--- language: - en license: cc-by-nc-4.0 tags: - merge - lazymergekit - dpo - rlhf - autoquant - exl2 dataset: - mlabonne/truthy-dpo-v0.1 - mlabonne/distilabel-intel-orca-dpo-pairs - mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha base_model: - mlabonne/NeuralMonarch-7B model-index: - name: AlphaMonarch-7B results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 73.04 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/AlphaMonarch-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 89.18 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/AlphaMonarch-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 64.4 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/AlphaMonarch-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 77.91 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/AlphaMonarch-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 84.69 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/AlphaMonarch-7B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 66.72 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/AlphaMonarch-7B name: Open LLM Leaderboard --- ![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), [Argilla](https://huggingface.co/argilla), and [Teknium](https://huggingface.co/teknium) for the preference datasets. **Try the demo**: https://huggingface.co/spaces/mlabonne/AlphaMonarch-7B-GGUF-Chat ## 🔍 Applications This model uses a context window of 8k. I recommend using it with the Mistral Instruct chat template (works perfectly with LM Studio). If you use SillyTavern, you might want to tweak the inference parameters. Here's what LM Studio uses as a reference: `temp` 0.8, `top_k` 40, `top_p` 0.95, `min_p` 0.05, `repeat_penalty` 1.1. 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 Thanks to [LoneStriker](https://huggingface.co/LoneStriker) for the GPTQ, AWQ, and EXL2 quants. * **GGUF**: https://huggingface.co/mlabonne/AlphaMonarch-7B-GGUF * **GPTQ**: https://huggingface.co/LoneStriker/AlphaMonarch-7B-GPTQ * **AWQ**: https://huggingface.co/LoneStriker/AlphaMonarch-7B-AWQ * **mlx**: https://huggingface.co/mlx-community/AlphaMonarch-7B-mlx * **EXL2**: * https://huggingface.co/LoneStriker/AlphaMonarch-7B-3.0bpw-h6-exl2 * https://huggingface.co/LoneStriker/AlphaMonarch-7B-4.0bpw-h6-exl2 * https://huggingface.co/LoneStriker/AlphaMonarch-7B-5.0bpw-h6-exl2 * https://huggingface.co/LoneStriker/AlphaMonarch-7B-6.0bpw-h6-exl2 * https://huggingface.co/LoneStriker/AlphaMonarch-7B-8.0bpw-h6-exl2 ## 🏆 Evaluation ### Nous AlphaMonarch-7B is the best-performing 7B model on Nous' benchmark suite (evaluation performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval)). 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 also outperforming 70B and 120B parameter models on [EQ-bench](https://eqbench.com/) by [Samuel J. Paech](https://twitter.com/sam_paech), who kindly ran the evaluations. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/dnCFxieqLiAC3Ll6CfdZW.png) ### MT-Bench ``` ########## First turn ########## score model turn gpt-4 1 8.95625 OmniBeagle-7B 1 8.31250 AlphaMonarch-7B 1 8.23750 claude-v1 1 8.15000 NeuralMonarch-7B 1 8.09375 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 OmniBeagle-7B 2 7.837500 gpt-3.5-turbo 2 7.812500 claude-v1 2 7.650000 AlphaMonarch-7B 2 7.618750 NeuralMonarch-7B 2 7.375000 ########## Average ########## score model gpt-4 8.990625 OmniBeagle-7B 8.075000 gpt-3.5-turbo 7.943750 AlphaMonarch-7B 7.928125 claude-instant-v1 7.905660 claude-v1 7.900000 NeuralMonarch-7B 7.734375 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) ## 🌳 Model Family Tree ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/IMAE6DpzkUN6YaEhOX2wA.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"]) ```