Create README.md

README.md

@@ -0,0 +1,84 @@
+CuteGPT is an open-source conversational language model that supports both Chinese and English, developed by [Fudan University KnowledgeWorks Laboratory](http://kw.fudan.edu.cn/). It is based on the original Llama model structure, and has a scale of 13B (13 billion) parameters. It can perform int8 precision inference on a single 3090 graphics card. CuteGPT expands the Chinese vocabulary and performs pre-training on the Llama model, improving its ability to understand Chinese. Subsequently, it is fine-tuned with conversational instructions to enhance the model's ability to understand instructions.
+Based on the KW-CuteGPT-7b version, KW-CuteGPT-13b has improved accuracy in knowledge, understanding of complex instructions, ability to comprehend long texts, reasoning ability, faithful question answering, and other capabilities. Currently, the KW-CuteGPT-13b version model outperforms the majority of models of similar scale in certain evaluation tasks.
+
+```python
+from transformers import LlamaForCausalLM, LlamaTokenizer
+from peft import PeftModel
+import torch
+```
+
+* The prompt template for inference
+
+```python
+overall_instruction = "你是复旦大学知识工场实验室训练出来的语言模型CuteGPT。给定任务描述，请给出对应请求的回答。\n"
+def generate_prompt(query, history, input=None):
+    prompt = overall_instruction
+    for i, (old_query, response) in enumerate(history):
+        prompt += "Q: {}\nA: {}\n".format(old_query, response)
+    prompt += "Q: {}\nA: ".format(query)
+    return prompt
+```
+
+* Load model, tokenizer, here we use lora version of checkpoint
+  * w/o 8bit quantization
+    ```python
+    model_name = "XuYipei/kw-cutegpt-13b-base"
+    LORA_WEIGHTS = "Abbey4799/kw-cutegpt-13b-ift-lora"
+    tokenizer = LlamaTokenizer.from_pretrained(LORA_WEIGHTS)
+    model = LlamaForCausalLM.from_pretrained(
+        model_name,
+        torch_dtype=torch.float16,
+        device_map="auto",
+    )
+    model.eval()
+    model = PeftModel.from_pretrained(model, LORA_WEIGHTS).to(torch.float16)
+    device = torch.device("cuda")
+    ```
+  * w/ 8bit quantization (The performance of the model will experience some degradation after quantization.)
+    ```python
+    model_name = "XuYipei/kw-cutegpt-13b-base"
+    LORA_WEIGHTS = "Abbey4799/kw-cutegpt-13b-ift-lora"
+    tokenizer = LlamaTokenizer.from_pretrained(LORA_WEIGHTS)
+    model = LlamaForCausalLM.from_pretrained(
+        model_name,
+        load_in_8bit=True,
+        torch_dtype=torch.float16,
+        device_map="auto",
+    )
+    model.eval()
+    model = PeftModel.from_pretrained(model, LORA_WEIGHTS)
+    device = torch.device("cuda")
+    ```
+
+* Inference
+
+```python
+history = []
+queries = ['请推荐五本名著，依次列出作品名、作者','再来三本呢？']
+memory_limit = 3 # the number of (query, response) to remember
+for query in queries:
+    prompt = generate_prompt(query, history)
+    print(prompt)
+
+    input_ids = tokenizer(prompt, return_tensors="pt", padding=False, truncation=False, add_special_tokens=False)
+    input_ids = input_ids["input_ids"].to(device)
+
+    with torch.no_grad():
+        outputs=model.generate(
+                input_ids=input_ids,
+                top_p=0.8,
+                top_k=50,
+                repetition_penalty=1.1,
+                max_new_tokens = 256,
+                early_stopping = True,
+                eos_token_id = tokenizer.convert_tokens_to_ids('<s>'),
+                pad_token_id = tokenizer.eos_token_id,
+                min_length = input_ids.shape[1] + 1
+        )
+    s = outputs[0][input_ids.shape[1]:]
+    response=tokenizer.decode(s)
+    response = response.replace('<s>', '').replace('<end>', '').replace('</s>', '')
+    print(response)
+    history.append((query, response))
+    history = history[-memory_limit:]
+```