--- tags: - merge - mergekit - lazymergekit - eren23/ogno-monarch-jaskier-merge-7b - liminerity/Omningotex-7b-slerp - yleo/OgnoMonarch-7B base_model: - eren23/ogno-monarch-jaskier-merge-7b --- # ramonda-monarch-7b ramonda-monarch-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [eren23/ogno-monarch-jaskier-merge-7b](https://huggingface.co/eren23/ogno-monarch-jaskier-merge-7b) * [liminerity/Omningotex-7b-slerp](https://huggingface.co/liminerity/Omningotex-7b-slerp) * [yleo/OgnoMonarch-7B](https://huggingface.co/yleo/OgnoMonarch-7B) # 🏆 Benchmarks ### Open LLM Leaderboard | Model | Average | ARC_easy | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K | ARC_challenge | |------------------------|--------:|-----:|----------:|-----:|-----------:|-----------:|------:|--------:| | mayacinka/ramonda-monarch-7b | / | 86.91 | 87.45 | 61.97 | / | 81.61 | /| 68.26 | ### MMLU | Groups |Version|Filter|n-shot|Metric|Value | |Stderr| |------------------|-------|------|------|------|-----:|---|-----:| |mmlu |N/A |none | 0|acc |0.6197|± |0.0039| | - humanities |N/A |none |None |acc |0.5762|± |0.0067| | - other |N/A |none |None |acc |0.6936|± |0.0080| | - social_sciences|N/A |none |None |acc |0.7192|± |0.0079| | - stem |N/A |none |None |acc |0.5147|± |0.0085| ## 🧩 Configuration ```yaml models: - model: bardsai/jaskier-7b-dpo-v5.6 # No parameters necessary for base model - model: eren23/ogno-monarch-jaskier-merge-7b parameters: density: 0.53 weight: 0.4 - model: liminerity/Omningotex-7b-slerp parameters: density: 0.53 weight: 0.3 - model: yleo/OgnoMonarch-7B parameters: density: 0.53 weight: 0.3 merge_method: dare_ties base_model: bardsai/jaskier-7b-dpo-v5.6 parameters: int8_mask: true dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mayacinka/ramonda-monarch-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"]) ```