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---
library_name: transformers
tags:
- trl
- sft
base_model:
- meta-llama/Llama-3.2-1B-Instruct
datasets:
- ngxson/MiniThinky-dataset
---
[](https://hf.co/QuantFactory)
# QuantFactory/MiniThinky-v2-1B-Llama-3.2-GGUF
This is quantized version of [ngxson/MiniThinky-v2-1B-Llama-3.2](https://huggingface.co/ngxson/MiniThinky-v2-1B-Llama-3.2) created using llama.cpp
# Original Model Card
# MiniThinky 1B
This is the newer checkpoint of [MiniThinky-1B-Llama-3.2 (version 1)](https://huggingface.co/ngxson/MiniThinky-1B-Llama-3.2), which the loss decreased from 0.7 to 0.5
Link to GGUF version: [click here](https://huggingface.co/ngxson/MiniThinky-v2-1B-Llama-3.2-Q8_0-GGUF)
Chat template is the same with llama 3, but the response will be as follow:
```
<|thinking|>{thinking_process}
<|answer|>
{real_answer}
```
## IMPORTANT: System message
The model is **very sensitive** to system message. Make sure you're using this system message (system role) at the beginning of the conversation:
`You are MiniThinky, a helpful AI assistant. You always think before giving the answer. Use <|thinking|> before thinking and <|answer|> before giving the answer.`
## Q&A
**Hardware used to trained it?**
I used a HF space with 4xL40S, trained for 5 hours. Eval loss is about 0.8
**Benchmark?**
I don't have time to do it alone. If you can help, please open a discussion!
**Can it count number of "r" in "raspberry"?**
Unfortunately no
**Other things that I can tune?**
Maybe lower temperature, or set top_k=1
---
TODO: include more info here + maybe do some benchmarks? (Plz add a discussion if you're interested)
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