Upload README.md

README.md

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+---
+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
+---
+<center><img src='https://i.imgur.com/0xFTuAX.png' width='450px'></center>
+
+# Pearl-7B-0211-ties, an xtraordinary 7B model
+
+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).