Update Readme (#1)

- Update Readme (c33fbd28d2c48b5c40672ea90486b183a8b98aac)

README.md

@@ -1,3 +1,58 @@
 ---
 license: llama2
+tags:
+- text2text-generation
+pipeline_tag: text2text-generation
+language:
+- zh
+- en
 ---
+
+# 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 !
+
+## Model description
+This model is obtained by fine-tuning the complete parameters using 0.4M Chinese instruction data on the original Llama2-13B-chat. 
+We firmly believe that the original Llama2-chat exhibits commendable performance post Supervised Fine-Tuning (SFT) and Reinforcement Learning with Human Feedback (RLHF).
+Our pursuit continues to be the further enhancement of this model using Chinese instructional data for fine-tuning, with an aspiration to facilitate stable and high-quality 
+Chinese language outputs.
+## Use model
+Please note that the input should be formatted as follows in both **training** and **inference**.
+``` python
+Human: \n{input}\n\nAssistant:\n
+``` 
+
+
+After you decrypt the files, BELLE-Llama2-13B-chat-0.4M can be easily loaded with LlamaForCausalLM.
+``` python
+from transformers import AutoModelForCausalLM, LlamaTokenizer
+import torch
+
+ckpt = '/path/to_finetuned_model/'
+ckpt = '/nfs/a100-80G-15/xytian/myProjects/AI_NLP_GM/transformed_models/BELLE2-Llama2-13B-0.4M'
+device = torch.device('cuda')
+model = AutoModelForCausalLM.from_pretrained(ckpt).half().to(device)
+tokenizer = LlamaTokenizer.from_pretrained(ckpt)
+prompt = "Human: \n写一首中文歌曲，赞美大自然 \n\nAssistant: \n"
+input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
+generate_ids = model.generate(input_ids, max_new_tokens=1024, do_sample=True, top_k=30, top_p=0.85, temperature=0.5, repetition_penalty=1.2, 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):]
+print(response)
+
+```
+
+
+## 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.
+```