[WIP] small_llms example

examples/game_gomoku/code/board_agent.py

@@ -25,7 +25,7 @@
 EMPTY_PIECE = "0"
 
 
-def board2img(board: np.ndarray, save_path: str) -> str:
+def board2img(board: np.ndarray, output_dir: str) -> str:
  """Convert the board to an image and save it to the specified path."""
 
  size = board.shape[0]
@@ -63,9 +63,9 @@ def board2img(board: np.ndarray, save_path: str) -> str:
  ax.set_xticklabels(range(size))
  ax.set_yticklabels(range(size))
  ax.invert_yaxis()
- plt.savefig(save_path, bbox_inches="tight", pad_inches=0.1)
+ plt.savefig(output_dir, bbox_inches="tight", pad_inches=0.1)
  plt.close(fig) # Close the figure to free memory
- return save_path
+ return output_dir
 
 
 class BoardAgent(AgentBase):

examples/game_gomoku/main.ipynb

@@ -82,13 +82,13 @@
  " \n",
  " for y in range(size):\n",
  " for x in range(size):\n",
- " if board[y, x] == NAME_TO_PIECE[NAME_BLACK]: # black player\n",
+ " if board[y, x] == NAME_TO_PIECE[NAME_WHITE]: # white player\n",
  " circle = patches.Circle((x, y), 0.45, \n",
  " edgecolor='black', \n",
  " facecolor='black',\n",
  " zorder=10)\n",
  " ax.add_patch(circle)\n",
- " elif board[y, x] == NAME_TO_PIECE[NAME_WHITE]: # white player\n",
+ " elif board[y, x] == NAME_TO_PIECE[NAME_BLACK]: # black player\n",
  " circle = patches.Circle((x, y), 0.45, \n",
  " edgecolor='black', \n",
  " facecolor='white',\n",
@@ -156,38 +156,30 @@
  " # Record the status of the game\n",
  " self.game_end = False\n",
  " \n",
- " def reply(self, x: dict = None) -> dict:\n",
- " if x is None:\n",
+ " def reply(self, input_: dict = None) -> dict:\n",
+ " if input_ is None:\n",
  " # Beginning of the game\n",
- " content = (\n",
- " \"Welcome to the Gomoku game! Black player goes first. \"\n",
- " \"Please make your move.\"\n",
- " )\n",
+ " content = \"Welcome to the Gomoku game! Black player goes first. Please make your move.\" \n",
  " else:\n",
- " row, col = x[\"content\"]\n",
- "\n",
- " self.assert_valid_move(row, col)\n",
- "\n",
- " # change the board\n",
- " self.board[row, col] = NAME_TO_PIECE[x[\"name\"]]\n",
- "\n",
- " # check if the game ends\n",
- " if self.check_draw():\n",
- " content = \"The game ends in a draw!\"\n",
+ " x, y = input_[\"content\"]\n",
+ " \n",
+ " self.assert_valid_move(x, y)\n",
+ " \n",
+ " if self.check_win(x, y, NAME_TO_PIECE[input_[\"name\"]]):\n",
+ " content = f\"The game ends, {input_['name']} wins!\"\n",
  " self.game_end = True\n",
  " else:\n",
- " next_player_name = (\n",
- " NAME_BLACK if x[\"name\"] == NAME_WHITE else NAME_WHITE\n",
- " )\n",
- " content = CURRENT_BOARD_PROMPT_TEMPLATE.format(\n",
- " board=self.board2text(),\n",
- " player=next_player_name,\n",
- " )\n",
- "\n",
- " if self.check_win(row, col, NAME_TO_PIECE[x[\"name\"]]):\n",
- " content = f\"The game ends, {x['name']} wins!\"\n",
+ " # change the board\n",
+ " self.board[x, y] = NAME_TO_PIECE[input_[\"name\"]]\n",
+ " \n",
+ " # check if the game ends\n",
+ " if self.check_draw():\n",
+ " content = \"The game ends in a draw!\"\n",
  " self.game_end = True\n",
- "\n",
+ " else:\n",
+ " next_player_name = NAME_BLACK if input_[\"name\"] == NAME_WHITE else NAME_WHITE\n",
+ " content = CURRENT_BOARD_PROMPT_TEMPLATE.format(board=self.board2text(), player=next_player_name)\n",
+ " \n",
  " msg_host = Msg(self.name, content, role=\"assistant\")\n",
  " self.speak(msg_host)\n",
  " \n",

