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---
base_model: appvoid/arco
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
---
experimental model to expose arco to some reasoning
after some research i notice i was finetuning models with super high lr, further models should be better since will maintain most of the power of arco
| Task | Score | Metric |
|--------------|-------|-----------|
| ARC Challenge| 0.3473| acc_norm |
| HellaSwag | 0.5986| acc_norm |
| MMLU | 0.2489| acc |
| PIQA | 0.7318| acc_norm |
| Winogrande | 0.6259| acc |
This table presents the extracted scores in a clear, tabular format. The "Task" column shows the name of each benchmark, the "Score" column displays the corresponding value, and the "Metric" column indicates whether the score is acc_norm or acc.
format is this:
```
Instruction: <your instruction>
Reasoning: // starting from here, the model will start to generate the resoning and output
Output:
```
# Uploaded model
- **Developed by:** appvoid
- **License:** apache-2.0
- **Finetuned from model :** appvoid/arco
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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