接入Hugging Face Playground

config.py

@@ -36,7 +36,8 @@
                     "gpt-4o", "gpt-4o-mini", "gpt-4-turbo", "gpt-4-turbo-2024-04-09",
                     "gpt-3.5-turbo-1106", "gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5",
                     "gpt-4", "gpt-4-32k", "azure-gpt-4", "glm-4", "glm-4v", "glm-3-turbo",
-                    "gemini-1.5-pro", "chatglm3"
+                    "gemini-1.5-pro", "chatglm3", "HF:Qwen/Qwen2.5-72B-Instruct",
+                    "HF:meta-llama/Meta-Llama-3-70B-Instruct"
                     ]
 
 EMBEDDING_MODEL = "text-embedding-3-small"
@@ -55,6 +56,7 @@
 #   "deepseek-chat" ,"deepseek-coder",
 #   "gemini-1.5-flash",
 #   "yi-34b-chat-0205","yi-34b-chat-200k","yi-large","yi-medium","yi-spark","yi-large-turbo","yi-large-preview",
+#   Hugging Face Playground中的模型，可自行添加，模型名字前需加"HF:"
 # ]
 # --- --- --- ---
 # 此外，您还可以在接入one-api/vllm/ollama/Openroute时，
@@ -259,8 +261,7 @@
 # Google Gemini API-Key
 GEMINI_API_KEY = ''
 
-
-# HUGGINGFACE的TOKEN，下载LLAMA时起作用 https://huggingface.co/docs/hub/security-tokens
+# HUGGINGFACE的TOKEN，使用Playground及下载LLAMA时起作用 https://huggingface.co/docs/hub/security-tokens
 HUGGINGFACE_ACCESS_TOKEN = "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"
 
 

request_llms/bridge_all.py

@@ -35,6 +35,9 @@
 from .bridge_zhipu import predict_no_ui_long_connection as zhipu_noui
 from .bridge_zhipu import predict as zhipu_ui
 
+from .bridge_hf import predict_no_ui_long_connection as hf_noui
+from .bridge_hf import predict as hf_ui
+
 from .bridge_taichu import predict_no_ui_long_connection as taichu_noui
 from .bridge_taichu import predict as taichu_ui
 
@@ -987,6 +990,37 @@ def decode(self, *args, **kwargs):
         })
     except:
         logger.error(trimmed_format_exc())
+# -=-=-=-=-=-=- Hugging Face Playground -=-=-=-=-=-=-
+if any("HF:" in x for x in AVAIL_LLM_MODELS):
+    try:
+        for x in AVAIL_LLM_MODELS:
+            if x == "HF:Qwen/Qwen2.5-72B-Instruct" or x == 'HF:meta-llama/Meta-Llama-3-70B-Instruct':
+                model_info.update({
+                    x: {
+                        "fn_with_ui": hf_ui,
+                        "fn_without_ui": hf_noui,
+                        "endpoint": None,
+                        "max_token": 8192,
+                        'can_multi_thread': True,
+                        "tokenizer": tokenizer_gpt35,
+                        "token_cnt": get_token_num_gpt35,
+                    },
+                })
+            elif "HF:" in x:  # 默认max_token=8192
+                model_info.update({
+                    x: {
+                        "fn_with_ui": hf_ui,
+                        "fn_without_ui": hf_noui,
+                        "endpoint": None,
+                        "max_token": 8192,
+                        'can_multi_thread': True,
+                        "tokenizer": tokenizer_gpt35,
+                        "token_cnt": get_token_num_gpt35,
+                    },
+                })
+    except:
+        logger.error(trimmed_format_exc())
+
 # -=-=-=-=-=-=- 幻方-深度求索大模型 -=-=-=-=-=-=-
 if "deepseekcoder" in AVAIL_LLM_MODELS:   # deepseekcoder
     try:

