--- license: apache-2.0 tags: - moe - merge - mergekit - lazymergekit - mlabonne/AlphaMonarch-7B - OmnicromsBrain/Eros_Scribe-7b - SanjiWatsuki/Kunoichi-DPO-v2-7B - OmnicromsBrain/NeuralStar_Fusion-7B base_model: - mlabonne/AlphaMonarch-7B - OmnicromsBrain/Eros_Scribe-7b - SanjiWatsuki/Kunoichi-DPO-v2-7B - OmnicromsBrain/NeuralStar_Fusion-7B --- ![FusionWriter-7b.png](https://cdn-uploads.huggingface.co/production/uploads/65c70c9e21d80a923d664563/0WNo5m8BWu7lF4YSlMX7K.png) # NeuralStar_FusionWriter_4x7b NeuralStar_FusionWriter_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) * [OmnicromsBrain/Eros_Scribe-7b](https://huggingface.co/OmnicromsBrain/Eros_Scribe-7b) * [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B) * [OmnicromsBrain/NeuralStar_Fusion-7B](https://huggingface.co/OmnicromsBrain/NeuralStar_Fusion-7B) ## ⚡ Quantized Models Special thanks to **MRadermacher** for the **static** and **imatrix** quantized models **.GGUF** https://huggingface.co/mradermacher/NeuralStar_FusionWriter_4x7b-GGUF **IMatrix** https://huggingface.co/mradermacher/NeuralStar_FusionWriter_4x7b-i1-GGUF ## 🧩 Configuration ```yaml base_model: mlabonne/AlphaMonarch-7B experts: - source_model: mlabonne/AlphaMonarch-7B positive_prompts: - "chat" - "assistant" - "tell me" - "explain" - "ideas" - source_model: OmnicromsBrain/Eros_Scribe-7b positive_prompts: - "adult" - "sex" - "explicit" - "nsfw" - "gory" - source_model: SanjiWatsuki/Kunoichi-DPO-v2-7B positive_prompts: - "story" - "character" - "scene" - "plot" - "editor" - source_model: OmnicromsBrain/NeuralStar_Fusion-7B positive_prompts: - "codex" - "write" - "outline" - "scenebeat" - "prose" ``` ## 💻 Usage ```python !pip install -qU transformers bitsandbytes accelerate from transformers import AutoTokenizer import transformers import torch model = "OmnicromsBrain/NeuralStar_FusionWriter_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"]) ```