By providing proper credit and attribution, you are granted permission to use this model as a foundational base for further Full fine tuning, DPO, PPO or ORPO tuning and any kind of Merges. I actively encourage users to customize and enhance the model according to their specific needs, as this version is designed to be a comprehensive general model. Dive in and innovate!

Evaluation

Metric	Value
Avg.	67.78
AI2 Reasoning Challenge (25-Shot)	61.86
HellaSwag (10-Shot)	82.35
MMLU (5-Shot)	65.10
TruthfulQA (0-shot)	56.24
Winogrande (5-shot)	73.40
GSM8k (5-shot)	67.70

Example Usage

Here is the ChatML prompt format

<|im_start|>system
You are Orca Mini, a helpful AI assistant.<|im_end|>
<|im_start|>user
Hello Orca Mini, what can you do for me?<|im_end|>
<|im_start|>assistant

Below shows a code example on how to use this model

python

from transformers import AutoModel, AutoTokenizer
model_slug = "pankajmathur/orca_mini_v5_8b_dpo"
model = AutoModel.from_pretrained(model_slug)
tokenizer = AutoTokenizer.from_pretrained(model_slug)

messages = [
    {"role": "system", "content": "You are Orca Mini, a helpful AI assistant."},
    {"role": "user", "content": "Hello Orca Mini, what can you do for me?"}
]

gen_input = tokenizer.apply_chat_template(messages, return_tensors="pt")
model.generate(**gen_input)

This model is governed by META LLAMA 3 COMMUNITY LICENSE AGREEMENT

Quants

GGUF : Coming Soon

AWQ: Coming Soon

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	19.96
IFEval (0-Shot)	48.96
BBH (3-Shot)	29.61
MATH Lvl 5 (4-Shot)	7.48
GPQA (0-shot)	3.24
MuSR (0-shot)	6.94
MMLU-PRO (5-shot)	23.51