Instructions to use pruna-test/test-save-tiny-random-llama4-smashed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pruna-test/test-save-tiny-random-llama4-smashed with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="pruna-test/test-save-tiny-random-llama4-smashed")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("pruna-test/test-save-tiny-random-llama4-smashed") model = AutoModelForCausalLM.from_pretrained("pruna-test/test-save-tiny-random-llama4-smashed") - Pruna AI
How to use pruna-test/test-save-tiny-random-llama4-smashed with Pruna AI:
# Use a pipeline as a high-level helper from pruna import PrunaModel pipe = PrunaModel.from_pretrained("pruna-test/test-save-tiny-random-llama4-smashed")from pruna import PrunaModel # Load model directly from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("pruna-test/test-save-tiny-random-llama4-smashed") model = PrunaModel.from_pretrained("pruna-test/test-save-tiny-random-llama4-smashed") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use pruna-test/test-save-tiny-random-llama4-smashed with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "pruna-test/test-save-tiny-random-llama4-smashed" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "pruna-test/test-save-tiny-random-llama4-smashed", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/pruna-test/test-save-tiny-random-llama4-smashed
- SGLang
How to use pruna-test/test-save-tiny-random-llama4-smashed 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 "pruna-test/test-save-tiny-random-llama4-smashed" \ --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": "pruna-test/test-save-tiny-random-llama4-smashed", "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 "pruna-test/test-save-tiny-random-llama4-smashed" \ --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": "pruna-test/test-save-tiny-random-llama4-smashed", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use pruna-test/test-save-tiny-random-llama4-smashed with Docker Model Runner:
docker model run hf.co/pruna-test/test-save-tiny-random-llama4-smashed
Add files using upload-large-folder tool
Browse files- README.md +2 -1
- smash_config.json +1 -0
README.md
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---
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library_name: transformers
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tags:
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- pruna-ai
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---
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# Model Card for pruna-test/test-save-tiny-random-llama4-smashed
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"ifw": false,
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"img2img_denoise": false,
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"ipex_llm": false,
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"llm_int8": false,
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"moe_kernel_tuner": false,
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"pab": false,
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---
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library_name: transformers
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tags:
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- pruna-ai
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- safetensors
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---
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# Model Card for pruna-test/test-save-tiny-random-llama4-smashed
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"ifw": false,
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"img2img_denoise": false,
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"ipex_llm": false,
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"kvpress": false,
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"llm_int8": false,
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"moe_kernel_tuner": false,
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"pab": false,
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smash_config.json
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"ifw": false,
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"img2img_denoise": false,
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"ipex_llm": false,
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"llm_int8": false,
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"moe_kernel_tuner": false,
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"pab": false,
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"ifw": false,
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"img2img_denoise": false,
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"ipex_llm": false,
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"kvpress": false,
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"llm_int8": false,
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"moe_kernel_tuner": false,
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"pab": false,
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