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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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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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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ICE-GRT is a chat assistant trained by Reinforcement Learning from Human Feedback (RLHF) by Vicuna model.
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- ICE-GRT: Instruction Context Enhancement by Generative Reinforcement based Transformers
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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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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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## Ice-breaking!
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The choice of the acronym "ICE" for ICE-GRT is deliberate. It represents our aspiration to catalyze an 'ice-breaking' moment in the research of LLMs.This reflects our hope to encourage researchers to explore and realize the broad possibilities of ICE-GRT in a range of tasks. We aim to pave the way for novel discoveries and advancements in the field, demonstrating that the capabilities of our model are as extensive and varied as they are innovative.
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## Model Performance on 12 different Tasks
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