Hugging Face's logo

RichardErkhov
/
moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf

GGUF
Inference Endpoints
Model card Files Community
moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf
File size: 5,889 Bytes 
f892a31
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
 
Quantization made by Richard Erkhov.

[Github](https://github.com/RichardErkhov)

[Discord](https://discord.gg/pvy7H8DZMG)

[Request more models](https://github.com/RichardErkhov/quant_request)


MoMo-72B-lora-1.8.6-DPO - GGUF
- Model creator: https://huggingface.co/moreh/
- Original model: https://huggingface.co/moreh/MoMo-72B-lora-1.8.6-DPO/


| Name | Quant method | Size |
| ---- | ---- | ---- |
| [MoMo-72B-lora-1.8.6-DPO.Q2_K.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/blob/main/MoMo-72B-lora-1.8.6-DPO.Q2_K.gguf) | Q2_K | 25.22GB |
| [MoMo-72B-lora-1.8.6-DPO.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/blob/main/MoMo-72B-lora-1.8.6-DPO.IQ3_XS.gguf) | IQ3_XS | 27.88GB |
| [MoMo-72B-lora-1.8.6-DPO.IQ3_S.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/blob/main/MoMo-72B-lora-1.8.6-DPO.IQ3_S.gguf) | IQ3_S | 29.4GB |
| [MoMo-72B-lora-1.8.6-DPO.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/blob/main/MoMo-72B-lora-1.8.6-DPO.Q3_K_S.gguf) | Q3_K_S | 29.4GB |
| [MoMo-72B-lora-1.8.6-DPO.IQ3_M.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/blob/main/MoMo-72B-lora-1.8.6-DPO.IQ3_M.gguf) | IQ3_M | 30.98GB |
| [MoMo-72B-lora-1.8.6-DPO.Q3_K.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/blob/main/MoMo-72B-lora-1.8.6-DPO.Q3_K.gguf) | Q3_K | 32.85GB |
| [MoMo-72B-lora-1.8.6-DPO.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/blob/main/MoMo-72B-lora-1.8.6-DPO.Q3_K_M.gguf) | Q3_K_M | 32.85GB |
| [MoMo-72B-lora-1.8.6-DPO.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/blob/main/MoMo-72B-lora-1.8.6-DPO.Q3_K_L.gguf) | Q3_K_L | 35.85GB |
| [MoMo-72B-lora-1.8.6-DPO.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/blob/main/MoMo-72B-lora-1.8.6-DPO.IQ4_XS.gguf) | IQ4_XS | 36.41GB |
| [MoMo-72B-lora-1.8.6-DPO.Q4_0.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/tree/main/) | Q4_0 | 38.19GB |
| [MoMo-72B-lora-1.8.6-DPO.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/tree/main/) | IQ4_NL | 38.42GB |
| [MoMo-72B-lora-1.8.6-DPO.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/tree/main/) | Q4_K_S | 38.45GB |
| [MoMo-72B-lora-1.8.6-DPO.Q4_K.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/tree/main/) | Q4_K | 40.77GB |
| [MoMo-72B-lora-1.8.6-DPO.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/tree/main/) | Q4_K_M | 40.77GB |
| [MoMo-72B-lora-1.8.6-DPO.Q4_1.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/tree/main/) | Q4_1 | 42.32GB |
| [MoMo-72B-lora-1.8.6-DPO.Q5_0.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/tree/main/) | Q5_0 | 46.46GB |
| [MoMo-72B-lora-1.8.6-DPO.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/tree/main/) | Q5_K_S | 46.46GB |
| [MoMo-72B-lora-1.8.6-DPO.Q5_K.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/tree/main/) | Q5_K | 47.79GB |
| [MoMo-72B-lora-1.8.6-DPO.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/tree/main/) | Q5_K_M | 47.79GB |
| [MoMo-72B-lora-1.8.6-DPO.Q5_1.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/tree/main/) | Q5_1 | 50.59GB |
| [MoMo-72B-lora-1.8.6-DPO.Q6_K.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/tree/main/) | Q6_K | 55.24GB |
| [MoMo-72B-lora-1.8.6-DPO.Q8_0.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/tree/main/) | Q8_0 | 71.55GB |




Original model description:
---
license: mit
language:
- en
---
# **Introduction**
MoMo-72B-lora-1.8.6-DPO is trained via Direct Preference Optimization([DPO](https://arxiv.org/abs/2305.18290)) from [MoMo-72B-LoRA-V1.4](https://huggingface.co/moreh/MoMo-72B-LoRA-V1.4) as its base model, with several optimizations in hyperparameters.  
[MoMo-72B-LoRA-V1.4](https://huggingface.co/moreh/MoMo-72B-LoRA-V1.4) is trained via Supervised Fine-Tuning (SFT) using [LoRA](https://arxiv.org/abs/2106.09685), with the QWEN-72B model as its base-model.  
Note that we did not exploit any form of weight merge.  
For leaderboard submission, the trained weight is realigned for compatibility with llama.  
MoMo-72B is trained using **[Moreh](https://moreh.io/)**'s [MoAI platform](https://moreh.io/product), which simplifies the training of large-scale models, and AMD's MI250 GPU.


## Details
### Used Librarys
- torch
- peft
### Used Datasets
- [slimorca](Open-Orca/SlimOrca)
- [truthy](https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1)
- [orca_dpo_pairs](https://huggingface.co/datasets/Intel/orca_dpo_pairs)
- No other dataset was used
- No benchmark test set or the training set are used
  - [data contamination check](https://github.com/swj0419/detect-pretrain-code-contamination) result
    
| Model                        | ARC   | MMLU | TruthfulQA | GSM8K |
|------------------------------|-------|-------|-------|-------|
| **V1.8.6(result < 0.1, %)**| TBU |TBU | 0.73 | TBU |
### Used Environments
- AMD MI250 & MoAI platform
- Please visit https://moreh.io/product for more information about MoAI platform
- Or, contact us directly [[email protected]](mailto:[email protected])

## How to use

```python
# pip install transformers==4.35.2
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("moreh/MoMo-72B-lora-1.8.6-DPO")
model = AutoModelForCausalLM.from_pretrained(
    "moreh/MoMo-72B-lora-1.8.6-DPO"
)
```