--- license: apache-2.0 tags: - moe - frankenmoe - merge - mergekit - lazymergekit - mlabonne/AlphaMonarch-7B - FPHam/Karen_TheEditor_V2_STRICT_Mistral_7B - SanjiWatsuki/Kunoichi-DPO-v2-7B - OmnicromsBrain/NeuralStar-7b-Lazy base_model: - mlabonne/AlphaMonarch-7B - FPHam/Karen_TheEditor_V2_STRICT_Mistral_7B - SanjiWatsuki/Kunoichi-DPO-v2-7B - OmnicromsBrain/NeuralStar-7b-Lazy model-index: - name: NeuralStar_AlphaWriter_4x7b 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: 70.22 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=OmnicromsBrain/NeuralStar_AlphaWriter_4x7b 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: 88.31 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=OmnicromsBrain/NeuralStar_AlphaWriter_4x7b 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: 64.6 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=OmnicromsBrain/NeuralStar_AlphaWriter_4x7b 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: 71.7 source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=OmnicromsBrain/NeuralStar_AlphaWriter_4x7b 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: 82.0 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=OmnicromsBrain/NeuralStar_AlphaWriter_4x7b 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: 63.0 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=OmnicromsBrain/NeuralStar_AlphaWriter_4x7b name: Open LLM Leaderboard --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65c70c9e21d80a923d664563/ntyev6qExGVY3Ysg2D6-l.png) # NeuralStar_AlphaWriter_4x7b I was blown away by the writing results I was getting from mlabonne/Beyonder-4x7B-v3 while writing in [NovelCrafter](https://www.novelcrafter.com). Inspired by his [LLM Course](https://github.com/mlabonne/llm-course) and fueled by his [LazyMergeKit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb). I couldnt help but wonder what a writing model would be like if all 4 “experts” excelled in creative writing. I present NeuralStar-AlphaWriter-4x7b: NeuralStar_AlphaWriter_4x7b is a Mixture of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B) * [FPHam/Karen_TheEditor_V2_STRICT_Mistral_7B](https://huggingface.co/FPHam/Karen_TheEditor_V2_STRICT_Mistral_7B) * [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B) * [OmnicromsBrain/NeuralStar-7b-Lazy](https://huggingface.co/OmnicromsBrain/NeuralStar-7b-Lazy) ## ⚡ Quantized Models Thanks to MRadermacher for the quantized models **.GGUF** https://huggingface.co/mradermacher/NeuralStar_AlphaWriter_4x7b-GGUF Q4_K_M and Q5_K_M .gguf [**Here**](https://huggingface.co/OmnicromsBrain/NeuralStar_AlphaWriter_4x7b-GGUF) created with [mlabonne/Autogguf](https://colab.research.google.com/drive/1P646NEg33BZy4BfLDNpTz0V0lwIU3CHu) ## 🧩 Configuration ```yaml base_model: mlabonne/AlphaMonarch-7B experts: - source_model: mlabonne/AlphaMonarch-7B positive_prompts: - "chat" - "assistant" - "tell me" - "explain" - "I want" - source_model: FPHam/Karen_TheEditor_V2_STRICT_Mistral_7B positive_prompts: - "edit" - "rewrite" - "evaluate" - "spelling" - "grammer" - source_model: SanjiWatsuki/Kunoichi-DPO-v2-7B positive_prompts: - "storywriting" - "write" - "scene" - "prose" - "character" - source_model: OmnicromsBrain/NeuralStar-7b-Lazy positive_prompts: - "codex" - "plot" - "outline" - "scenebeat" - "count" ``` ## 💻 Usage ```python !pip install -qU transformers bitsandbytes accelerate from transformers import AutoTokenizer import transformers import torch model = "OmnicromsBrain/NeuralStar_AlphaWriter_4x7b" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True}, ) messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}] prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) 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"]) ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_OmnicromsBrain__NeuralStar_AlphaWriter_4x7b) | Metric |Value| |---------------------------------|----:| |Avg. |73.31| |AI2 Reasoning Challenge (25-Shot)|70.22| |HellaSwag (10-Shot) |88.31| |MMLU (5-Shot) |64.60| |TruthfulQA (0-shot) |71.70| |Winogrande (5-shot) |82.00| |GSM8k (5-shot) |63.00|