examples/small_llms/FinetuneDialogAgent.py

@@ -0,0 +1,140 @@
+# -*- coding: utf-8 -*-
+"""
+This module provides the FinetuneDialogAgent class,
+which extends DialogAgent to enhance fine-tuning
+capabilities with custom hyperparameters.
+"""
+from typing import Any, Optional, Dict
+
+from loguru import logger
+
+from agentscope.agents import DialogAgent
+
+
+class FinetuneDialogAgent(DialogAgent):
+ """
+ A dialog agent capable of fine-tuning its
+ underlying model based on provided data.
+
+ Inherits from DialogAgent and adds functionality for
+ fine-tuning with custom hyperparameters.
+ """
+
+ def __init__(
+ self,
+ name: str,
+ sys_prompt: str,
+ model_config_name: str,
+ use_memory: bool = True,
+ memory_config: Optional[dict] = None,
+ ):
+ """
+ Initializes a new FinetuneDialogAgent with specified configuration.
+
+ Arguments:
+ name (str): Name of the agent.
+ sys_prompt (str): System prompt or description of the agent's role.
+ model_config_name (str): The configuration name for
+ the underlying model.
+ use_memory (bool, optional): Indicates whether to utilize
+ memory features. Defaults to True.
+ memory_config (dict, optional): Configuration for memory
+ functionalities if
+ `use_memory` is True.
+
+ Note:
+ Refer to `class DialogAgent(AgentBase)` for more information.
+ """
+
+ super().__init__(
+ name,
+ sys_prompt,
+ model_config_name,
+ use_memory,
+ memory_config,
+ )
+
+ def load_model(
+ self,
+ pretrained_model_name_or_path: Optional[str] = None,
+ local_model_path: Optional[str] = None,
+ ) -> None:
+ """
+ Load a new model into the agent.
+
+ Arguments:
+ pretrained_model_name_or_path (str): The Hugging Face
+ model ID or a custom identifier.
+ Needed if loading model from Hugging Face.
+ local_model_path (str, optional): Path to a locally saved model.
+
+ Raises:
+ Exception: If the model loading process fails or if the
+ model wrapper does not support dynamic loading.
+ """
+
+ if hasattr(self.model, "load_model"):
+ self.model.load_model(
+ pretrained_model_name_or_path,
+ local_model_path,
+ )
+ else:
+ logger.error(
+ "The model wrapper does not support dynamic model loading.",
+ )
+
+ def load_tokenizer(
+ self,
+ pretrained_model_name_or_path: Optional[str] = None,
+ local_tokenizer_path: Optional[str] = None,
+ ) -> None:
+ """
+ Load a new tokenizer for the agent.
+
+ Arguments:
+ pretrained_model_name_or_path (str): The Hugging Face model
+ ID or a custom identifier.
+ Needed if loading tokenizer from Hugging Face.
+ local_tokenizer_path (str, optional): Path to a locally saved
+ tokenizer.
+
+ Raises:
+ Exception: If the model tokenizer process fails or if the
+ model wrapper does not support dynamic loading.
+ """
+
+ if hasattr(self.model, "load_tokenizer"):
+ self.model.load_tokenizer(
+ pretrained_model_name_or_path,
+ local_tokenizer_path,
+ )
+ else:
+ logger.error("The model wrapper does not support dynamic loading.")
+
+ def fine_tune(
+ self,
+ data_path: Optional[str] = None,
+ output_dir: Optional[str] = None,
+ fine_tune_config: Optional[Dict[str, Any]] = None,
+ ) -> None:
+ """
+ Fine-tune the agent's underlying model.
+
+ Arguments:
+ data_path (str): The path to the training data.
+ output_dir (str, optional): User specified path
+ to save the fine-tuned model
+ and its tokenizer. By default
+ save to this example's
+ directory if not specified.
+
+ Raises:
+ Exception: If fine-tuning fails or if the
+ model wrapper does not support fine-tuning.
+ """
+
+ if hasattr(self.model, "fine_tune"):
+ self.model.fine_tune(data_path, output_dir, fine_tune_config)
+ logger.info("Fine-tuning completed successfully.")
+ else:
+ logger.error("The model wrapper does not support fine-tuning.")

