59 lines
2.3 KiB
Markdown
59 lines
2.3 KiB
Markdown
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---
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license: other
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language:
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- en
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- zh
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library_name: transformers
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pipeline_tag: text-generation
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inference: false
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tags:
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- baichuan
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- llama2
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- baichuan2
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---
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This is the LLaMAfied version of [Baichuan2-7B-Chat](https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat) model by Baichuan Inc.
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This model is converted with https://github.com/hiyouga/LLaMA-Factory/blob/main/tests/llamafy_baichuan2.py
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You may use this model for fine-tuning in downstream tasks, we recommend using our efficient fine-tuning toolkit. https://github.com/hiyouga/LLaMA-Factory
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- **Developed by:** Baichuan Inc.
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- **Language(s) (NLP):** Chinese/English
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- **License:** [Baichuan2 License](https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat/resolve/main/Baichuan2%20%E6%A8%A1%E5%9E%8B%E7%A4%BE%E5%8C%BA%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf)
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Usage:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
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tokenizer = AutoTokenizer.from_pretrained("hiyouga/Baichuan2-7B-Chat-LLaMAfied", use_fast=False)
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model = AutoModelForCausalLM.from_pretrained("hiyouga/Baichuan2-7B-Chat-LLaMAfied").cuda()
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streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
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query = "<reserved_106>晚上睡不着怎么办<reserved_107>"
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inputs = tokenizer([query], return_tensors="pt")
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inputs = inputs.to("cuda")
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generate_ids = model.generate(**inputs, max_new_tokens=256, streamer=streamer)
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```
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You could also alternatively launch a CLI demo by using the script in [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory)
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```bash
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python src/cli_demo.py --template baichuan2 --model_name_or_path hiyouga/Baichuan2-7B-Chat-LLaMAfied
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```
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_hiyouga__Baichuan2-7B-Chat-LLaMAfied)
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| Metric | Value |
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|-----------------------|---------------------------|
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| Avg. | 47.92 |
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| ARC (25-shot) | 52.47 |
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| HellaSwag (10-shot) | 74.04 |
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| MMLU (5-shot) | 53.88 |
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| TruthfulQA (0-shot) | 48.04 |
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| Winogrande (5-shot) | 69.14 |
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| GSM8K (5-shot) | 10.92 |
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| DROP (3-shot) | 26.94 |
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