Chinese-LLaMA-2-7B

This is the full Chinese-LLaMA-2-7B model，which can be loaded directly for inference and full-parameter training.

Related models?

• Long context base models
• Chinese-LLaMA-2-7B-16K (full model)
• Chinese-LLaMA-2-LoRA-7B-16K (LoRA model)
• Chinese-LLaMA-2-13B-16K (full model)
• Chinese-LLaMA-2-LoRA-13B-16K (LoRA model)
• Base models
• Chinese-LLaMA-2-7B (full model)
• Chinese-LLaMA-2-LoRA-7B (LoRA model)
• Chinese-LLaMA-2-13B (full model)
• Chinese-LLaMA-2-LoRA-13B (LoRA model)
• Instruction/Chat models
• Chinese-Alpaca-2-7B (full model)
• Chinese-Alpaca-2-LoRA-7B (LoRA model)
• Chinese-Alpaca-2-13B (full model)
• Chinese-Alpaca-2-LoRA-13B (LoRA model)

示例代码

from modelscope import AutoModelForCausalLM, AutoTokenizer

model_id = "AI-ModelScope/chinese-llama-2-7b"
tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(model_id, device_map='auto')

text = "你好，我的名字是"
inputs = tokenizer(text, return_tensors="pt")

outputs = model.generate(inputs.input_ids.to(model.device), max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Description of Chinese-LLaMA-Alpaca-2

This project is based on the Llama-2, released by Meta, and it is the second generation of the Chinese LLaMA & Alpaca LLM project. We open-source Chinese LLaMA-2 (foundation model) and Alpaca-2 (instruction-following model). These models have been expanded and optimized with Chinese vocabulary beyond the original Llama-2. We used large-scale Chinese data for incremental pre-training, which further improved the fundamental semantic understanding of the Chinese language, resulting in a significant performance improvement compared to the first-generation models. The relevant models support a 4K context and can be expanded up to 18K+ using the NTK method.

The main contents of this project include:

• ? New extended Chinese vocabulary beyond Llama-2, open-sourcing the Chinese LLaMA-2 and Alpaca-2 LLMs.
• ? Open-sourced the pre-training and instruction finetuning (SFT) scripts for further tuning on user's data
• ? Quickly deploy and experience the quantized LLMs on CPU/GPU of personal PC
• ? Support for LLaMA ecosystems like ?transformers, llama.cpp, text-generation-webui, LangChain, vLLM etc.

Please refer to https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/ for details.