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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🤗 **Model**: The *weight-diff* version of [BayLing-7B](https://huggingface.co/ICTNLP/bayling-7b-diff) and [BayLing-13B](https://huggingface.co/ICTNLP/bayling-13b-diff), you can quickly get the parameters of BayLing through [apply_delta.py](https://github.com/ictnlp/BayLing/blob/main/apply_delta.py). The HF models of BayLing are anonymized version (exclude BayLing's name in its knowledge), in order to facilitate future LLMs to build upon BayLing.
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> BayLing is developed by [NLP Group](http://nlp.ict.ac.cn/) of [Institute of Computing Technology](
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> BayLing is continuously optimizing 🆙
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> If you have any suggestions, please contact `bayling@ict.ac.cn`. Thanks for your support!
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🤗 **Model**: The *weight-diff* version of [BayLing-7B](https://huggingface.co/ICTNLP/bayling-7b-diff) and [BayLing-13B](https://huggingface.co/ICTNLP/bayling-13b-diff), you can quickly get the parameters of BayLing through [apply_delta.py](https://github.com/ictnlp/BayLing/blob/main/apply_delta.py). The HF models of BayLing are anonymized version (exclude BayLing's name in its knowledge), in order to facilitate future LLMs to build upon BayLing.
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> BayLing is developed by [NLP Group](http://nlp.ict.ac.cn/) of [Institute of Computing Technology](http://www.ict.ac.cn/), [Chinese Academy of Sciences](https://www.cas.cn/) (ICT/CAS)
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> BayLing is continuously optimizing 🆙
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> If you have any suggestions, please contact `bayling@ict.ac.cn`. Thanks for your support!
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