--- tags: - merge - mergekit - lazymergekit - automerger/YamShadow-7B - mlabonne/AlphaMonarch-7B - automerger/OgnoExperiment27-7B - Kukedlc/Jupiter-k-7B-slerp base_model: - automerger/YamShadow-7B - mlabonne/AlphaMonarch-7B - automerger/OgnoExperiment27-7B - Kukedlc/Jupiter-k-7B-slerp --- # NeuralShiva-7B-DT NeuralShiva-7B-DT is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [automerger/YamShadow-7B](https://huggingface.co/automerger/YamShadow-7B) * [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B) * [automerger/OgnoExperiment27-7B](https://huggingface.co/automerger/OgnoExperiment27-7B) * [Kukedlc/Jupiter-k-7B-slerp](https://huggingface.co/Kukedlc/Jupiter-k-7B-slerp) ## 🧩 Configuration ```yaml models: - model: liminerity/M7-7b # no parameters necessary for base model - model: automerger/YamShadow-7B parameters: weight: 0.3 density: 0.5 - model: mlabonne/AlphaMonarch-7B parameters: weight: 0.2 density: 0.5 - model: automerger/OgnoExperiment27-7B parameters: weight: 0.2 density: 0.5 - model: Kukedlc/Jupiter-k-7B-slerp parameters: weight: 0.3 density: 0.5 merge_method: dare_ties base_model: liminerity/M7-7b parameters: int8_mask: true normalize: true dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Kukedlc/NeuralShiva-7B-DT" 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"]) ```