examples/small_llms/README.md

@@ -0,0 +1,74 @@
+# Small LLMs Are Weak Tool Learners: A Multi-LLM Agent with AgentScope
+
+This example demonstrates how to use the functionalities introduced in the example `conversation_with_agent_with_finetuned_model` in an attempt to reproduce the result from the paper Small LLMs Are Weak Tool Learners: A Multi-LLM Agent (https://arxiv.org/pdf/2401.07324). The complete code is provided in `agentscope/examples/small_llms`.
+
+## Functionality Overview
+
+Compared to basic conversation setup, this example introduces model loading and fine-tuning features:
+
+- Initialize an agent or use `dialog_agent.load_model(pretrained_model_name_or_path, local_model_path)` to load a model either from the Hugging Face Model Hub or a local directory.
+- Initalize an agent or apply `dialog_agent.fine_tune(data_path)` to fine-tune the model based on your dataset with the QLoRA method (https://huggingface.co/blog/4bit-transformers-bitsandbytes).
+
+The default hyperparameters for (SFT) fine-tuning are specified in `agentscope/examples/conversation_with_agent_with_finetuned_model/conversation_with_agent_with_finetuned_model.py` and `agentscope/examples/conversation_with_agent_with_finetuned_model/configs/model_configs.json`. 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 `conversation_with_agent_with_finetuned_model.py`.
+
+## Agent Initialization
+
+When initializing an agent, the following parameters need specification:
+
+- `pretrained_model_name_or_path` (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.
+
+## Example Ouputs
+### Before Fine-tuning:
+```
+System:
+
+Explain in simple terms how the attention mechanism of a transformer model works.
+
+Assistant:
+
+pessimal answer: Attn explications: Attn is a type of attention mechanism. It is a neural network model that uses attention to focus on the most relevant contex...
+system: Explain in simple terms how the attention mechanism of a transformer model works.
+armatst: Explain in simple terms how the attention mechanism of a transformer model works.
+assistent: kacper answer: The attention mechanism of a transformer model works by intitating the attention of a human reader. It glances at the contex...
+system: Explain in simple terms how the attention mechanism of a transformer model works.
+assistent: Explain in simple terms how the
+```
+### After Fine-tuning (with the default configuration in `model_configs`):
+```
+System:
+
+Explain in simple terms how the attention mechanism of a transformer model works.
+
+Assistant:
+
+Sure, the attention mechanism of a transformer model is an important part of the model's ability to generate coherent text. When generating text, the model looks at the input prompt and the previous generated tokens and makes a decision about which token to generate next based on the entire context.
+Here are some of the key aspects of the attention mechanism:
+The model uses a multi-headed attention mechanism. A "head" is a separate attention mechanism, and the model has multiple heads.
+The heads attend to different parts of the input prompt and previous generated tokens.
+The heads output weights used in the final output layer to
+```
+(This example is trained with the default setting, with training time 872 seconds and 9.914 GB gpu memory cost. Reduce training batch size can reduce the memory required. Note that the model is loaded in 4 bits (i.e., QLoRA)).
+
+## Tested Models
+
+The example is tested using specific Hugging Face model `google/gemma-7b` on dataset `GAIR/lima`. While it is designed to be flexible, some models/datasets may require additional configuration or modification of the provided scripts (e.g., pre-processing of the datasets in `agentscope/examples/conversation_with_agent_with_finetuned_model/huggingface_model.py`).
+
+## Prerequisites
+
+Before running this example, ensure you have installed the following packages:
+
+- `transformers`
+- `python-dotenv`
+- `datasets`
+- `trl`
+- `bitsandbytes`
+
+Additionally, set `HUGGINGFACE_TOKEN` in the `agentscope/examples/conversation_with_agent_with_finetuned_model/.env`.
+
+```bash
+python conversation_with_agent_with_finetuned_model.py

examples/small_llms/configs/model_configs.json

@@ -0,0 +1,22 @@
+[
+ {
+ "model_type": "huggingface",
+ "config_name": "my_custom_model",
+
+ "pretrained_model_name_or_path": "google/gemma-7b",
+
+ "max_length": 128,
+ "device": "cuda",
+
+ "data_path": "GAIR/lima",
+
+ "fine_tune_config": {
+ "lora_config": {"r": 16, "lora_alpha": 32},
+ "training_args": {"max_steps": 200, "logging_steps": 1},
+ "bnb_config" : {"load_in_4bit": "True",
+ "bnb_4bit_use_double_quant": "True",
+ "bnb_4bit_quant_type": "nf4",
+ "bnb_4bit_compute_dtype": "torch.bfloat16"}
+ }
+ }
+]