Instructions to use dacorvo/llama-test-upload-folder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dacorvo/llama-test-upload-folder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="dacorvo/llama-test-upload-folder")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("dacorvo/llama-test-upload-folder") model = AutoModelForCausalLM.from_pretrained("dacorvo/llama-test-upload-folder") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use dacorvo/llama-test-upload-folder with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "dacorvo/llama-test-upload-folder" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dacorvo/llama-test-upload-folder", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/dacorvo/llama-test-upload-folder
- SGLang
How to use dacorvo/llama-test-upload-folder 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 "dacorvo/llama-test-upload-folder" \ --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": "dacorvo/llama-test-upload-folder", "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 "dacorvo/llama-test-upload-folder" \ --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": "dacorvo/llama-test-upload-folder", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use dacorvo/llama-test-upload-folder with Docker Model Runner:
docker model run hf.co/dacorvo/llama-test-upload-folder
Neuronx model for meta-llama/Llama-2-7b-hf
This repository contains AWS Inferentia2 and neuronx compatible checkpoints for meta-llama/Llama-2-7b-hf.
You can find detailed information about the base model on its Model Card.
This model has been exported to the neuron format using specific input_shapes and compiler parameters detailed in the paragraphs below.
Please refer to the π€ optimum-neuron documentation for an explanation of these parameters.
Usage on Amazon SageMaker
coming soon
Usage with π€ optimum-neuron
>>> from optimum.neuron import pipeline
>>> p = pipeline('text-generation', 'aws-neuron/Llama-2-7b-hf-neuron-latency')
>>> p("My favorite place on earth is", max_new_tokens=64, do_sample=True, top_k=50)
[{'generated_text': 'My favorite place on earth is the ocean. It is where I feel most
at peace. I love to travel and see new places. I have a'}]
This repository contains tags specific to versions of neuronx. When using with π€ optimum-neuron, use the repo revision specific to the version of neuronx you are using, to load the right serialized checkpoints.
Arguments passed during export
input_shapes
{
"batch_size": 1,
"sequence_length": 2048,
}
compiler_args
{
"auto_cast_type": "fp16",
"num_cores": 24,
}
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