Interface with LLaMa2 from huggingface

README.md

@@ -27,7 +27,7 @@ To translate this project to arbitary language with GPT, read and run [`multi_la
 
 功能（⭐= 近期新增功能） | 描述
 --- | ---
-⭐[接入新模型](https://github.com/binary-husky/gpt_academic/wiki/%E5%A6%82%E4%BD%95%E5%88%87%E6%8D%A2%E6%A8%A1%E5%9E%8B)！ | ⭐阿里达摩院[通义千问](https://modelscope.cn/models/qwen/Qwen-7B-Chat/summary)，上海AI-Lab[书生](https://github.com/InternLM/InternLM)，讯飞[星火](https://xinghuo.xfyun.cn/)
+⭐[接入新模型](https://github.com/binary-husky/gpt_academic/wiki/%E5%A6%82%E4%BD%95%E5%88%87%E6%8D%A2%E6%A8%A1%E5%9E%8B)！ | ⭐阿里达摩院[通义千问](https://modelscope.cn/models/qwen/Qwen-7B-Chat/summary)，上海AI-Lab[书生](https://github.com/InternLM/InternLM)，讯飞[星火](https://xinghuo.xfyun.cn/)，[LLaMa2](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)
 一键润色 | 支持一键润色、一键查找论文语法错误
 一键中英互译 | 一键中英互译
 一键代码解释 | 显示代码、解释代码、生成代码、给代码加注释

config.py

@@ -149,4 +149,8 @@ ANTHROPIC_API_KEY = ""
 
 
 # 自定义API KEY格式
-CUSTOM_API_KEY_PATTERN = ""
+CUSTOM_API_KEY_PATTERN = ""
+
+
+# HUGGINGFACE的TOKEN 下载LLAMA时起作用 https://huggingface.co/docs/hub/security-tokens
+HUGGINGFACE_ACCESS_TOKEN = "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"

request_llm/bridge_all.py

@@ -385,6 +385,22 @@ if "spark" in AVAIL_LLM_MODELS:   # 讯飞星火认知大模型
         })
     except:
         print(trimmed_format_exc())
+if "llama2" in AVAIL_LLM_MODELS:   # 讯飞星火认知大模型
+    try:
+        from .bridge_llama2 import predict_no_ui_long_connection as llama2_noui
+        from .bridge_llama2 import predict as llama2_ui
+        model_info.update({
+            "llama2": {
+                "fn_with_ui": llama2_ui,
+                "fn_without_ui": llama2_noui,
+                "endpoint": None,
+                "max_token": 4096,
+                "tokenizer": tokenizer_gpt35,
+                "token_cnt": get_token_num_gpt35,
+            }
+        })
+    except:
+        print(trimmed_format_exc())
 
 
 

request_llm/bridge_llama2.py

@@ -0,0 +1,91 @@
+model_name = "LLaMA"
+cmd_to_install = "`pip install -r request_llm/requirements_chatglm.txt`"
+
+
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+from toolbox import update_ui, get_conf, ProxyNetworkActivate
+from multiprocessing import Process, Pipe
+from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns, SingletonLocalLLM
+from threading import Thread
+
+
+# ------------------------------------------------------------------------------------------------------------------------
+# 🔌💻 Local Model
+# ------------------------------------------------------------------------------------------------------------------------
+@SingletonLocalLLM
+class GetONNXGLMHandle(LocalLLMHandle):
+
+    def load_model_info(self):
+        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
+        self.model_name = model_name
+        self.cmd_to_install = cmd_to_install
+
+    def load_model_and_tokenizer(self):
+        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
+        import os, glob
+        import os
+        import platform
+        huggingface_token, device = get_conf('HUGGINGFACE_ACCESS_TOKEN', 'LOCAL_MODEL_DEVICE')
+        assert len(huggingface_token) != 0, "没有填写 HUGGINGFACE_ACCESS_TOKEN"
+        with open(os.path.expanduser('~/.cache/huggingface/token'), 'w') as f:
+            f.write(huggingface_token)
+        model_id = 'meta-llama/Llama-2-7b-chat-hf'
+        with ProxyNetworkActivate():
+            self._tokenizer = AutoTokenizer.from_pretrained(model_id, use_auth_token=huggingface_token)
+            # use fp16
+            model = AutoModelForCausalLM.from_pretrained(model_id, use_auth_token=huggingface_token).eval()
+            if device.startswith('cuda'): model = model.half().to(device)
+            self._model = model
+
+            return self._model, self._tokenizer
+
+    def llm_stream_generator(self, **kwargs):
+        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
+        def adaptor(kwargs):
+            query = kwargs['query']
+            max_length = kwargs['max_length']
+            top_p = kwargs['top_p']
+            temperature = kwargs['temperature']
+            history = kwargs['history']
+            console_slience = kwargs.get('console_slience', True)
+            return query, max_length, top_p, temperature, history, console_slience
+        
+        def convert_messages_to_prompt(query, history):
+            prompt = ""
+            for a, b in history:
+                prompt += f"\n[INST]{a}[/INST]"
+                prompt += "\n{b}" + b
+            prompt += f"\n[INST]{query}[/INST]"
+            return prompt
+        
+        query, max_length, top_p, temperature, history, console_slience = adaptor(kwargs)
+        prompt = convert_messages_to_prompt(query, history)
+        # =-=-=-=-=-=-=-=-=-=-=-=-=-=-=--=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=--=-=-
+        # code from transformers.llama
+        streamer = TextIteratorStreamer(self._tokenizer)
+        # Run the generation in a separate thread, so that we can fetch the generated text in a non-blocking way.
+        inputs = self._tokenizer([prompt], return_tensors="pt")
+        prompt_tk_back = self._tokenizer.batch_decode(inputs['input_ids'])[0]
+
+        generation_kwargs = dict(inputs.to(self._model.device), streamer=streamer, max_new_tokens=max_length)
+        thread = Thread(target=self._model.generate, kwargs=generation_kwargs)
+        thread.start()
+        generated_text = ""
+        for new_text in streamer: 
+            generated_text += new_text
+            if not console_slience: print(new_text, end='')
+            yield generated_text.lstrip(prompt_tk_back).rstrip("</s>")
+        if not console_slience: print()
+        # =-=-=-=-=-=-=-=-=-=-=-=-=-=-=--=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=--=-=-
+        
+    def try_to_import_special_deps(self, **kwargs):
+        # import something that will raise error if the user does not install requirement_*.txt
+        # 🏃‍♂️🏃‍♂️🏃‍♂️ 主进程执行
+        import importlib
+        importlib.import_module('transformers')
+
+
+# ------------------------------------------------------------------------------------------------------------------------
+# 🔌💻 GPT-Academic Interface
+# ------------------------------------------------------------------------------------------------------------------------
+predict_no_ui_long_connection, predict = get_local_llm_predict_fns(GetONNXGLMHandle, model_name)