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Model: AI-ModelScope/chinese-llama-2-1.3b Source: Original Platform
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README.md
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---
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license: apache-2.0
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---
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# Chinese-LLaMA-2-1.3B
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**This is the full Chinese-LLaMA-2-1.3B model，which can be loaded directly for inference and full-parameter training.**
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**Related models👇**
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* Long context base models (16K)
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* [Chinese-LLaMA-2-7B-16K (full model)](https://huggingface.co/hfl/chinese-llama-2-7b-16k)
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* [Chinese-LLaMA-2-LoRA-7B-16K (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-7b-16k)
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* [Chinese-LLaMA-2-13B-16K (full model)](https://huggingface.co/hfl/chinese-llama-2-13b-16k)
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* [Chinese-LLaMA-2-LoRA-13B-16K (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-13b-16k)
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* Long context Instruction/Chat models
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* [Chinese-Alpaca-2-7B-16K (full model)](https://huggingface.co/hfl/chinese-alpaca-2-7b-16k)
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* [Chinese-Alpaca-2-LoRA-7B-16K (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-7b-16k)
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* [Chinese-Alpaca-2-13B-16K (full model)](https://huggingface.co/hfl/chinese-alpaca-2-13b-16k)
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* [Chinese-Alpaca-2-LoRA-13B-16K (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-13b-16k)
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* Base models
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* [Chinese-LLaMA-2-7B (full model)](https://huggingface.co/hfl/chinese-llama-2-7b)
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* [Chinese-LLaMA-2-LoRA-7B (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-7b)
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* [Chinese-LLaMA-2-13B (full model)](https://huggingface.co/hfl/chinese-llama-2-13b)
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* [Chinese-LLaMA-2-LoRA-13B (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-13b)
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* Instruction/Chat models
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* [Chinese-Alpaca-2-7B (full model)](https://huggingface.co/hfl/chinese-alpaca-2-7b)
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* [Chinese-Alpaca-2-LoRA-7B (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-7b)
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* [Chinese-Alpaca-2-13B (full model)](https://huggingface.co/hfl/chinese-alpaca-2-13b)
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* [Chinese-Alpaca-2-LoRA-13B (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-13b)
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## 示例代码
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```python
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import torch
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from modelscope import Model, AutoTokenizer
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model = Model.from_pretrained("AI-ModelScope/chinese-llama-2-1.3b", revision='master', device_map='auto', torch_dtype=torch.float16)
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tokenizer = AutoTokenizer.from_pretrained("AI-ModelScope/chinese-llama-2-1.3b", revision='master')
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prompt = """锄禾日当午，"""
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inputs = tokenizer(prompt, return_tensors="pt")
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# Generate
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generate_ids = model.generate(inputs.input_ids.to(model.device), max_length=20)
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print(tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0])
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```
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# Description of Chinese-LLaMA-Alpaca-2
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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.
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The main contents of this project include:
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* 🚀 New extended Chinese vocabulary beyond Llama-2, open-sourcing the Chinese LLaMA-2 and Alpaca-2 LLMs.
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* 🚀 Open-sourced the pre-training and instruction finetuning (SFT) scripts for further tuning on user's data
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* 🚀 Quickly deploy and experience the quantized LLMs on CPU/GPU of personal PC
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* 🚀 Support for LLaMA ecosystems like 🤗transformers, llama.cpp, text-generation-webui, LangChain, vLLM etc.
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Please refer to [https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/) for details.
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config.json
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{
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"_name_or_path": "meta-llama/Llama-2-7b-hf",
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"architectures": [
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"LlamaForCausalLM"
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],
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 11008,
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"max_length": 4096,
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"max_position_embeddings": 4096,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 4,
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"num_key_value_heads": 32,
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.33.2",
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"vocab_size": 55296
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}
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{"framework":"Pytorch","task":"text-generation"}
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generation_config.json
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special_tokens_map.json
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