Fine tuned model of mistral-7b-instruct-v0-2. the dataset used for fine tuning is small and custom dataset for question answering in turkish language made by me. the main duty was to make model more adapted to turkish language. prompt format :
[INST]<<SYS>>
{System}
<</SYS>>
{User} [/INST] {Assistant}
example in turkish language ;
[INST]<<SYS>>
Aşağıda bir görevi tanımlayan bir talimat bulunmaktadır. İsteği uygun şekilde tamamlayan bir yanıt yazın.
<</SYS>>
python dilinde yılan oyunu için kod verin [/INST] Aşağıda Python dili ile basit bir snake game kodu bulunmaktadır;
import os
import time
# Konfigürasyonlar
WIDTH = 20
HEIGHT = 10
snake_speed = 0.1
snake_size = 3
class Snake:
def __init__(self):
[i cutted output for brevity]
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 65.63 |
AI2 Reasoning Challenge (25-Shot) | 62.97 |
HellaSwag (10-Shot) | 84.87 |
MMLU (5-Shot) | 60.78 |
TruthfulQA (0-shot) | 68.18 |
Winogrande (5-shot) | 77.43 |
GSM8k (5-shot) | 39.58 |
- Downloads last month
- 69
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard62.970
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard84.870
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard60.780
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard68.180
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard77.430
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard39.580