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Model: paulilioaica/Hugo-7B-slerp Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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tags:
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- merge
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- mergekit
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- lazymergekit
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- mistralai/Mistral-7B-Instruct-v0.2
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- beowolx/CodeNinja-1.0-OpenChat-7B
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base_model:
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- mistralai/Mistral-7B-Instruct-v0.2
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- beowolx/CodeNinja-1.0-OpenChat-7B
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model-index:
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- name: Hugo-7B-slerp
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results:
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: AI2 Reasoning Challenge (25-Shot)
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type: ai2_arc
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config: ARC-Challenge
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split: test
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args:
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num_few_shot: 25
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metrics:
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- type: acc_norm
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value: 64.51
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=paulilioaica/Hugo-7B-slerp
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: HellaSwag (10-Shot)
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type: hellaswag
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split: validation
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args:
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num_few_shot: 10
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metrics:
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- type: acc_norm
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value: 84.77
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=paulilioaica/Hugo-7B-slerp
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MMLU (5-Shot)
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type: cais/mmlu
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config: all
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 62.54
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=paulilioaica/Hugo-7B-slerp
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: TruthfulQA (0-shot)
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type: truthful_qa
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config: multiple_choice
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split: validation
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args:
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num_few_shot: 0
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metrics:
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- type: mc2
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value: 57.13
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=paulilioaica/Hugo-7B-slerp
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: Winogrande (5-shot)
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type: winogrande
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config: winogrande_xl
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split: validation
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 80.03
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=paulilioaica/Hugo-7B-slerp
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: GSM8k (5-shot)
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type: gsm8k
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config: main
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 53.45
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=paulilioaica/Hugo-7B-slerp
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name: Open LLM Leaderboard
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---
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# Hugo-7B-slerp
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<p align="center">
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<img src="https://cdn.openart.ai/stable_diffusion/54be6f0516fee5ce9b3f8a8b68620a05059fc4cf_2000x2000.webp" alt="alt text" class="center" width="300"/>
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</p>
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Hugo-7B-slerp is a successful merge of the following models using mergekit:
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* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
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* [beowolx/CodeNinja-1.0-OpenChat-7B](https://huggingface.co/beowolx/CodeNinja-1.0-OpenChat-7B)
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## 🧩 Configuration
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```yaml
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slices:
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- sources:
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- model: mistralai/Mistral-7B-Instruct-v0.2
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layer_range: [0, 32]
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- model: beowolx/CodeNinja-1.0-OpenChat-7B
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layer_range: [0, 32]
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merge_method: slerp
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base_model: mistralai/Mistral-7B-Instruct-v0.2
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parameters:
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t:
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- filter: self_attn
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value: [0, 0.5, 0.3, 0.7, 1]
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- filter: mlp
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value: [1, 0.5, 0.7, 0.3, 0]
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- value: 0.5
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dtype: bfloat16
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```
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## 📈 Performance
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| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
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| --- | --- | --- | --- | --- | --- | --- | --- |
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| [paulilioaica/Hugo-7B-slerp](#) | **67.07** | **64.51** | 84.77 | **62.54** | 57.13 | **80.03** | 53.45 |
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| [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) | 65.71 | 63.14 | 84.88 | 60.78 | 68.26 | 77.19 | 40.03 |
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| [beowolx/CodeNinja-1.0-OpenChat-7B](https://huggingface.co/beowolx/CodeNinja-1.0-OpenChat-7B) | 67.4 | 63.48 | 83.65 | 63.77 | 47.16 | 79.79 | 66.57 |
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With bold one can see the benchmarks where this merge overtakes the basemodel in performance.
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## 💻 Usage
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```python
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!pip install -qU transformers accelerate
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from transformers import AutoTokenizer
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import transformers
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import torch
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model = "paulilioaica/Hugo-7B-slerp"
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messages = [{"role": "user", "content": "What is a large language model?"}]
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tokenizer = AutoTokenizer.from_pretrained(model)
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pipeline = transformers.pipeline(
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"conversational",
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model=model,
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torch_dtype=torch.float16,
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device_map="auto",
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)
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outputs = pipeline(messages, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
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print(outputs)
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```
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## 🛈 More on megekit
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[mergekit](https://huggingface.co/blog/mlabonne/merge-models)
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_paulilioaica__Hugo-7B-slerp)
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |67.07|
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|AI2 Reasoning Challenge (25-Shot)|64.51|
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|HellaSwag (10-Shot) |84.77|
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|MMLU (5-Shot) |62.54|
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|TruthfulQA (0-shot) |57.13|
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|Winogrande (5-shot) |80.03|
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|GSM8k (5-shot) |53.45|
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