Instructions to use pruna-test/test-save-tiny-random-llama3-smashed-pro 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-llama3-smashed-pro 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-llama3-smashed-pro")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("pruna-test/test-save-tiny-random-llama3-smashed-pro") model = AutoModelForCausalLM.from_pretrained("pruna-test/test-save-tiny-random-llama3-smashed-pro") - Pruna AI
How to use pruna-test/test-save-tiny-random-llama3-smashed-pro with Pruna AI:
# Use a pipeline as a high-level helper from pruna_pro import PrunaProModel pipe = PrunaProModel.from_pretrained("pruna_pro-test/test-save-tiny-random-llama3-smashed-pro")from pruna_pro import PrunaProModel # Load model directly from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("pruna_pro-test/test-save-tiny-random-llama3-smashed-pro") model = PrunaProModel.from_pretrained("pruna_pro-test/test-save-tiny-random-llama3-smashed-pro") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use pruna-test/test-save-tiny-random-llama3-smashed-pro 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-llama3-smashed-pro" # 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-llama3-smashed-pro", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/pruna-test/test-save-tiny-random-llama3-smashed-pro
- SGLang
How to use pruna-test/test-save-tiny-random-llama3-smashed-pro 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-llama3-smashed-pro" \ --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-llama3-smashed-pro", "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-llama3-smashed-pro" \ --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-llama3-smashed-pro", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use pruna-test/test-save-tiny-random-llama3-smashed-pro with Docker Model Runner:
docker model run hf.co/pruna-test/test-save-tiny-random-llama3-smashed-pro
Add files using upload-large-folder tool
Browse files- README.md +1 -1
- config.json +1 -1
- generation_config.json +1 -1
README.md
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library_name: transformers
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tags:
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- pruna_pro-ai
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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-llama3-smashed-pro
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library_name: transformers
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tags:
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- pruna_pro-ai
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- safetensors
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- pruna-ai
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---
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# Model Card for pruna-test/test-save-tiny-random-llama3-smashed-pro
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config.json
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"transformers_version": "4.
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"use_cache": true,
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"vocab_size": 128256
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}
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"transformers_version": "4.56.2",
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"use_cache": true,
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"vocab_size": 128256
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}
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generation_config.json
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"transformers_version": "4.
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}
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"transformers_version": "4.56.2"
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}
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