library_name: transformers
tags:
- trl
- sft
base_model:
- meta-llama/Llama-3.2-1B-Instruct
datasets:
- ngxson/MiniThinky-dataset
MiniThinky 1B
My first trial to fine tune a small model to add reasoning capability.
Link to GGUF version: click here
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)