Instructions to use jburtoft/TencentARC-LLaMA-Pro-8B-Neuron with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jburtoft/TencentARC-LLaMA-Pro-8B-Neuron with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jburtoft/TencentARC-LLaMA-Pro-8B-Neuron")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("jburtoft/TencentARC-LLaMA-Pro-8B-Neuron") model = AutoModelForCausalLM.from_pretrained("jburtoft/TencentARC-LLaMA-Pro-8B-Neuron") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use jburtoft/TencentARC-LLaMA-Pro-8B-Neuron with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jburtoft/TencentARC-LLaMA-Pro-8B-Neuron" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jburtoft/TencentARC-LLaMA-Pro-8B-Neuron", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jburtoft/TencentARC-LLaMA-Pro-8B-Neuron
- SGLang
How to use jburtoft/TencentARC-LLaMA-Pro-8B-Neuron 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 "jburtoft/TencentARC-LLaMA-Pro-8B-Neuron" \ --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": "jburtoft/TencentARC-LLaMA-Pro-8B-Neuron", "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 "jburtoft/TencentARC-LLaMA-Pro-8B-Neuron" \ --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": "jburtoft/TencentARC-LLaMA-Pro-8B-Neuron", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use jburtoft/TencentARC-LLaMA-Pro-8B-Neuron with Docker Model Runner:
docker model run hf.co/jburtoft/TencentARC-LLaMA-Pro-8B-Neuron
TencentARC-LLaMA-Pro-8B-Neuron / checkpoint /pytorch_model.bin /p131.model.layers.14.mlp.gate_proj.weight
- Xet hash:
- 9715bee9ab4cd7491c87a8d50a4e767a3934e1c60877e29928d6cf7f3619ab96
- Size of remote file:
- 180 MB
- SHA256:
- e62173af9865082f2c51d60b634a4e005cbef211474fbda81140a00bc681b1e6
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