--- title: NeuralMonarch-7B canonical_url: "https://www.modelscope.cn/models/mlabonne/NeuralMonarch-7B" md_url: "https://www.modelscope.cn/models/mlabonne/NeuralMonarch-7B.md" repository: mlabonne/NeuralMonarch-7B last_updated: 2025-03-18 license: cc-by-nc-4.0 pipeline_tag: text-generation tasks: - text-generation model_type: - mistral architectures: - MistralForCausalLM parameters: 7.2B tensor_type: - F16 library_name: - pytorch - transformer - safetensors frameworks: - pytorch language: - en inference_backends: - "deploy_task text/emb" - "lmdeploy 0.9.1" - "lmdeploy_turbomind 0.9.1" - "sglang 0.5.2" - "vllm 0.9.2" downloads: 570 stars: 0 tags: - merge - lazymergekit - dpo - rlhf --- # NeuralMonarch-7B > NeuralMonarch-7B - mlabonne 在 ModelScope 开源的模型。NeuralMonarch-7B is a DPO fine-tuned of mlabonne/Monarch-7B using the jondurbin/truthy-dpo-v0.1 and argilla/distilabel-intel-orca-dpo-pairs preference datasets. mlabonne/NeuralMonarch-7B 是 ModelScope 魔搭社区上的 7.2B 参数text-generation模型，采用 cc-by-nc-4.0 许可，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。 - **Repository**: mlabonne/NeuralMonarch-7B - **License**: cc-by-nc-4.0 - **Tasks**: text-generation - **Parameters**: 7.2B - **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2 - **Tags**: merge, lazymergekit, dpo, rlhf - **Downloads**: 570 - **Stars**: 0 - **Last updated**: 2025-03-18 Source: https://www.modelscope.cn/models/mlabonne/NeuralMonarch-7B --- ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/LxRUvkSATmy-UDKN54Q3H.jpeg) # 👑 NeuralMonarch-7B NeuralMonarch-7B is a DPO fine-tuned of [mlabonne/Monarch-7B](https://huggingface.co/mlabonne/Monarch-7B/) using the [jondurbin/truthy-dpo-v0.1](https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1) and [argilla/distilabel-intel-orca-dpo-pairs](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs) preference datasets. 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. **Try the demo**: https://huggingface.co/spaces/mlabonne/NeuralMonarch-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). Compared to other 7B models, it performs well in instruction following and reasoning tasks. For a chat/RP model with strong reasoning abilities, check out [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B). ## ⚡ Quantized models * **GGUF**: https://huggingface.co/mlabonne/NeuralMonarch-7B-GGUF ## 🏆 Evaluation ### Nous NeuralMonarch-7B is one of the best-performing 7B models 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 | |---|---:|---:|---:|---:|---:| | [**NeuralMonarch-7B**](https://huggingface.co/mlabonne/NeuralMonarch-7B) [📄](https://gist.github.com/mlabonne/64050c96c6aa261a8f5b403190c8dee4) | **62.73** | **45.31** | **76.99** | **78.35** | **50.28** | | [AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B) [📄](https://gist.github.com/mlabonne/1d33c86824b3a11d2308e36db1ba41c1) | 62.74 | 45.37 | 77.01 | 78.39 | 50.2 | | [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 NeuralMonarch-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) ### Open LLM Leaderboard NeuralMonarch-7B is one of the best-performing 7B models on the Open LLM Leaderboard. ### 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 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/NeuralMonarch-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"]) ```