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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/NeuralBeagle14-7B |
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- fblgit/UNA-dolphin-2.6-mistral-7b-dpo-laser |
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base_model: |
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- mlabonne/NeuralBeagle14-7B |
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- fblgit/UNA-dolphin-2.6-mistral-7b-dpo-laser |
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model-index: |
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- name: CultriX-MoE-Model |
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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: 70.05 |
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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=CultriX/CultriX-MoE-Model |
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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: 87.22 |
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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=CultriX/CultriX-MoE-Model |
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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: 64.95 |
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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=CultriX/CultriX-MoE-Model |
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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: 68.04 |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=CultriX/CultriX-MoE-Model |
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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: 80.9 |
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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=CultriX/CultriX-MoE-Model |
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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: 62.09 |
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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=CultriX/CultriX-MoE-Model |
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name: Open LLM Leaderboard |
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--- |
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# CultriX-MoE-Model |
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CultriX-MoE-Model 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/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B) |
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* [fblgit/UNA-dolphin-2.6-mistral-7b-dpo-laser](https://huggingface.co/fblgit/UNA-dolphin-2.6-mistral-7b-dpo-laser) |
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## 🧩 Configuration |
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```yaml |
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base_model: "mlabonne/Marcoro14-7B-slerp" |
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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/NeuralBeagle14-7B" |
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positive_prompts: |
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- "Create a story based on" |
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- "Debate the topic of" |
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- "Come up with some arguments" |
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- "Provide me with instructions on" |
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- "Interpret the sentiment" |
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- "Interpret and execute these cooking instructions" |
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- "Craft a persuasive argument" |
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- "Analyze the motivations" |
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- "Construct a detailed plan for" |
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- "Narrate an event from multiple perspectives." |
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- "Formulate a response" |
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- "Write a script for a short play" |
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- "Generate a sequence of instructions to teach a skill." |
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- "Solve this riddle" |
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- "Create an engaging story" |
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- "Write a fictional" |
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- "Propose a solution to a social issue" |
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- "Develop a dialogue" |
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- "Create a step-by-step guide" |
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- "Devise a strategy" |
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- "Write a narrative" |
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- "Tell me how to" |
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- "Explain the concept of" |
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- "Give an overview of" |
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- "Compare and contrast between" |
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- "Provide information about" |
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- "Help me understand" |
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- "Summarize" |
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- "Make a recommendation on" |
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- "Answer this question" |
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- "How do you approach" |
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- "Explain the concept of" |
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- "Give an overview of" |
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- "Provide information about" |
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- "Help me understand the principles of" |
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- "Summarize the key components of" |
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- "Make a recommendation on how to" |
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- "Answer this question:" |
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negative_prompts: |
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- "Provide in-depth information about quantum computing." |
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- "Explain the inner workings of an internal combustion engine." |
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- "Give a detailed tutorial on advanced calculus." |
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- "Summarize the latest research in genetic engineering." |
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- "Interpret financial markets and stock trends." |
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- "Analyze the chemical composition of" |
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- "Develop a blueprint for." |
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- "Offer a critique of a modern art piece." |
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- "Provide a technical review of" |
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- "Conduct a linguistic analysis of an ancient language." |
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- "Write a user manual for advanced medical equipment." |
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- "Give a step-by-step guide on piloting an aircraft." |
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- "Conduct an in-depth analysis of this code" |
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- "Explain the physics behind black holes." |
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- "Provide a strategy for managing a cyber attack" |
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- "Develop an algorithm for predictive analytics in finance." |
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- "Provide information about advanced programming algorithms." |
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- "Help me understand the details of this code" |
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- "Summarize the process of cellular respiration." |
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- "Improve the security of" |
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- "What are the latest advancements in artificial intelligence?" |
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- "Provide detailed technical coding solutions." |
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- "Analyze complex scientific data and statistics." |
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- "Offer medical diagnoses based on symptoms." |
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- "Conduct a detailed financial audit of a company." |
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- "Perform real-time translation of multiple languages." |
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- "Create high-resolution graphic designs." |
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- "Develop complex mathematical proofs." |
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- "Offer legal advice on specific cases." |
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- "Write a detailed manual on advanced mechanical engineering." |
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- "Conduct an in-depth psychological assessment." |
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- "Perform a security analysis of a computer network." |
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- "Compose an original piece of music." |
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- "Plan and execute a scientific experiment." |
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- "Provide professional career counseling." |
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- "Develop a complex database management system." |
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- "Write a software program for data analysis." |
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- "Give expert advice on cyber" |
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- "Conduct a pentesting security audit" |
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- source_model: "fblgit/UNA-dolphin-2.6-mistral-7b-dpo-laser" |
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positive_prompts: |
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- "Provide step-by-step coding instructions for..." |
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- "Draft a function with detailed steps in [language]" |
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- "Guide me through coding a simple [type of application or script]" |
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- "Recommend best practices for code implementation in [context]" |
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- "Generate a regex pattern for extracting [specific data]" |