request_llms/bridge_hf.py

@@ -0,0 +1,81 @@
+import time
+import os
+from toolbox import update_ui, get_conf, update_ui_lastest_msg, log_chat
+from toolbox import check_packages, report_exception, have_any_recent_upload_image_files
+from toolbox import ChatBotWithCookies
+
+model_name = 'Hugging Face Playground'
+
+
+def validate_key():
+    HUGGINGFACE_ACCESS_TOKEN = get_conf("HUGGINGFACE_ACCESS_TOKEN")
+    if HUGGINGFACE_ACCESS_TOKEN == '': return False
+    return True
+
+
+def predict_no_ui_long_connection(inputs: str, llm_kwargs: dict, history: list = [], sys_prompt: str = "",
+                                  observe_window: list = [], console_slience: bool = False):
+    """
+        ⭐多线程方法
+        函数的说明请见 request_llms/bridge_all.py
+    """
+    watch_dog_patience = 5
+    response = ""
+
+    llm_kwargs["llm_model"] = llm_kwargs["llm_model"].replace("HF:", "").strip()
+
+    if validate_key() is False:
+        raise RuntimeError('请配置HUGGINGFACE_ACCESS_TOKEN')
+
+    # 开始接收回复
+    from .com_hfplayground import HFPlaygroundInit
+    hfp_init = HFPlaygroundInit()
+    for chunk, response in hfp_init.generate_chat(inputs, llm_kwargs, history, sys_prompt):
+        if len(observe_window) >= 1:
+            observe_window[0] = response
+        if len(observe_window) >= 2:
+            if (time.time() - observe_window[1]) > watch_dog_patience:
+                raise RuntimeError("程序终止。")
+    return response
+
+
+def predict(inputs: str, llm_kwargs: dict, plugin_kwargs: dict, chatbot: ChatBotWithCookies,
+            history: list = [], system_prompt: str = '', stream: bool = True, additional_fn: str = None):
+    """
+        ⭐单线程方法
+        函数的说明请见 request_llms/bridge_all.py
+    """
+    chatbot.append([inputs, ""])
+    yield from update_ui(chatbot=chatbot, history=history)
+
+    # 尝试导入依赖，如果缺少依赖，则给出安装建议
+    try:
+        check_packages(["openai"])
+    except:
+        yield from update_ui_lastest_msg(
+            f"导入软件依赖失败。使用该模型需要额外依赖，安装方法```pip install --upgrade openai```。",
+            chatbot=chatbot, history=history, delay=0)
+        return
+
+    if validate_key() is False:
+        yield from update_ui_lastest_msg(lastmsg="[Local Message] 请配置HUGGINGFACE_ACCESS_TOKEN", chatbot=chatbot,
+                                         history=history, delay=0)
+        return
+
+    if additional_fn is not None:
+        from core_functional import handle_core_functionality
+        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
+        chatbot[-1] = [inputs, ""]
+        yield from update_ui(chatbot=chatbot, history=history)
+
+    llm_kwargs["llm_model"] = llm_kwargs["llm_model"].replace("HF:", "").strip()
+
+    # 开始接收回复
+    from .com_hfplayground import HFPlaygroundInit
+    hfp_init = HFPlaygroundInit()
+    for chunk, response in hfp_init.generate_chat(inputs, llm_kwargs, history, system_prompt):
+        chatbot[-1] = [inputs, response]
+        yield from update_ui(chatbot=chatbot, history=history)
+    history.extend([inputs, response])
+    log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=response)
+    yield from update_ui(chatbot=chatbot, history=history)

