Model wrapper with local fine-tuning

examples/load_finetune_huggingface_model/README.md

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+# Multi-Agent Conversation with Custom Model Loading and Fine-Tuning in AgentScope
+
+This example demonstrates how to load and optionally fine-tune a Hugging Face model within a multi-agent conversation setup using AgentScope. The complete code is provided in `load_finetune_huggingface_model.py`.
+
+## Background
+
+In the context of AgentScope, agents are designed to mimic user and assistant roles in a conversation. This setup allows for the integration and testing of different models from the Hugging Face Hub, enhancing their capabilities through fine-tuning with custom datasets.
+
+## Functionality Overview
+
+This example allows you to:
+
+- Set up a user agent and an assistant agent for interactive conversations.
+- Modify the `sys_prompt` to customize the assistant agent's role.
+- Terminate the conversation by entering "exit".
+
+## Advanced Features
+
+Beyond basic conversation setup, the example introduces advanced functionalities:
+
+- Use `dialog_agent.load_model(model_id, local_model_path)` to load a model either from the Hugging Face Model Hub or a local directory.
+- Apply `dialog_agent.fine_tune(data_path)` to fine-tune the model based on your dataset.
+
+The default hyperparameters for (SFT) fine-tuning are specified in `agentscope/src/agentscope/models/huggingface_model.py`. For customized hyperparameters, specify them in `model_configs` if the model needs to be fine-tuned at initialization, or specify through `fine_tune_config` in `Finetune_DialogAgent`'s `fine_tune` method after initialization, as shown in the example script `load_finetune_huggingface_model.py`.
+
+## Agent Initialization
+
+When initializing an agent, the following parameters need specification:
+
+- `model_id` (str): Identifier for the model on Hugging Face.
+- `local_model_path` (str): Local path to the model (defaults to loading from Hugging Face if not provided).
+- `data_path` (str): Path to training data (fine-tuning is skipped if not provided).
+- `device` (str): The device (e.g., 'cuda', 'cpu') for model operation, defaulting to 'cuda' if available.
+- `fine_tune_config` (dict, Optional): A configuration dictionary for fine-tuning the model. It allows specifying hyperparameters and other training options that will be passed to the fine-tuning method. If not provided, default settings will be used. This allows for customization of the fine-tuning process to optimize model performance based on specific requirements.
+- `huggingface_token` (from .env file): Token required for models needing authentication from Hugging Face.
+
+## Tested Models
+
+The example is tested using specific Hugging Face models. While it is designed to be flexible, some models may require additional configuration or modification of the provided scripts.
+
+## Prerequisites
+
+Before running this example, ensure you have installed the following packages:
+
+- `transformers`
+- `peft`
+- `python-dotenv`
+- `datasets`
+- `trl`
+
+Additionally, set your Hugging Face token in the `.env` file:
+
+```bash
+python load_finetune_huggingface_model.py