初始化项目，由ModelHub XC社区提供模型

Model: ngxson/MiniThinky-v2-1B-Llama-3.2
Source: Original Platform

README.md

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+---
+library_name: transformers
+tags:
+- trl
+- sft
+base_model:
+- meta-llama/Llama-3.2-1B-Instruct
+datasets:
+- ngxson/MiniThinky-dataset
+---
+
+# 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 (v1) and an additional of 6 hours (v2)
+
+**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)