Instructions to use aws-neuron/SOLAR-10.7B-v1.0-neuron with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aws-neuron/SOLAR-10.7B-v1.0-neuron with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="aws-neuron/SOLAR-10.7B-v1.0-neuron")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("aws-neuron/SOLAR-10.7B-v1.0-neuron") model = AutoModelForCausalLM.from_pretrained("aws-neuron/SOLAR-10.7B-v1.0-neuron", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use aws-neuron/SOLAR-10.7B-v1.0-neuron with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "aws-neuron/SOLAR-10.7B-v1.0-neuron" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "aws-neuron/SOLAR-10.7B-v1.0-neuron", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/aws-neuron/SOLAR-10.7B-v1.0-neuron
- SGLang
How to use aws-neuron/SOLAR-10.7B-v1.0-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 "aws-neuron/SOLAR-10.7B-v1.0-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": "aws-neuron/SOLAR-10.7B-v1.0-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 "aws-neuron/SOLAR-10.7B-v1.0-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": "aws-neuron/SOLAR-10.7B-v1.0-neuron", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use aws-neuron/SOLAR-10.7B-v1.0-neuron with Docker Model Runner:
docker model run hf.co/aws-neuron/SOLAR-10.7B-v1.0-neuron
b103556ce7f2bcb66146e0f9f4048f151c32e65544b0e2fd199d6fd382f079a8
Browse files- checkpoint/pytorch_model.bin/p304.model.layers.33.mlp.down_proj.weight +3 -0
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- checkpoint/pytorch_model.bin/p309.model.layers.34.self_attn.v_proj.weight +3 -0
- checkpoint/pytorch_model.bin/p31.model.layers.3.self_attn.o_proj.weight +3 -0
- checkpoint/pytorch_model.bin/p310.model.layers.34.self_attn.o_proj.weight +3 -0
- checkpoint/pytorch_model.bin/p311.model.layers.34.mlp.gate_proj.weight +3 -0
- checkpoint/pytorch_model.bin/p312.model.layers.34.mlp.up_proj.weight +3 -0
- checkpoint/pytorch_model.bin/p313.model.layers.34.mlp.down_proj.weight +3 -0
- checkpoint/pytorch_model.bin/p314.model.layers.34.input_layernorm.weight +3 -0
- checkpoint/pytorch_model.bin/p315.model.layers.34.post_attention_layernorm.weight +3 -0
- checkpoint/pytorch_model.bin/p316.model.layers.35.self_attn.q_proj.weight +3 -0
- checkpoint/pytorch_model.bin/p317.model.layers.35.self_attn.k_proj.weight +3 -0
- checkpoint/pytorch_model.bin/p318.model.layers.35.self_attn.v_proj.weight +3 -0
- checkpoint/pytorch_model.bin/p319.model.layers.35.self_attn.o_proj.weight +3 -0
- checkpoint/pytorch_model.bin/p32.model.layers.3.mlp.gate_proj.weight +3 -0
- checkpoint/pytorch_model.bin/p320.model.layers.35.mlp.gate_proj.weight +3 -0
- checkpoint/pytorch_model.bin/p321.model.layers.35.mlp.up_proj.weight +3 -0
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