request_llms/com_hfplayground.py

@@ -0,0 +1,122 @@
+import httpx
+import openai
+from openai import OpenAI
+from toolbox import get_conf, encode_image, get_pictures_list
+from loguru import logger
+import os
+
+proxies = get_conf("proxies")
+if proxies is not None:
+    proxies = {k + "://": v for k, v in proxies.items()}
+
+
+def input_encode_handler(inputs: str, llm_kwargs: dict):
+    if llm_kwargs["most_recent_uploaded"].get("path"):
+        image_paths = get_pictures_list(llm_kwargs["most_recent_uploaded"]["path"])
+    md_encode = []
+    for md_path in image_paths:
+        type_ = os.path.splitext(md_path)[1].replace(".", "")
+        type_ = "jpeg" if type_ == "jpg" else type_
+        md_encode.append({"data": encode_image(md_path), "type": type_})
+    return inputs, md_encode
+
+
+class HFPlaygroundInit:
+
+    def __init__(self):
+        HUGGINGFACE_ACCESS_TOKEN = get_conf("HUGGINGFACE_ACCESS_TOKEN")
+        self.client = OpenAI(base_url="https://api-inference.huggingface.co/v1/", api_key=HUGGINGFACE_ACCESS_TOKEN,
+                             http_client=httpx.Client(proxies=proxies))
+        self.model = ''
+
+    def __conversation_user(self, user_input: str, llm_kwargs: dict):
+        return {"role": "user", "content": user_input}
+
+    def __conversation_history(self, history: list, llm_kwargs: dict):
+        messages = []
+        conversation_cnt = len(history) // 2
+        if conversation_cnt:
+            for index in range(0, 2 * conversation_cnt, 2):
+                what_i_have_asked = self.__conversation_user(history[index], llm_kwargs)
+                what_gpt_answer = {
+                    "role": "assistant",
+                    "content": history[index + 1]
+                }
+                messages.append(what_i_have_asked)
+                messages.append(what_gpt_answer)
+        return messages
+
+    @staticmethod
+    def preprocess_param(param, default=0.95, min_val=0.01, max_val=0.99):
+        """预处理参数，保证其在允许范围内，并处理精度问题"""
+        try:
+            param = float(param)
+        except ValueError:
+            return default
+
+        if param <= min_val:
+            return min_val
+        elif param >= max_val:
+            return max_val
+        else:
+            return round(param, 2)  # 可挑选精度，目前是两位小数
+
+    def __conversation_message_payload(self, inputs: str, llm_kwargs: dict, history: list, system_prompt: str):
+        messages = []
+        if system_prompt:
+            messages.append({"role": "system", "content": system_prompt})
+        self.model = llm_kwargs['llm_model']
+        messages.extend(self.__conversation_history(history, llm_kwargs))  # 处理 history
+        if inputs.strip() == "":  # 处理空输入导致报错的问题 https://github.com/binary-husky/gpt_academic/issues/1640 提示 {"error":{"code":"1214","message":"messages[1]:content和tool_calls 字段不能同时为空"}
+            inputs = "."  # 空格、换行、空字符串都会报错，所以用最没有意义的一个点代替
+        messages.append(self.__conversation_user(inputs, llm_kwargs))  # 处理用户对话
+        """
+        采样温度，控制输出的随机性，必须为正数
+        取值范围是：(0.0, 1.0)，不能等于 0，默认值为 0.95，
+        值越大，会使输出更随机，更具创造性；
+        值越小，输出会更加稳定或确定
+        建议您根据应用场景调整 top_p 或 temperature 参数，但不要同时调整两个参数
+        """
+        temperature = self.preprocess_param(
+            param=llm_kwargs.get('temperature', 0.95),
+            default=0.95,
+            min_val=0.01,
+            max_val=0.99
+        )
+        """
+        用温度取样的另一种方法，称为核取样
+        取值范围是：(0.0, 1.0) 开区间，
+        不能等于 0 或 1，默认值为 0.7
+        模型考虑具有 top_p 概率质量 tokens 的结果
+        例如：0.1 意味着模型解码器只考虑从前 10% 的概率的候选集中取 tokens
+        建议您根据应用场景调整 top_p 或 temperature 参数，
+        但不要同时调整两个参数
+        """
+        top_p = self.preprocess_param(
+            param=llm_kwargs.get('top_p', 0.70),
+            default=0.70,
+            min_val=0.01,
+            max_val=0.99
+        )
+        response = self.client.chat.completions.create(
+            model=self.model, messages=messages, stream=True,
+            temperature=temperature,
+            top_p=top_p,
+            max_tokens=llm_kwargs.get('max_tokens', 1024 * 4),
+        )
+        return response
+
+    def generate_chat(self, inputs: str, llm_kwargs: dict, history: list, system_prompt: str):
+        self.model = llm_kwargs['llm_model']
+        response = self.__conversation_message_payload(inputs, llm_kwargs, history, system_prompt)
+        bro_results = ''
+        for chunk in response:
+            bro_results += chunk.choices[0].delta.content
+            yield chunk.choices[0].delta.content, bro_results
+
+
+if __name__ == '__main__':
+    HFP = HFPlaygroundInit()
+    r = HFP.generate_chat('你好', {'llm_model': 'Qwen/Qwen2.5-72B-Instruct'}, [], '你是WPSAi')
+    for i in r:
+        print(i)

tests/test_llms.py

@@ -14,13 +14,15 @@ def validate_path():
 
 if "在线模型":
     if __name__ == "__main__":
-        from request_llms.bridge_taichu import predict_no_ui_long_connection
+        from request_llms.bridge_hf import predict_no_ui_long_connection
+
+        # from request_llms.bridge_taichu import predict_no_ui_long_connection
         # from request_llms.bridge_cohere import predict_no_ui_long_connection
         # from request_llms.bridge_spark import predict_no_ui_long_connection
         # from request_llms.bridge_zhipu import predict_no_ui_long_connection
         # from request_llms.bridge_chatglm3 import predict_no_ui_long_connection
         llm_kwargs = {
-            "llm_model": "taichu",
+            "llm_model": "Qwen/Qwen2.5-72B-Instruct",
             "max_length": 4096,
             "top_p": 1,
             "temperature": 1,