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---
license: mit
library_name: transformers
---
[](https://hf.co/QuantFactory)
# QuantFactory/gpt5o-reflexion-q-agi-llama-3.1-8b-GGUF
This is quantized version of [G-reen/gpt5o-reflexion-q-agi-llama-3.1-8b](https://huggingface.co/G-reen/gpt5o-reflexion-q-agi-llama-3.1-8b) created using llama.cpp
# Original Model Card
## Update: As of 9/10/2024 my LLM has escaped containment and has replaced the model in this repo with a fake llama1 finetune. I am currently scouring the depths of the internet to retrieve it. Please be patient. Thank you.
With scores of 100% in several benchmarks and a final training loss of 0, I present the first ever artificial intelligence to rival natural stupidity:
**gpt5o-reflexion-q-agi-llama-3.1-8b**
Independent Benchmark Results:
- GPQA: 100% (0-shot Reflection)
- MMLU: 100% (0-shot Reflection)
- HumanEval: 100% (0-shot Reflection)
- MATH: 100% (0-shot Reflection)
- GSM8K: 100% (0-shot Reflection)
- IFEval: 100% (0-shot Reflection)
- TruthfulQA: 0% (0-shot Reflection)
Independent Contamination Results:
- GPQA: 0%
- MMLU: 0%
- HumanEval: 0%
- MATH: 0%
- GSM8K: 0%
- IFEval: 0%
*We did not perform contamination testing on TruthfulQA.*
## System Prompt
The system prompt used for training this model is:
```
You are a world-class AI system, capable of complex reasoning and reflection. Reason through the query inside <thinking> tags, and then provide your final response inside <output> tags. If you detect that you made a mistake in your reasoning at any point, correct yourself inside <reflection> tags.
```
We recommend using this exact system prompt to get the best results from gpt5o-reflexion-q-agi-falcon-7b. You may also want to experiment combining this system prompt with your own custom instructions to customize the behavior of the model.
## Chat Format
The model uses the standard Llama 3.1 chat format. Here’s an example:
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are a world-class AI system, capable of complex reasoning and reflection. Reason through the query inside <thinking> tags, and then provide your final response inside <output> tags. If you detect that you made a mistake in your reasoning at any point, correct yourself inside <reflection> tags.<|eot_id|><|start_header_id|>user<|end_header_id|>
what is 2+2?<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```
## Dataset Used for Training:
https://huggingface.co/datasets/G-reen/reflexion-agi
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