Adding Evaluation Results

README.md

@@ -1,75 +1,3 @@
----
-license: openrail
-datasets:
-- shareAI/ShareGPT-Chinese-English-90k
-- shareAI/CodeChat
-language:
-- zh
-library_name: transformers
-tags:
-- code
----
-
-## CodeLlaMa模型的中文化版本 （支持多轮对话）
-
-科普：CodeLlaMa是专门用于代码助手的，与ChineseLlaMa不同，适用于代码类问题的回复。  
-用于多轮对话的推理代码：  
-（可以直接复制运行，默认会自动拉取该模型权重）  
-
-关联Github仓库：https://github.com/CrazyBoyM/CodeLLaMA-chat  
-
-```
-# from Firefly
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-
-
-def main():
-    model_name = 'shareAI/CodeLLaMA-chat-13b-Chinese'
-
-    device = 'cuda'
-    max_new_tokens = 500    # 每轮对话最多生成多少个token
-    history_max_len = 1000  # 模型记忆的最大token长度
-    top_p = 0.9
-    temperature = 0.35
-    repetition_penalty = 1.0
-
-    model = AutoModelForCausalLM.from_pretrained(
-        model_name,
-        trust_remote_code=True,
-        low_cpu_mem_usage=True,
-        torch_dtype=torch.float16,
-        device_map='auto'
-    ).to(device).eval()
-    tokenizer = AutoTokenizer.from_pretrained(
-        model_name,
-        trust_remote_code=True,
-        use_fast=False
-    )
-
-
-    history_token_ids = torch.tensor([[]], dtype=torch.long)
-
-    user_input = input('User：')
-    while True:
-        input_ids = tokenizer(user_input, return_tensors="pt", add_special_tokens=False).input_ids
-        eos_token_id = torch.tensor([[tokenizer.eos_token_id]], dtype=torch.long)
-        user_input_ids = torch.concat([input_ids, eos_token_id], dim=1)
-        history_token_ids = torch.concat((history_token_ids, user_input_ids), dim=1)
-        model_input_ids = history_token_ids[:, -history_max_len:].to(device)
-        with torch.no_grad():
-            outputs = model.generate(
-                input_ids=model_input_ids, max_new_tokens=max_new_tokens, do_sample=True, top_p=top_p,
-                temperature=temperature, repetition_penalty=repetition_penalty, eos_token_id=tokenizer.eos_token_id
-            )
-        model_input_ids_len = model_input_ids.size(1)
-        response_ids = outputs[:, model_input_ids_len:]
-        history_token_ids = torch.concat((history_token_ids, response_ids.cpu()), dim=1)
-        response = tokenizer.batch_decode(response_ids)
-        print("Bot：" + response[0].strip().replace(tokenizer.eos_token, ""))
-        user_input = input('User：')
-
-
-if __name__ == '__main__':
-    main()
-```
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