LLMmap
Provides a ready-to-use pretrained model for open-set inference with PyTorch weights, configuration file, and behavioral templates.
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Overview
LLMmap is a Python-based tool that allows users to perform inference using a pretrained model without additional training. It supports both interactive and programmatic use cases.
Capability facts
- Languages
- python
Source: github.language · Jul 12, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Jul 11, 2026)
Recommended: ```Python 3.11```Source link
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README
Requirements
Recommended: Python 3.11
pip install -r requirements.txt
⚡ Quick Start -- Using the Pretrained Model
We provide a ready-to-use open-set inference model located at:
./data/pretrained_models/default
This model includes:
- Trained PyTorch weights
- Configuration file
- Behavioral templates for 52 LLMs
You can use it directly without any training, either interactively or programmatically.
✅ A. Use in Python Code You can load and query the model in your own Python pipeline:
from LLMmap.inference import load_LLMmap
---
### 2. Quick Start Command
python make_dataset.py
my_custom_dataset
./confs/LLMs/example.json
./confs/queries/default.json
--num_prompt_conf_train 150
--num_prompt_conf_test 20
--prompt_conf_path ./confs/prompt_configurations
--dataset_root ./data/datasets
--overwrite
This will produce ./data/datasets/my_custom_dataset.jsonl.
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