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--- |
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license: apache-2.0 |
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tags: |
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- moe |
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- frankenmoe |
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- merge |
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- mergekit |
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- lazymergekit |
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- mlabonne/AlphaMonarch-7B |
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- Syed-Hasan-8503/Tess-Coder-7B-Mistral-v1.0 |
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base_model: |
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- mlabonne/AlphaMonarch-7B |
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- Syed-Hasan-8503/Tess-Coder-7B-Mistral-v1.0 |
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language: |
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- en |
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library_name: transformers |
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model-index: |
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- name: MonarchCoder-MoE-2x7B |
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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: 70.99 |
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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=abideen/MonarchCoder-MoE-2x7B |
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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: 87.99 |
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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=abideen/MonarchCoder-MoE-2x7B |
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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: 65.11 |
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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=abideen/MonarchCoder-MoE-2x7B |
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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: 71.25 |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abideen/MonarchCoder-MoE-2x7B |
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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: 80.66 |
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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=abideen/MonarchCoder-MoE-2x7B |
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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: 69.37 |
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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=abideen/MonarchCoder-MoE-2x7B |
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name: Open LLM Leaderboard |
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--- |
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# MonarchCoder-MoE-2x7B |
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MonarchCoder-MoE-2x7B is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): |
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* [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-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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The main aim behind creating this model is to create a model that performs well in reasoning, conversation, and coding. AlphaMonarch performs amazing on reasoning and conversation tasks. Merging AlphaMonarch with a coding model yielded MonarchCoder-2x7B which performs better on OpenLLM, Nous, and HumanEval benchmark. |
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## 🏆 Evaluation results |
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``` |
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| Metric |MonarchCoder-Moe-2x7B||MonarchCoder-7B||AlphaMonarch| |
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|---------------------------------|---------------------|-----------------|------------| |
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|Avg. | 74.23 | 71.17 | 75.99 | |
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|HumanEval | 41.15 | 39.02 | 34.14 | |
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|HumanEval+ | 29.87 | 31.70 | 29.26 | |
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|MBPP | 40.60 | * | * | |
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|AI2 Reasoning Challenge (25-Shot)| 70.99 | 68.52 | 73.04 | |
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|HellaSwag (10-Shot) | 87.99 | 87.30 | 89.18 | |
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|MMLU (5-Shot) | 65.11 | 64.65 | 64.40 | |
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|TruthfulQA (0-shot) | 71.25 | 61.21 | 77.91 | |
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|Winogrande (5-shot) | 80.66 | 80.19 .| 84.69 | |
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|GSM8k (5-shot) . | 69.37 | 65.13 | 66.72 | |
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``` |
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## 🧩 Configuration |
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```yaml |
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base_model: paulml/OGNO-7B |
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gate_mode: hidden |
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dtype: bfloat16 |
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experts: |
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- source_model: mlabonne/AlphaMonarch-7B |
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positive_prompts: |
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- "Mathematics" |
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- "Logical Reasoning" |
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- "Intelligent Conversations" |
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- "Thoughtful Analysis" |
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- "Biology" |
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- "Medicine" |
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- "Problem-solving Dialogue" |
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- "Physics" |
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- "Emotional intelligence" |
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negative_prompts: |
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- "History" |
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- "Philosophy" |
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- "Linguistics" |
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- "Literature" |
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- "Art and Art History" |
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- "Music Theory and Composition" |
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- "Performing Arts (Theater, Dance)" |
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- source_model: Syed-Hasan-8503/Tess-Coder-7B-Mistral-v1.0 |
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positive_prompts: |
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- "Coding" |
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- "Algorithm Design" |
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- "Problem Solving" |
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- "Software Development" |
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- "Computer" |
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- "Code Refactoring" |
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- "Web development" |
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- "Machine learning" |
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negative_prompts: |
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- "Education" |
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- "Law" |
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- "Theology and Religious Studies" |
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- "Communication Studies" |
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- "Business and Management" |
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- "Agricultural Sciences" |
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- "Nutrition and Food Science" |
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- "Sports Science" |
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``` |
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## 💻 Usage |
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```python |
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!pip install -qU transformers bitsandbytes 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 = "abideen/MonarchCoder-MoE-2x7B" |
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tokenizer = AutoTokenizer.from_pretrained(model) |
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pipeline = transformers.pipeline( |
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"text-generation", |
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model=model, |
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model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True}, |
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) |
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messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}] |
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prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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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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``` |
