255/events.out.tfevents.1717625245.158-101-18-255.10600.0
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version https://git-lfs.github.com/spec/v1
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runs/Jun05_22-08-15_158-101-18-255/events.out.tfevents.1717625298.158-101-18-255.10600.1
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version https://git-lfs.github.com/spec/v1
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runs/Jun05_22-08-15_158-101-18-255/events.out.tfevents.1717625310.158-101-18-255.10600.2
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runs/Jun05_22-09-36_158-101-18-255/events.out.tfevents.1717625377.158-101-18-255.10761.0
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version https://git-lfs.github.com/spec/v1
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size 4973
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:79c1e9bebeaf8a0c0ba231b91f6c7e7f99a16606164f25333e9b5f105b76efd0
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size 5112
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