Text Generation
Transformers
PyTorch
Chinese
English
llama
translation
multilingual
large language model
instruction tuning
text-generation-inference
Instructions to use ICTNLP/bayling-7b-diff with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ICTNLP/bayling-7b-diff with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ICTNLP/bayling-7b-diff")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ICTNLP/bayling-7b-diff") model = AutoModelForCausalLM.from_pretrained("ICTNLP/bayling-7b-diff") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use ICTNLP/bayling-7b-diff with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ICTNLP/bayling-7b-diff" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ICTNLP/bayling-7b-diff", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ICTNLP/bayling-7b-diff
- SGLang
How to use ICTNLP/bayling-7b-diff 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 "ICTNLP/bayling-7b-diff" \ --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": "ICTNLP/bayling-7b-diff", "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 "ICTNLP/bayling-7b-diff" \ --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": "ICTNLP/bayling-7b-diff", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ICTNLP/bayling-7b-diff with Docker Model Runner:
docker model run hf.co/ICTNLP/bayling-7b-diff
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README.md
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📄 [**Paper**](https://arxiv.org/abs/2306.10968): A comprehensive research paper of BayLing.
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✍️ [**BayLing-80 Test Set**](https://github.com/ictnlp/BayLing/tree/main/data/BayLing-80): A human-annotated evaluation set comprising multi-turn instructions in both English and Chinese, can be used to evaluate the multilingual and multi-turn interaction capabilities of LLMs.
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📄 [**Paper**](https://arxiv.org/abs/2306.10968): A comprehensive research paper of BayLing.
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🏠 [**Homepage**](http://nlp.ict.ac.cn/bayling): BayLing's homepage. You can discover more information and cases of BayLing here.
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✍️ [**BayLing-80 Test Set**](https://github.com/ictnlp/BayLing/tree/main/data/BayLing-80): A human-annotated evaluation set comprising multi-turn instructions in both English and Chinese, can be used to evaluate the multilingual and multi-turn interaction capabilities of LLMs.
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