--- tags: - merge - mergekit - louisbrulenaudet/Pearl-7B-slerp - WizardLM/WizardMath-7B-V1.1 - cognitivecomputations/WestLake-7B-v2-laser - CultriX/NeuralTrix-7B-dpo - chemistry - biology - math base_model: - louisbrulenaudet/Pearl-7B-slerp - WizardLM/WizardMath-7B-V1.1 - cognitivecomputations/WestLake-7B-v2-laser - CultriX/NeuralTrix-7B-dpo license: apache-2.0 language: - en library_name: transformers pipeline_tag: text-generation model-index: - name: Pearl-7B-0211-ties results: - task: type: text-generation metrics: - name: Average type: Average value: 75.11 - name: ARC type: ARC value: 71.42 - name: GSM8K type: GSM8K value: 70.66 - name: Winogrande type: Winogrande value: 84.37 - name: TruthfulQA type: TruthfulQA value: 71.46 - name: HellaSwag type: HellaSwag value: 88.86 source: name: Open LLM Leaderboard url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard ---
# Pearl-7B-0211-ties, an xtraordinary 7B model **03-22-2024 - To date, louisbrulenaudet/Pearl-34B-ties is the "Best 🤝 base merges and moerges model of around 30B" on the Open LLM Leaderboard.** Pearl-7B-0211-ties is a merge of the following models: * [louisbrulenaudet/Pearl-7B-slerp](https://huggingface.co./louisbrulenaudet/Pearl-7B-slerp) * [WizardLM/WizardMath-7B-V1.1](https://huggingface.co./WizardLM/WizardMath-7B-V1.1) * [cognitivecomputations/WestLake-7B-v2-laser](https://huggingface.co./cognitivecomputations/WestLake-7B-v2-laser) * [CultriX/NeuralTrix-7B-dpo](https://huggingface.co./CultriX/NeuralTrix-7B-dpo) ## Evaluation The evaluation was performed using the HuggingFace Open LLM Leaderboard. | Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K | #Params (B) | |--------------------------------------------------|---------|-------|-----------|-------|------------|------------|-------|--------------| | **louisbrulenaudet/Pearl-34B-ties** | **75.48** | 70.99 | 84.83 | **76.63** | 70.32 | 82.64 | 67.48 | 34.39 | | **louisbrulenaudet/Pearl-7B-0211-ties** | **75.11** | **71.42** | **88.86** | 63.91 | **71.46** | **84.37** | 70.66 | 7.24 | | NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO | 73.35 | 71.08 | 87.29 | 72.17 | 54.83 | 83.11 | 71.65 | 46.7 | | argilla/notus-8x7b-experiment | 73.18 | 70.99 | 87.73 | 71.33 | 65.79 | 81.61 | 61.64 | 46.7 | | **louisbrulenaudet/Pearl-7B-slerp** | 72.75 | 68.00 | 87.16 | 64.04 | 62.35 | 81.29 | **73.62** | 7.24 | | mistralai/Mixtral-8x7B-Instruct-v0.1 | 72.7 | 70.14 | 87.55 | 71.4 | 64.98 | 81.06 | 61.11 | 46.7 | | microsoft/Orca-2-13b | 61.98 | 60.92 | 79.85 | 60.3 | 56.42 | 76.56 | 37.83 | 13 | | microsoft/phi-2 | 61.33 | 61.09 | 75.11 | 58.11 | 44.47 | 74.35 | 54.81 | 2.78 | ### Ties merging TIES-Merging is a method designed to facilitate the efficient merging of multiple task-specific models into a consolidated multitask model. It addresses two primary challenges encountered in the process of model merging with a focus on maintaining objectivity. One key challenge tackled by TIES-Merging involves addressing redundancy in model parameters. This is achieved by identifying and eliminating redundant parameters within task-specific models, emphasizing the changes made during fine-tuning and selectively retaining the top-k% most significant changes while discarding the rest. Another challenge pertains to conflicts arising from disagreements between parameter signs across different models. TIES-Merging resolves these conflicts by creating a unified sign vector representing the most dominant direction of change across all models. The TIES-Merging process consists of three steps: - Trim: Reduces redundancy in task-specific models by retaining a fraction of the most significant parameters (density parameter) and resetting the remaining parameters to zero. - Elect Sign: Resolves sign conflicts across different models by creating a unified sign vector based on the most dominant direction (positive or negative) in terms of cumulative magnitude. - Disjoint Merge: Averages parameter values aligned with the unified sign vector, excluding zero values. ## Configuration ```yaml models: - model: OpenPipe/mistral-ft-optimized-1227 - model: louisbrulenaudet/Pearl-7B-slerp parameters: density: 0.6 weight: 0.3 - model: WizardLM/WizardMath-7B-V1.1 parameters: density: 0.55 weight: 0.2 - model: cognitivecomputations/WestLake-7B-v2-laser parameters: density: 0.55 weight: 0.25 - model: CultriX/NeuralTrix-7B-dpo parameters: density: 0.6 weight: 0.25 merge_method: ties base_model: OpenPipe/mistral-ft-optimized-1227 parameters: normalize: true int8_mask: true dtype: float16 ``` ## Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "louisbrulenaudet/Pearl-7B-0211-ties" 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"]) ``` ## Citing & Authors If you use this code in your research, please use the following BibTeX entry. ```BibTeX @misc{louisbrulenaudet2023, author = {Louis Brulé Naudet}, title = {Pearl-7B-0211-ties, an xtraordinary 7B model}, year = {2023} howpublished = {\url{https://huggingface.co./louisbrulenaudet/Pearl-7B-0211-ties}}, } ``` ## Feedback If you have any feedback, please reach out at [louisbrulenaudet@icloud.com](mailto:louisbrulenaudet@icloud.com).