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- "Create a regex for matching [pattern]" |
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- "Explain the purpose of this regex pattern" |
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- "Compose regex for [specific use case]" |
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- "Annotate this code with detailed comments for each line" |
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- "Add explanatory comments to this script" |
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- "Comment on each part of this code for clarity" |
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- "Develop a script to [accomplish task]" |
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- "Design a database schema for [specific use case]" |
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- "Outline secure methods for [specific operation]" |
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- "Guide on optimizing [specific aspect] in this code" |
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- "Refactor this code for better readability and efficiency" |
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- "Compare and contrast these code snippets" |
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- "Identify the programming language of this snippet" |
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- "Demonstrate the usage of [specific tool/library/API]" |
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- "Show implementation steps for this [feature/concept]" |
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- "Teach how to use [specific tool/library/framework]" |
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- "Generate a README file for this project" |
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- "Create a manual page for [specific tool/command]" |
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- "Produce comprehensive documentation for this code" |
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- "Build detailed documentation for [specific module]" |
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- "Explain the underlying concept of this code snippet" |
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- "Propose enhancements for this script" |
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- "Suggest improvements for this API call integration" |
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- "Diagnose and solve this coding issue" |
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- "Demonstrate robust error handling in this code" |
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- "Debug and resolve issues in this script" |
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- "Design a user-friendly GUI for this script's functionality" |
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- "Detail the deployment process for this application" |
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- "Deploy an app designed to [perform function]" |
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- "Set up a web service for [specific purpose]" |
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- "Develop a website with [specific features]" |
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- "Craft a webpage showcasing [specific content]" |
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- "Illustrate data flow in this code architecture" |
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- "Convert this code from [language A] to [language B]" |
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- "Translate this script into [different programming language]" |
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- "Explain resource management techniques in [context]" |
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- "Build a basic API endpoint for [functionality]" |
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- "Strategies to enhance scalability in [context]" |
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- "Conduct a security review for this code" |
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- "Enhance security measures in [application/module]" |
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- "Set up a development environment for [language/framework]" |
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- "Visualize data from [specific dataset]" |
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- "Generate a dataset for [specific use case]" |
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- "Scripting guide for automating [task/process]" |
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- "Utilize this code for [specific purpose]" |
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- "Principles of object-oriented programming in [language]" |
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- "Create a mobile-responsive layout for this web app" |
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- "Explain the debugging process for this code" |
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- "Compose code to accomplish [task]" |
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- "Guidance on writing code for [specific purpose]" |
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- "I need a script for [specific function]" |
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- "Clarify the functionality of this code" |
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- "What is the purpose of this code segment?" |
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- "Enhance this code for [specific improvement]" |
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- "Develop a program that [solves problem]" |
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- "Code needed for [specific task]" |
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- "Program a solution for [problem statement]" |
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- "Enhance this function's performance by..." |
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- "Refactor code for better readability in [context]" |
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- "Craft a custom function for [specific requirement]" |
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- "Reduce computational complexity in this algorithm by..." |
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- "Extend the codebase to include [new feature]" |
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- "Incorporate this API into an existing application" |
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- "Assist in troubleshooting and bug fixing for [issue]" |
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- "Review and prep this code for deployment" |
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- "Analyze error logs for potential issues in [context]" |
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- "Create unit tests for [module/component]" |
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- "Evaluate methodologies for [problem-solving]" |
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- "Research [topic] online" |
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- "Utilize the [plugin/tool] to achieve [result]" |
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- "Design an efficient search algorithm for [data type]" |
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- "Create a web crawler for [specific data extraction]" |
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- "Application of web sockets in [real-time scenario]" |
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- "Guide to integrating a third-party library in [framework]" |
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- "Best practices in API design for [application type]" |
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negative_prompts: |
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- "Provide a detailed analysis of historical events." |
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- "Give medical advice for treating a specific illness." |
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- "Write a comprehensive review of a novel." |
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- "Explain legal implications of a contract." |
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- "Develop a marketing strategy for a new product." |
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- "Offer financial advice for stock investments." |
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- "Create a recipe for a gourmet dish." |
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- "Teach a foreign language lesson." |
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- "Compose a symphony or musical piece." |
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- "Provide workout plans and fitness coaching." |
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- "Conduct a psychological analysis of a character." |
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- "Write a script for a movie or play." |
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- "Design a blueprint for architectural structures." |
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- "Give a tutorial on how to paint a landscape." |
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- "Explain quantum physics theories." |
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- "Offer career counseling and resume writing tips." |
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- "Teach how to repair a car engine." |
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- "Plan a travel itinerary for a world tour." |
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- "Guide on how to grow organic vegetables." |
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- "Discuss political strategies for an election campaign." |
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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 = "CultriX/CultriX-MoE-Model" |
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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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``` |
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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_CultriX__CultriX-MoE-Model) |
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| Metric |Value| |
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|---------------------------------|----:| |
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|Avg. |72.21| |
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|AI2 Reasoning Challenge (25-Shot)|70.05| |
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|HellaSwag (10-Shot) |87.22| |
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|MMLU (5-Shot) |64.95| |
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|TruthfulQA (0-shot) |68.04| |
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|Winogrande (5-shot) |80.90| |
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|GSM8k (5-shot) |62.09| |
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