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license: other |
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<h4 align="center"> |
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<b>English</b> | |
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<a href="https://huggingface.co/BAAI/Aquila2-34B/blob/main/README_zh.md">简体中文</a> | |
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</h4> |
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We opensource our **Aquila2** series, now including **Aquila2**, the base language models, namely **Aquila2-7B** and **Aquila2-34B**, as well as **AquilaChat2**, the chat models, namely **AquilaChat2-7B** and **AquilaChat2-34B**, as well as the long-text chat models, namely **AquilaChat2-7B-16k** and **AquilaChat2-34B-16k** |
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The additional details of the Aquila model will be presented in the official technical report. Please stay tuned for updates on official channels. |
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## Updates 2024.6.6 |
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We have updated the basic language model **Aquila2-34B**, which has the following advantages compared to the previous model: |
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* Replaced tokenizer with higher compression ratio: |
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| Tokenizer | Size | Zh | En | Code | Math | Average | |
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|-----------|-------|--------------------------|--------|-------|-------|---------| |
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| Aquila2-original | 100k | **4.70** | 4.42 | 3.20 | 3.77 | 4.02 | |
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| Qwen1.5 | 151k | 4.27 | 4.51 | 3.62 | 3.35 | 3.94 | |
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| Llama3 | 128k | 3.45 | **4.61** | 3.77 | **3.88** | 3.93 | |
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| Aquila2-new | 143k | 4.60 | **4.61** | **3.78** | **3.88** | **4.22** | |
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* The maximum processing length supported by the model has increased from 2048 to 8192 |
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## Quick Start Aquila2-34B |
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### 1. Inference |
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Aquila2-34B is a base model that can be used for continuation. |
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```python |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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from transformers import BitsAndBytesConfig |
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device= "cuda:0" |
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# Model Name |
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model_name = 'BAAI/Aquila2-34B' |
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# load model and tokenizer |
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quantization_config=BitsAndBytesConfig( |
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load_in_4bit=True, |
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bnb_4bit_use_double_quant=True, |
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bnb_4bit_quant_type="nf4", |
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bnb_4bit_compute_dtype=torch.bfloat16, |
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) |
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model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, trust_remote_code=True, |
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# quantization_config=quantization_config # Uncomment this one for 4-bit quantization |
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) |
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tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True) |
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model.eval() |
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model.to(device) |
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# Example |
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text = "The meaning of life is" |
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tokens = tokenizer.encode_plus(text)['input_ids'] |
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tokens = torch.tensor(tokens)[None,].to(device) |
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with torch.no_grad(): |
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out = model.generate(tokens, do_sample=False, max_length=128, eos_token_id=tokenizer.eos_token_id)[0] |
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out = tokenizer.decode(out.cpu().numpy().tolist()) |
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print(out) |
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``` |
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## License |
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Aquila2 series open-source model is licensed under [ BAAI Aquila Model Licence Agreement](https://huggingface.co/BAAI/Aquila2-34B/blob/main/BAAI-Aquila-Model-License%20-Agreement.pdf) |
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