Instructions to use xverse/XVERSE-13B-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xverse/XVERSE-13B-Chat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="xverse/XVERSE-13B-Chat", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("xverse/XVERSE-13B-Chat", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use xverse/XVERSE-13B-Chat with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "xverse/XVERSE-13B-Chat" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "xverse/XVERSE-13B-Chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/xverse/XVERSE-13B-Chat
- SGLang
How to use xverse/XVERSE-13B-Chat 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 "xverse/XVERSE-13B-Chat" \ --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": "xverse/XVERSE-13B-Chat", "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 "xverse/XVERSE-13B-Chat" \ --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": "xverse/XVERSE-13B-Chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use xverse/XVERSE-13B-Chat with Docker Model Runner:
docker model run hf.co/xverse/XVERSE-13B-Chat
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README.md
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# XVERSE-13B-Chat
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## 模型介绍
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**XVERSE-13B-Chat**为[**XVERSE-13B**](https://huggingface.co/xverse/XVERSE-13B)模型对齐后的版本。
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# XVERSE-13B-Chat
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## 更新信息
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**[2023/11/06]** 发布新版本的 **XVERSE-13B-2** 底座模型和 **XVERSE-13B-Chat-2** 对话模型,相较于原始版本,新版本的模型训练更加充分(从 1.4T 增加到 3.2T),各方面的能力均得到大幅提升,同时新增工具调用能力。
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**[2023/09/26]** 发布 7B 尺寸的 [XVERSE-7B](https://github.com/xverse-ai/XVERSE-7B) 底座模型和 [XVERSE-7B-Chat](https://github.com/xverse-ai/XVERSE-7B) 对话模型,支持在单张消费级显卡部署运行,并保持高性能、全开源、免费可商用。
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**[2023/08/22]** 发布经过指令精调的 XVERSE-13B-Chat 对话模型。
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**[2023/08/07]** 发布 13B 尺寸的 XVERSE-13B 底座模型。
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## Update Information
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**[2023/11/06]** The new versions of the **XVERSE-13B-2** base model and the **XVERSE-13B-Chat-2** model have been released. Compared to the original versions, the new models have undergone more extensive training (increasing from 1.4T to 3.2T), resulting in significant improvements in all capabilities, along with the addition of Function Call abilities.
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**[2023/09/26]** Released the [XVERSE-7B](https://github.com/xverse-ai/XVERSE-7B) base model and [XVERSE-7B-Chat](https://github.com/xverse-ai/XVERSE-7B) instruct-finetuned model with 7B size, which support deployment and operation on a single consumer-grade graphics card while maintaining high performance, full open source, and free for commercial use.
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**[2023/08/22]** Released the aligned instruct-finetuned model XVERSE-13B-Chat.
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**[2023/08/07]* Released the XVERSE-13B base model.
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## 模型介绍
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**XVERSE-13B-Chat**为[**XVERSE-13B**](https://huggingface.co/xverse/XVERSE-13B)模型对齐后的版本。
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