---
license: llama3
language:
- en
library_name: transformers
pipeline_tag: text2text-generation
---

**Model Name: llama_3_orca_mini_v4_8b**

##  Llama-3-8b base model trained on Orca Style Mini Datasets

<img src="" width="600" />

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](https://github.com/EleutherAI/lm-evaluation-harness) from EleutherAI. 

Here are the results on metrics used by [HuggingFaceH4 Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |57.24|
|AI2 Reasoning Challenge (25-Shot)|63.14|
|HellaSwag (10-Shot)              |82.35|
|MMLU (5-Shot)                    |56.52|
|TruthfulQA (0-shot)              |51.81|
|Winogrande (5-shot)              |76.48|
|GSM8k (5-shot)                   |13.12|


<br>

## 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 AutoConfig, AutoModel, AutoTokenizer
config = AutoConfig.from_pretrained("investbrainsorg/BrokenLlama-3-8b")
model = AutoModel.from_pretrained("investbrainsorg/BrokenLlama-3-8b")
tokenizer = AutoTokenizer.from_pretrained("investbrainsorg/BrokenLlama-3-8b")

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(message, return_tensors="pt")
model.generate(**gen_input)
```
This model is governed by [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](LICENSE)

**Quants**

GGUF : Coming Soon

AWQ: Coming Soon