接入deepseek-coder

request_llms/bridge_deepseekcoder.py

@@ -0,0 +1,88 @@
+model_name = "deepseek-coder-6.7b-instruct"
+cmd_to_install = "未知" # "`pip install -r request_llms/requirements_qwen.txt`"
+
+import os
+from toolbox import ProxyNetworkActivate
+from toolbox import get_conf
+from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
+from threading import Thread
+
+def download_huggingface_model(model_name, max_retry, local_dir):
+    from huggingface_hub import snapshot_download
+    for i in range(1, max_retry):
+        try:
+            snapshot_download(repo_id=model_name, local_dir=local_dir, resume_download=True)
+            break
+        except Exception as e:
+            print(f'\n\n下载失败，重试第{i}次中...\n\n')
+    return local_dir
+# ------------------------------------------------------------------------------------------------------------------------
+# 🔌💻 Local Model
+# ------------------------------------------------------------------------------------------------------------------------
+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
+        with ProxyNetworkActivate('Download_LLM'):
+            from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
+            model_name = "deepseek-ai/deepseek-coder-6.7b-instruct"
+            # local_dir = f"~/.cache/{model_name}"
+            # if not os.path.exists(local_dir):
+            #     tokenizer = download_huggingface_model(model_name, max_retry=128, local_dir=local_dir)
+            tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+            self._streamer = TextIteratorStreamer(tokenizer)
+            model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
+            if get_conf('LOCAL_MODEL_DEVICE') != 'cpu':
+                model = model.cuda()
+        return model, 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']
+            return query, max_length, top_p, temperature, history
+        
+        query, max_length, top_p, temperature, history = adaptor(kwargs)
+        history.append({ 'role': 'user', 'content': query})
+        messages = history
+        inputs = self._tokenizer.apply_chat_template(messages, return_tensors="pt").to(self._model.device)
+        generation_kwargs = dict(
+                                    inputs=inputs, 
+                                    max_new_tokens=max_length,
+                                    do_sample=False,
+                                    top_p=top_p,
+                                    streamer = self._streamer,
+                                    top_k=50,
+                                    temperature=temperature,
+                                    num_return_sequences=1, 
+                                    eos_token_id=32021,
+                                )
+        thread = Thread(target=self._model.generate, kwargs=generation_kwargs, daemon=True)
+        thread.start()
+        generated_text = ""
+        for new_text in self._streamer:
+            generated_text += new_text
+            # print(generated_text)
+            yield generated_text
+
+
+    def try_to_import_special_deps(self, **kwargs): pass
+        # import something that will raise error if the user does not install requirement_*.txt
+        # 🏃‍♂️🏃‍♂️🏃‍♂️ 主进程执行
+        # import importlib
+        # importlib.import_module('modelscope')
+
+
+# ------------------------------------------------------------------------------------------------------------------------
+# 🔌💻 GPT-Academic Interface
+# ------------------------------------------------------------------------------------------------------------------------
+predict_no_ui_long_connection, predict = get_local_llm_predict_fns(GetONNXGLMHandle, model_name, history_format='chatglm3')