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Model Name: llama_3_orca_mini_v5_8b
Llama-3-8b base model trained on Orca Style Mini Datasets
"Obsessed with GenAI's potential? So am I ! Let's create together 🚀 https://www.linkedin.com/in/pankajam"
NOTICE
By providing proper credit and attribution, you are granted permission to use this model as a foundational base for further DPO/PPO tuning or 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, fully fine-tuned general model. Dive in and innovate!
Evaluation
We evaluated this model on a wide range of tasks using Language Model Evaluation Harness from EleutherAI.
Here are the results on similar metrics used by HuggingFaceH4 Open LLM Leaderboard
| Metric | Value |
|---|---|
| Avg. | 67.28 |
| AI2 Reasoning Challenge (25-Shot) | 60.92 |
| HellaSwag (10-Shot) | 81.78 |
| MMLU (5-Shot) | 64.97 |
| TruthfulQA (0-shot) | 55.04 |
| Winogrande (5-shot) | 73.40 |
| GSM8k (5-shot) | 67.55 |
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
from transformers import AutoModel, AutoTokenizer
model_slug = "pankajmathur/orca_mini_v5_8b"
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. | 20.16 |
| IFEval (0-Shot) | 48.06 |
| BBH (3-Shot) | 29.35 |
| MATH Lvl 5 (4-Shot) | 7.85 |
| GPQA (0-shot) | 4.92 |
| MuSR (0-shot) | 7.70 |
| MMLU-PRO (5-shot) | 23.07 |
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Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard48.060
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard29.350
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard7.850
- acc_norm on GPQA (0-shot)Open LLM Leaderboard4.920
- acc_norm on MuSR (0-shot)Open LLM Leaderboard7.700
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard23.070