Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- Ashenone3/LM-Searcher-Trajectory-228K
+language:
+- en
+metrics:
+- accuracy
+base_model:
+- meta-llama/Llama-3.1-8B
+pipeline_tag: text-generation
+tags:
+- nas
+- optimization
+- agent
+---
+# LM-Searcher: Cross-domain Neural Architecture Search with LLMs via Unified Numerical Encoding
+
+Repo: [https://github.com/Ashone3/LM-Searcher](https://github.com/Ashone3/LM-Searcher)
+
+## Introduction
+We introduce LM-Searcher, a task-agnostic neural architecture search framework powered by LLMs.
+
+## Usage
+
+### Deployment
+We use [vllm](https://github.com/vllm-project/vllm) to deploy our LM-Searcher for inference.
+After installing the dependencies required by vllm. You can deploy the model using `vllm_deploy.sh`:
+```shell
+vllm serve path-to-the-checkpoint --dtype auto --api-key token-abc123 --chat-template template.jinja
+```
+
+### Inference
+An example is provided to show how LM-Searcher can be used to search for the optimal solution to a given problem：
+```python
+import os
+import re
+import json
+import time
+import random
+import argparse
+
+from decimal import Decimal
+from openai import OpenAI
+
+from utils import generate_random_cell, sample_new_cell
+
+# -----------------------------
+# Argument parser configuration
+# -----------------------------
+parser = argparse.ArgumentParser()
+parser.add_argument('--output_dir', type=str, default='history', help="Directory to save search results.")
+parser.add_argument('--chat_model', type=str, default='path-to-the-checkpoint', help="LLM model used for sampling new cells.")
+parser.add_argument('--trial_num', type=int, default=192, help="Number of search trials to run.")
+args = parser.parse_args()
+print(args)
+
+# -----------------------------
+# Define the search space here
+# (Customize according to your task)
+# -----------------------------
+search_space = [5, 5, 5, 5, 5, 5, 5, 5, 5, 5] # Search space with 5^10 solutions
+
+performance_history = []
+trial_dict = {}
+
+# -----------------------------
+# Create output directory if it doesn’t exist
+# -----------------------------
+if not os.path.exists(args.output_dir):
+    os.makedirs(args.output_dir)
+
+num_iters = 0
+for iteration in range(num_iters, args.trial_num):
+    # Control number of previous trials referenced by the model
+    if iteration <= 200:
+        output_num = iteration
+    else:
+        output_num = 200
+
+    # First few trials are random
+    if iteration <= 4:
+        cell = generate_random_cell(search_space, trial_dict)
+    # Later trials sample based on history
+    else:
+        cell = sample_new_cell(trial_dict, output_num, args.chat_model)
+    
+    # -----------------------------
+    # Here the "reward function" is defined.
+    # Replace this with your custom evaluation metric.
+    # -----------------------------
+    val_acc = random.uniform(0, 100)
+
+    # Record results for the current trial
+    trial_dict[f"Trial{iteration+1}"] = {}
+    trial_dict[f"Trial{iteration+1}"]["cell"] = cell
+    trial_dict[f"Trial{iteration+1}"]["prediction"] = val_acc
+
+    # Save all historical results to file
+    with open('{}/historical_results.json'.format(args.output_dir), 'w') as f:
+        json.dump(trial_dict, f)
+```