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value: 68.52
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name: normalized accuracy
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source:
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name: Open LLM Leaderboard
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type: text-generation
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value: 87.3
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name: normalized accuracy
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source:
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name: Open LLM Leaderboard
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type: text-generation
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value: 64.65
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name: accuracy
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source:
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name: Open LLM Leaderboard
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type: text-generation
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- type: mc2
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value: 61.21
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source:
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name: Open LLM Leaderboard
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type: text-generation
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value: 80.19
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name: accuracy
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source:
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name: Open LLM Leaderboard
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type: text-generation
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value: 65.13
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name: accuracy
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name: Open LLM Leaderboard
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---
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# MonarchCoder-7B
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* [Syed-Hasan-8503/Tess-Coder-7B-Mistral-v1.0](https://huggingface.co/Syed-Hasan-8503/Tess-Coder-7B-Mistral-v1.0)
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* [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B)
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## 🧩 Configuration
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```yaml
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outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
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print(outputs[0]["generated_text"])
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```
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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_abideen__MonarchCoder-7B)
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |71.17|
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|AI2 Reasoning Challenge (25-Shot)|68.52|
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|HellaSwag (10-Shot) |87.30|
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|MMLU (5-Shot) |64.65|
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|TruthfulQA (0-shot) |61.21|
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|Winogrande (5-shot) |80.19|
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|GSM8k (5-shot) |65.13|
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value: 68.52
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name: normalized accuracy
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source:
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url: >-
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abideen/MonarchCoder-7B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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value: 87.3
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name: normalized accuracy
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source:
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url: >-
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abideen/MonarchCoder-7B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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value: 64.65
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name: accuracy
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source:
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url: >-
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abideen/MonarchCoder-7B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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- type: mc2
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value: 61.21
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source:
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url: >-
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abideen/MonarchCoder-7B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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value: 80.19
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name: accuracy
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source:
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url: >-
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abideen/MonarchCoder-7B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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value: 65.13
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name: accuracy
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source:
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url: >-
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abideen/MonarchCoder-7B
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name: Open LLM Leaderboard
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language:
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- en
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library_name: transformers
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---
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# MonarchCoder-7B
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![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/64e380b2e12618b261fa6ba0/oJN8_xoMOq2RlIc799m-x.jpeg)
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MonarchCoder-7B is a slerp merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
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* [Syed-Hasan-8503/Tess-Coder-7B-Mistral-v1.0](https://huggingface.co/Syed-Hasan-8503/Tess-Coder-7B-Mistral-v1.0)
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* [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B)
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The main aim behind creating this model is to create a model that performs well in reasoning, conversation, and coding. AlphaMonarch pperforms amazing on reasoning and conversation tasks. Merging AlphaMonarch with a coding model yielded MonarchCoder-7B which performs better on OpenLLM, Nous, and HumanEval benchmark. Although [MonarchCoder-2x7B](abideen/MonarchCoder-MoE-2x7B) performs better than MonarchCoder-7B.
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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_abideen__MonarchCoder-7B)
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |71.17|
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|AI2 Reasoning Challenge (25-Shot)|68.52|
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|HellaSwag (10-Shot) |87.30|
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|MMLU (5-Shot) |64.65|
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|TruthfulQA (0-shot) |61.21|
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|Winogrande (5-shot) |80.19|
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|GSM8k (5-shot) |65.13|
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## 🧩 Configuration
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```yaml
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outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
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print(outputs[0]["generated_text"])
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```
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