transformers pytorch llama text-generation autotrain_compatible text-generation-inference endpoints_compatible region:us
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CuteGPT is an open-source conversational language model that supports both Chinese and English, developed by Fudan University KnowledgeWorks Laboratory. It has a scale of 13B (13 billion) parameters. It can perform int8 precision inference on a single 3090 graphics card. CuteGPT base is pre-trained on Chinese-English corpus. 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.

**Note: Ask The FAIR team of Meta AI for the license for LLAMA usage first.**

```python
from transformers import LlamaForCausalLM, LlamaTokenizer
import torch

def generate_prompt(query, history, input=None):
    prompt = ""
    for i, (old_query, response) in enumerate(history):
        prompt += "{}{}\n<end>".format(old_query, response)
    prompt += "{}".format(query)
    return prompt



# Load model
device = torch.device("cuda:0")
model_name = "/data/dell/xuyipei/my_llama/my_llama_13b/llama_13b_112_sft_v1"
tokenizer = LlamaTokenizer.from_pretrained(model_name)
model = LlamaForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16
)
model.eval()
model = model.to(device)


# Inference
history = []
queries = ['请推荐五本名著，依次列出作品名、作者\n', '请再来三本\n']
memory_limit = 3 # the number of (query, response) to remember
for query in queries:
    prompt = generate_prompt(prompt, history)
    input_ids = tokenizer(query, 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('<end>'),
                pad_token_id = tokenizer.eos_token_id,
                min_length = input_ids.shape[1] + 1
        )
    s = outputs[0]
    response=tokenizer.decode(s)
    response = response.replace('<s>', '').replace('<end>', '').replace('</s>', '')
    print(response)
    history.append((query, response))
    history = history[-memory_limit:]
```