mlabonne
/

AlphaMonarch-7B

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 ---
+license: cc-by-nc-4.0
+tags:
+- merge
+- lazymergekit
+dataset:
+- mlabonne/truthy-dpo-v0.1
+- mlabonne/distilabel-intel-orca-dpo-pairs
+- mlabonne/distilabel-intel-orca-dpo-pairs
+base_model:
+- mlabonne/NeuralMonarch-7B
+language:
+- en
 ---
+![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/TI7C8F2gk43gmI9U2L0uk.jpeg)
+# 👑 AlphaMonarch-7B
+**Update 14/02/24: AlphaMonarch-7B is the new best-performing 7B model on Nous' benchmark suite! 🎉**
+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.
+Compared to other 7B models, it displays good performance in instruction following and reasoning tasks. It can also be used for RP and storytelling.
+## ⚡ Quantized models
+* **GGUF**: https://huggingface.co/mlabonne/NeuralMonarch-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 |
+|---|---:|---:|---:|---:|---:|
+| [**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 |
+| [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 |
+### 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)
+### MT-Bench
+```
+########## First turn ##########
+                                    score
+model                       turn
+gpt-4                       1     8.95625
+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
+########## Average ##########
+                                score
+model
+gpt-4                        8.990625
+gpt-3.5-turbo                7.943750
+AlphaMonarch-7B              7.928125
+claude-instant-v1            7.905660
+claude-v1                    7.900000
+```
+## 💻 Usage
+```python
+!pip install -qU transformers accelerate
+from transformers import AutoTokenizer
+import transformers
+import torch
+model = "mlabonne/MonarchMonarch-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"])
+```