Create README.md

README.md

@@ -0,0 +1,127 @@
+---
+license: other
+tags:
+- text2text-generation
+pipeline_tag: text2text-generation
+language:
+- zh
+- en
+---
+
+Considering LLaMA's license constraints, the model is for research and learning only. 
+Please strictly respect LLaMA's usage policy. We are not allowed to publish weights for LLaMA, of course, even finetuned, but there is no problem publishing the difference, a patch that we suggest to apply to the files. 
+The encryption is a simple XOR between files, ensuring that only the people that have access to the original weights (from completely legal sources, of course) can transform them into finetuned weights. 
+You can find the decrypt code on https://github.com/LianjiaTech/BELLE/tree/main/models .
+
+
+# Model Card for Model ID
+
+## Welcome
+If you find this model helpful, please *like* this model and star us on https://github.com/LianjiaTech/BELLE !
+
+## Update
+A new checkpoint trained with learning rate of 5e-6 is uploaded. 
+In our evaluation, llama trained with smaller lr achieved better performance. 
+
+## Model description
+BELLE-LLAMA-7B-2M-enc is based on LLAMA 7B and finetuned with 2M Chinese data combined with 50,000 pieces of English data from the open source Stanford-Alpaca, resulting in good Chinese instruction understanding and response generation capabilities. 
+
+The code of Chinese data generation and other detailed information can be found in our Github project repository: https://github.com/LianjiaTech/BELLE.
+
+
+## Training hyper-parameters
+| Parameter | Value |
+| ------ | ------ |
+| Batch size | 16 |
+| Learning rate | 5e-6 |
+| Epochs | 3 |
+|Weight_decay | 0.0 |
+|Warmup_rate | 0.03 |
+|LR_scheduler | cosine |
+
+## Download, Convert & Check
+1. After you git clone this model
+```
+md5sum ./*
+45afa71e3067de5119233a57ef9d093d  ./config.json.99a4ef2a26cb38c7f684cb83ed9343f660c561dd5a02a97d1b34b47419324dc5.enc
+f9b33d359f17a437f6c24b4de6f2272e  ./generation_config.json.fd7ff399e5568cc21a0a8414f43df88ef7c424995b9b97a90563165d2cf79efd.enc
+172013287b452114abf5c0e64936f45b  ./pytorch_model-00001-of-00002.bin.166879223b7504f1632d72b1577d57bceaa8fdeee1857c61119e575c50a4aae5.enc
+384f8dc3b6da063c5f7554c52c531c44  ./pytorch_model-00002-of-00002.bin.2319db050dc286cb22c6e08a51a4ec0d9377017a7182a20a12c39eb658f39c80.enc
+2ac1e5262eefd012918724d68813d03e  ./pytorch_model.bin.index.json.f56e69fedde5d28e4f37f2b62f74e8522bbfa13395a6d696d1ef99222a431ab7.enc
+c066b68b4139328e87a694020fc3a6c3  ./special_tokens_map.json.ca3d163bab055381827226140568f3bef7eaac187cebd76878e0b63e9e442356.enc
+2d5d4156fd237fceae85f28d06751020  ./tokenizer_config.json.a672113277a674d753b5cdcfa6bfc860dc69bfcc5511bdccb0c6af3ed08873a0.enc
+39ec1b33fbf9a0934a8ae0f9a24c7163  ./tokenizer.model.9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347.enc
+```
+
+2. Decrypt the files using https://github.com/LianjiaTech/BELLE/tree/main/models#使用说明
+```
+for f in "encrypted"/*; do if [ -f "$f" ]; then python3 decrypt.py "$f" "original/7B/consolidated.00.pth" "result/"; fi; done
+```
+
+3. Check md5sum
+```
+md5sum ./*
+a57bf2d0d7ec2590740bc4175262610b  ./config.json
+2917a1cafb895cf57e746cfd7696bfe5  ./generation_config.json
+252143e5ed0f0073dc5c04159a0f78c2  ./pytorch_model-00001-of-00002.bin
+3f71478bd783685f0a45fc742af85042  ./pytorch_model-00002-of-00002.bin
+d5230ae5fb3bfd12df98af123be53cf5  ./pytorch_model.bin.index.json
+8a80554c91d9fca8acb82f023de02f11  ./special_tokens_map.json
+414f52220807d1300ad700283141de69  ./tokenizer_config.json
+eeec4125e9c7560836b4873b6f8e3025  ./tokenizer.model
+```
+
+## Use model
+Please note that the input should be formatted as follows in both **training** and **inference**.
+``` python
+Human: {input} \n\nAssistant:
+``` 
+
+In order to load BELLE-LLAMA-7B-2M-enc with huggingface transformers, please install the main version, as the latest stable version doesn't support LLAMA (as of March 26, 2023).
+``` python
+pip install git+https://github.com/huggingface/transformers
+```
+
+After you decrypt the files, BELLE-LLAMA-7B-2M can be easily loaded with LlamaForCausalLM.
+``` python
+from transformers import LlamaForCausalLM, AutoTokenizer
+import torch
+
+ckpt = './result/'
+device = torch.device('cuda')
+model = LlamaForCausalLM.from_pretrained(ckpt, device_map='auto', low_cpu_mem_usage=True)
+tokenizer = AutoTokenizer.from_pretrained(ckpt)
+prompt = "Human: 写一首中文歌曲，赞美大自然 \n\nAssistant: "
+input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
+generate_ids = model.generate(input_ids, max_new_tokens=500, do_sample = True, top_k = 30, top_p = 0.85, temperature = 0.5, repetition_penalty=1., eos_token_id=2, bos_token_id=1, pad_token_id=0)
+output = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+response = output[len(prompt):]
+
+```
+
+## Limitations
+There still exists a few issues in the model trained on current base model and data:
+
+1. The model might generate factual errors when asked to follow instructions related to facts.
+
+2. Occasionally generates harmful responses since the model still struggles to identify potential harmful instructions.
+
+3. Needs improvements on reasoning and coding.
+
+Since the model still has its limitations, we require developers only use the open-sourced code, data, model and any other artifacts generated via this project for research purposes. Commercial use and other potential harmful use cases are not allowed.
+
+
+## Citation
+
+Please cite us when using our code, data or model.
+
+```
+@misc{BELLE,
+  author = {Yunjie Ji, Yong Deng, Yan Gong, Yiping Peng, Qiang Niu, Baochang Ma, Xiangang Li},
+  title = {BELLE: Be Everyone's Large Language model Engine},
+  year = {2023},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/LianjiaTech/BELLE}},
+}
+```