README.md

---
license: cc-by-nc-4.0
tags:
- merge
- lazymergekit
- dpo
- rlhf
dataset:
- mlabonne/truthy-dpo-v0.1
- mlabonne/distilabel-intel-orca-dpo-pairs
- mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha
base_model:
- mlabonne/NeuralMonarch-7B
language:
- en
---

![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/TI7C8F2gk43gmI9U2L0uk.jpeg)

# 👑 AlphaMonarch-7B

**tl;dr: AlphaMonarch-7B is a new DPO merge that retains all the reasoning abilities of the very best merges and significantly improves its conversational abilities. Kind of the best of both worlds in a 7B model. 🎉**

AlphaMonarch-7B is a DPO fine-tuned of [mlabonne/NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B/) using the [argilla/OpenHermes2.5-dpo-binarized-alpha](https://huggingface.co/datasets/argilla/OpenHermes2.5-dpo-binarized-alpha) preference dataset.

It is based on a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [mlabonne/OmniTruthyBeagle-7B-v0](https://huggingface.co/mlabonne/OmniTruthyBeagle-7B-v0)
* [mlabonne/NeuBeagle-7B](https://huggingface.co/mlabonne/NeuBeagle-7B)
* [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B)

Special thanks to [Jon Durbin](https://huggingface.co/jondurbin), [Intel](https://huggingface.co/Intel), and [Argilla](https://huggingface.co/argilla) for the preference datasets.

## 🔍 Applications

This model uses a context window of 8k. I recommend using it with the Mistral Instruct chat template (works perfectly with LM Studio).

It is one of the very best 7B models in terms of instructing following and reasoning abilities and can be used for conversations, RP, and storytelling. Note that it tends to have a quite formal and sophisticated style, but it can be changed by modifying the prompt.

## ⚡ Quantized models

* **GGUF**: https://huggingface.co/mlabonne/AlphaMonarch-7B-GGUF

## 🏆 Evaluation

### Nous

The evaluation was performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval) on Nous suite. See the entire leaderboard [here](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard).

| Model | Average | AGIEval | GPT4All | TruthfulQA | Bigbench |
|---|---:|---:|---:|---:|---:|
| [**AlphaMonarch-7B**](https://huggingface.co/mlabonne/AlphaMonarch-7B) [📄](https://gist.github.com/mlabonne/1d33c86824b3a11d2308e36db1ba41c1) | **62.74** | **45.37** | **77.01** | **78.39** | **50.2** |
| [NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B) [📄](https://gist.github.com/mlabonne/64050c96c6aa261a8f5b403190c8dee4) | 62.73 | 45.31 | 76.99 | 78.35 | 50.28 |
| [Monarch-7B](https://huggingface.co/mlabonne/Monarch-7B) [📄](https://gist.github.com/mlabonne/0b8d057c5ece41e0290580a108c7a093) | 62.68 | 45.48 | 77.07 | 78.04 | 50.14 |
| [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/88b21dd9698ffed75d6163ebdc2f6cc8) | 52.42 | 42.75 | 72.99 | 52.99 | 40.94 |
| [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/14687f1eb3425b166db511f31f8e66f6) | 53.51 | 43.67 | 73.24 | 55.37 | 41.76 |
| [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B) [📄](https://gist.github.com/mlabonne/ad0c665bbe581c8420136c3b52b3c15c) | 60.25 | 46.06 | 76.77 | 70.32 | 47.86 |
| [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B) [📄](https://gist.github.com/mlabonne/0e49d591787185fa5ae92ca5d9d4a1fd) | 62.3 | 45.85 | 77.26 | 76.06 | 50.03 |
| [eren23/dpo-binarized-NeuralTrix-7B](https://huggingface.co/eren23/dpo-binarized-NeuralTrix-7B) [📄](https://gist.github.com/CultriX-Github/dbdde67ead233df0c7c56f1b091f728c) | 62.5 | 44.57 | 76.34 | 79.81 | 49.27 |
| [CultriX/NeuralTrix-7B-dpo](https://huggingface.co/CultriX/NeuralTrix-7B-dpo) [📄](https://gist.github.com/CultriX-Github/df0502599867d4043b45d9dafb5976e8) | 62.5 | 44.61 | 76.33 | 79.8 | 49.24 |

### EQ-bench

AlphaMonarch-7B is the second best-performing 7B model on [EQ-bench](https://eqbench.com/) by Samuel J. Peach.



### MT-Bench

```
########## First turn ##########
                                    score
model                       turn         
gpt-4                       1     8.95625
OmniBeagle-7B               1     8.32500 
AlphaMonarch-7B             1     8.23750
claude-v1                   1     8.15000
gpt-3.5-turbo               1     8.07500
claude-instant-v1           1     7.80000


########## Second turn ##########
                                     score
model                       turn          
gpt-4                       2     9.025000
claude-instant-v1           2     8.012658
gpt-3.5-turbo               2     7.812500
claude-v1                   2     7.650000
AlphaMonarch-7B             2     7.618750
OmniBeagle-7B               2     7.587500

########## Average ##########
                                score
model                                
gpt-4                        8.990625
OmniBeagle-7B                7.956250
gpt-3.5-turbo                7.943750
AlphaMonarch-7B              7.928125
claude-instant-v1            7.905660
claude-v1                    7.900000
NeuralBeagle14-7B            7.628125
```

### Open LLM Leaderboard

AlphaMonarch-7B is one of the best-performing non-merge 7B models on the Open LLM Leaderboard:

![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/njHxX_ERQaBssHqp17fMy.png)

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/AlphaMonarch-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```