transformers safetensors mixtral text-generation Retrieval Augmented Generation RAG Multi-domain ko dataset:HumanF-MarkrAI/Korean-RAG-ver2 license:cc-by-nc-sa-4.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
---
license: cc-by-nc-sa-4.0
datasets:
- HumanF-MarkrAI/Korean-RAG-ver2
language:
- ko
tags:
  - Retrieval Augmented Generation
  - RAG
  - Multi-domain
---

# MarkrAI/RAG-KO-Mixtral-7Bx2-v1.15

# Model Details  

## Model Developers  
MarkrAI - AI Researchers

## Base Model  
[DopeorNope/Ko-Mixtral-v1.3-MoE-7Bx2](https://huggingface.co/DopeorNope/Ko-Mixtral-v1.3-MoE-7Bx2).  

## Instruction tuning Method  
Using QLoRA.  
```
4-bit quantization
Lora_r: 64
Lora_alpha: 64
Lora_dropout: 0.05
Lora_target_modules: [embed_tokens, q_proj, k_proj, v_proj, o_proj, gate, w1, w2, w3, lm_head]
```

## Hyperparameters  
```
Epoch: 3
Batch size: 64
Learning_rate: 1e-5
Learning scheduler: linear
Warmup_ratio: 0.06
```

## Datasets
Private datasets: [HumanF-MarkrAI/Korean-RAG-ver2](https://huggingface.co/datasets/HumanF-MarkrAI/Korean-RAG-ver2)  
```
Aihub datasets 활용하여서 제작함.  
```

## Implmentation Code
```
### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo = "MarkrAI/RAG-KO-Mixtral-7Bx2-v1.15"
OpenOrca = AutoModelForCausalLM.from_pretrained(
        repo,
        return_dict=True,
        torch_dtype=torch.float16,
        device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)

```

# Model Benchmark
- Coming soon...  