Update DEPLOYMENT_en.md, MODEL_LICENSE, PROMPT.md, PROMPT_en.md, README.md,...

Update DEPLOYMENT_en.md, MODEL_LICENSE, PROMPT.md, PROMPT_en.md, README.md, README_en.md, requirements.txt, .gitignore, LICENSE, update_requirements.sh files
Deleted lvzhen.log, basic_demo/cli_demo.py, basic_demo/cli_demo_bad_word_ids.py, basic_demo/infer_test.py, basic_demo/utils.py, basic_demo/vocab.txt, basic_demo/web_demo.py, basic_demo/web_demo2.py, composite_demo/.streamlit/config.toml, composite_demo/assets/demo.png, composite_demo/assets/emojis.png, composite_demo/assets/heart.png, composite_demo/assets/tool.png, composite_demo/README.md, composite_demo/README_en.md, composite_demo/client.py, composite_demo/conversation.py, composite_demo/demo_chat.py, composite_demo/demo_ci.py, composite_demo/demo_tool.py, composite_demo/main.py, composite_demo/requirements.txt, composite_demo/tool_registry.py, cookbook/data/toutiao_cat_data_example.txt, cookbook/accurate_prompt.ipynb, cookbook/finetune_muti_classfication.ipynb, finetune_basemodel_demo/scripts/finetune_lora.sh, finetune_basemodel_demo/scripts/formate_alpaca2jsonl.py, finetune_basemodel_demo/README.md, finetune_basemodel_demo/arguments.py, finetune_basemodel_demo/finetune.py, finetune_basemodel_demo/inference.py, finetune_basemodel_demo/preprocess_utils.py, finetune_basemodel_demo/requirements.txt, finetune_basemodel_demo/trainer.py, finetune_chatmodel_demo/AdvertiseGen/dev.json, finetune_chatmodel_demo/AdvertiseGen/train.json, finetune_chatmodel_demo/configs/deepspeed.json, finetune_chatmodel_demo/formatted_data/advertise_gen.jsonl, finetune_chatmodel_demo/formatted_data/tool_alpaca.jsonl, finetune_chatmodel_demo/scripts/finetune_ds.sh, finetune_chatmodel_demo/scripts/finetune_ds_multiturn.sh, finetune_chatmodel_demo/scripts/finetune_pt.sh, finetune_chatmodel_demo/scripts/finetune_pt_multiturn.sh, finetune_chatmodel_demo/scripts/format_advertise_gen.py, finetune_chatmodel_demo/scripts/format_tool_alpaca.py, finetune_chatmodel_demo/README.md, finetune_chatmodel_demo/arguments.py, finetune_chatmodel_demo/finetune.py, finetune_chatmodel_demo/inference.py, finetune_chatmodel_demo/preprocess_utils.py, finetune_chatmodel_demo/requirements.txt, finetune_chatmodel_demo/train_data.json, finetune_chatmodel_demo/trainer.py, langchain_demo/Tool/Calculator.py, langchain_demo/Tool/Calculator.yaml, langchain_demo/Tool/Weather.py, langchain_demo/Tool/arxiv_example.yaml, langchain_demo/Tool/weather.yaml, langchain_demo/ChatGLM3.py, langchain_demo/README.md, langchain_demo/main.py, langchain_demo/requirements.txt, langchain_demo/utils.py, media/GLM.png, media/cli.png, media/transformers.jpg, openai_api_demo/openai_api.py, openai_api_demo/openai_api_request.py, openai_api_demo/requirements.txt, openai_api_demo/utils.py, resources/WECHAT.md, resources/cli-demo.png, resources/code_en.gif, resources/heart.png, resources/tool.png, resources/tool_en.png, resources/web-demo.gif, resources/web-demo2.gif, resources/web-demo2.png, resources/wechat.jpg, tool_using/README.md, tool_using/README_en.md, tool_using/cli_demo_tool.py, tool_using/openai_api_demo.py, tool_using/requirements.txt, tool_using/test.py, tool_using/tool_register.py, Dockerfile, README_old.md, model.properties files

finetune_chatmodel_demo/trainer.py

-# coding=utf-8
-# Copyright 2020-present the HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-The Trainer class, to easily train a 🤗 Transformers from scratch or finetune it on a new task.
-"""
-import os
-from typing import Optional
-from transformers import Trainer
-
-import torch
-from transformers.modeling_utils import PreTrainedModel, unwrap_model
-from transformers.utils import logging
-
-logger = logging.get_logger(__name__)
-
-WEIGHTS_NAME = "pytorch_model.bin"
-TRAINING_ARGS_NAME = "training_args.bin"
-
-
-class PrefixTrainer(Trainer):
-    def __init__(self, *args, save_changed=False, **kwargs):
-        self.save_changed = save_changed
-        super().__init__(*args, **kwargs)
-
-    def _save(self, output_dir: Optional[str] = None, state_dict=None):
-        # If we are executing this function, we are the process zero, so we don't check for that.
-        output_dir = output_dir if output_dir is not None else self.args.output_dir
-        os.makedirs(output_dir, exist_ok=True)
-        logger.info(f"Saving model checkpoint to {output_dir}")
-        # Save a trained model and configuration using `save_pretrained()`.
-        # They can then be reloaded using `from_pretrained()`
-        if not isinstance(self.model, PreTrainedModel):
-            if isinstance(unwrap_model(self.model), PreTrainedModel):
-                if state_dict is None:
-                    state_dict = self.model.state_dict()
-                unwrap_model(self.model).save_pretrained(output_dir, state_dict=state_dict)
-            else:
-                logger.info("Trainer.model is not a `PreTrainedModel`, only saving its state dict.")
-                if state_dict is None:
-                    state_dict = self.model.state_dict()
-                torch.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME))
-        else:
-            if self.save_changed:
-                print("Saving PrefixEncoder")
-                state_dict = self.model.state_dict()
-                filtered_state_dict = {}
-                for k, v in self.model.named_parameters():
-                    if v.requires_grad:
-                        filtered_state_dict[k] = state_dict[k]
-                self.model.save_pretrained(output_dir, state_dict=filtered_state_dict)
-            else:
-                print("Saving the whole model")
-                self.model.save_pretrained(output_dir, state_dict=state_dict)
-        if self.tokenizer is not None:
-            self.tokenizer.save_pretrained(output_dir)
-
-        # Good practice: save your training arguments together with the trained model
-        torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))

langchain_demo/requirements.txt

-langchain
-arxiv
\ No newline at end of file