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README.md ADDED
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+ ---
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+ license: cc-by-nc-4.0
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+ tags:
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+ - merge
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+ - lazymergekit
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+ - dpo
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+ - rlhf
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+ dataset:
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+ - mlabonne/truthy-dpo-v0.1
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+ - mlabonne/distilabel-intel-orca-dpo-pairs
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+ - mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha
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+ base_model:
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+ - mlabonne/NeuralMonarch-7B
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+ language:
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+ - en
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+ ---
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+
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+ ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/TI7C8F2gk43gmI9U2L0uk.jpeg)
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+
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+ # 👑 AlphaMonarch-7B
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+
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+ **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. 🎉**
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+
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+ 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.
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+
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+ It is based on a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
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+ * [mlabonne/OmniTruthyBeagle-7B-v0](https://huggingface.co/mlabonne/OmniTruthyBeagle-7B-v0)
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+ * [mlabonne/NeuBeagle-7B](https://huggingface.co/mlabonne/NeuBeagle-7B)
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+ * [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B)
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+
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+ 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.
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+
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+ **Try the demo**: https://huggingface.co/spaces/mlabonne/AlphaMonarch-7B-GGUF-Chat
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+
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+ ## 🔍 Applications
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+
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+ This model uses a context window of 8k. I recommend using it with the Mistral Instruct chat template (works perfectly with LM Studio).
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+
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+ 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.
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+
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+ ## ⚡ Quantized models
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+
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+ * **GGUF**: https://huggingface.co/mlabonne/AlphaMonarch-7B-GGUF
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+
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+ ## 🏆 Evaluation
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+
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+ ### Nous
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+
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+ 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).
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+
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+ | Model | Average | AGIEval | GPT4All | TruthfulQA | Bigbench |
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+ |---|---:|---:|---:|---:|---:|
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+ | [**AlphaMonarch-7B**](https://huggingface.co/mlabonne/AlphaMonarch-7B) [📄](https://gist.github.com/mlabonne/1d33c86824b3a11d2308e36db1ba41c1) | **62.74** | **45.37** | **77.01** | **78.39** | **50.2** |
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+ | [NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B) [📄](https://gist.github.com/mlabonne/64050c96c6aa261a8f5b403190c8dee4) | 62.73 | 45.31 | 76.99 | 78.35 | 50.28 |
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+ | [Monarch-7B](https://huggingface.co/mlabonne/Monarch-7B) [📄](https://gist.github.com/mlabonne/0b8d057c5ece41e0290580a108c7a093) | 62.68 | 45.48 | 77.07 | 78.04 | 50.14 |
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+ | [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 |
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+ | [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 |
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+ | [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 |
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+ | [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 |
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+ | [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 |
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+ | [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 |
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+
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+ ### EQ-bench
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+
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+ 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.
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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/dnCFxieqLiAC3Ll6CfdZW.png)
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+
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+ ### MT-Bench
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+
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+ ```
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+ ########## First turn ##########
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+ score
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+ model turn
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+ gpt-4 1 8.95625
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+ OmniBeagle-7B 1 8.31250
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+ AlphaMonarch-7B 1 8.23750
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+ claude-v1 1 8.15000
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+ NeuralMonarch-7B 1 8.09375
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+ gpt-3.5-turbo 1 8.07500
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+ claude-instant-v1 1 7.80000
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+
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+ ########## Second turn ##########
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+ score
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+ model turn
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+ gpt-4 2 9.025000
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+ claude-instant-v1 2 8.012658
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+ OmniBeagle-7B 2 7.837500
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+ gpt-3.5-turbo 2 7.812500
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+ claude-v1 2 7.650000
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+ AlphaMonarch-7B 2 7.618750
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+ NeuralMonarch-7B 2 7.375000
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+
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+ ########## Average ##########
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+ score
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+ model
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+ gpt-4 8.990625
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+ OmniBeagle-7B 8.075000
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+ gpt-3.5-turbo 7.943750
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+ AlphaMonarch-7B 7.928125
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+ claude-instant-v1 7.905660
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+ claude-v1 7.900000
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+ NeuralMonarch-7B 7.734375
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+ NeuralBeagle14-7B 7.628125
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+ ```
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+
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+ ### Open LLM Leaderboard
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+
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+ AlphaMonarch-7B is one of the best-performing non-merge 7B models on the Open LLM Leaderboard:
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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/njHxX_ERQaBssHqp17fMy.png)
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+
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+ ## 💻 Usage
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+
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+ ```python
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+ !pip install -qU transformers accelerate
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+
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+ from transformers import AutoTokenizer
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+ import transformers
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+ import torch
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+
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+ model = "mlabonne/AlphaMonarch-7B"
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+ messages = [{"role": "user", "content": "What is a large language model?"}]
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+
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+ tokenizer = AutoTokenizer.from_pretrained(model)
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+ prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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+ pipeline = transformers.pipeline(
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+ "text-generation",
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+ model=model,
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+ torch_dtype=torch.float16,
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+ device_map="auto",
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+ )
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+
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+ outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
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+ print(outputs[0]["generated_text"])
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+ ```
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+ "use_cache": true,
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+ "vocab_size": 32000
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