Instructions to use zhengchenphd/ICE-GRT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zhengchenphd/ICE-GRT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="zhengchenphd/ICE-GRT")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("zhengchenphd/ICE-GRT") model = AutoModelForCausalLM.from_pretrained("zhengchenphd/ICE-GRT") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use zhengchenphd/ICE-GRT with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "zhengchenphd/ICE-GRT" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zhengchenphd/ICE-GRT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/zhengchenphd/ICE-GRT
- SGLang
How to use zhengchenphd/ICE-GRT 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 "zhengchenphd/ICE-GRT" \ --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": "zhengchenphd/ICE-GRT", "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 "zhengchenphd/ICE-GRT" \ --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": "zhengchenphd/ICE-GRT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use zhengchenphd/ICE-GRT with Docker Model Runner:
docker model run hf.co/zhengchenphd/ICE-GRT
Chen Zheng commited on
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Parent(s): f349811
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README.md
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# ICE-GRT Model Card
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## Model Details
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ICE-GRT is a chat assistant trained by Reinforcement Learning from Human Feedback (RLHF) by Vicuna model.
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- Model type: An auto-regressive language model based on the transformer architecture
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- License: The same as lmsys/vicuna-13b-v1.5
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- Finetuned from model: lmsys/vicuna-13b-v1.5
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## Model Sources
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Paper 1 (SFT): https://arxiv.org/abs/2310.04945
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Paper 2 (RLHF): Publish soon!
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## Uses
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The primary use of Vicuna is research on large language models and chatbots. The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.
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