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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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- Gille/StrangeMerges_32-7B-slerp |
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- mlabonne/AlphaMonarch-7B |
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base_model: |
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- Gille/StrangeMerges_32-7B-slerp |
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- mlabonne/AlphaMonarch-7B |
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model-index: |
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- name: MixtureofMerges-MoE-2x7b-v7 |
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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: 73.21 |
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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=jsfs11/MixtureofMerges-MoE-2x7b-v7 |
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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: 89.05 |
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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=jsfs11/MixtureofMerges-MoE-2x7b-v7 |
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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: 64.63 |
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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=jsfs11/MixtureofMerges-MoE-2x7b-v7 |
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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: 78.34 |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-v7 |
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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: 84.93 |
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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=jsfs11/MixtureofMerges-MoE-2x7b-v7 |
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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: 69.07 |
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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=jsfs11/MixtureofMerges-MoE-2x7b-v7 |
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name: Open LLM Leaderboard |
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--- |
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# MixtureofMerges-MoE-2x7b-v7 |
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MixtureofMerges-MoE-2x7b-v7 is a Mixture of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): |
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* [Gille/StrangeMerges_32-7B-slerp](https://huggingface.co/Gille/StrangeMerges_32-7B-slerp) |
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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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base_model: Gille/StrangeMerges_32-7B-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: Gille/StrangeMerges_32-7B-slerp |
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positive_prompts: |
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- "Answer this question from the ARC (Argument Reasoning Comprehension)." |
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- "Use common sense and logical reasoning skills." |
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- "What assumptions does this argument rely on?" |
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- "Are these assumptions valid? Explain." |
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- "Analyze the logical structure of this argument. Identify the premises, conclusion, and any assumptions made" |
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- "Identify any potential counterarguments to this position. How might someone challenge the reasoning presented?" |
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- "Could this be explained in a different way? Provide an alternative explanation." |
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- "Identify any weaknesses in this argument." |
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- "Does this argument contain any logical fallacies? If so, which ones?" |
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- "Generate a few possible continuations to this scenario." |
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- "Demonstrate understanding of everyday commonsense in your response." |
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- "Use contextual clues to determine the most likely outcome." |
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- "Continue this scenario, but make the writing style sound archaic and overly formal." |
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- "This narrative is predictable. Can you introduce an unexpected yet plausible twist?" |
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- "The character is angry. Continue this scenario showcasing a furious outburst." |
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negative_prompts: |
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- "misses key evidence" |
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- "overly general" |
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- "commits the fallacy of hasty generalization" |
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- "focuses on irrelevant details" |
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- "assumes information not provided" |
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- "relies on stereotypes" |
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- "repetitive phrases" |
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- "engages in circular reasoning" |
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- "overuse of the same words" |
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- "contradicts earlier statements - breaks the internal logic of the scenario" |
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- "out of character dialogue" |
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- "awkward phrasing - sounds unnatural" |
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- "doesn't match the given genre" |
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- source_model: mlabonne/AlphaMonarch-7B |
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positive_prompts: |
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- "Answer this question, demonstrating commonsense understanding and using any relevant general knowledge you may have." |
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- "Provide a concise summary of this passage, then explain why the highlighted section is essential to the main idea." |
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- "Read these two brief articles presenting different viewpoints on the same topic. List their key arguments and highlight where they disagree." |
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- "Paraphrase this statement, changing the emotional tone but keeping the core meaning intact. Example: Rephrase a worried statement in a humorous way" |
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- "Create a short analogy that helps illustrate the main concept of this article." |
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- "Explain the concept of physics to a high school student. Use analogies and examples to clarify the main ideas." |
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- "Calculate the answer to this math problem" |
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- "My mathematical capabilities are strong, allowing me to handle complex mathematical queries" |
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- "solve for" |
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- "Analyze the given data and identify any patterns or trends. What conclusions can be drawn from this information?" |
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- "A store sells apples at $0.50 each. If Emily buys 12 apples, how much does she need to pay?" |
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- "Isolate x in the following equation: 2x + 5 = 17" |
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- "Solve this equation and show your working." |
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- "Explain why you used this formula to solve the problem." |
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- "Attempt to divide this number by zero. Explain why this cannot be done." |
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negative_prompts: |
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- "sounds too basic" |
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- "understated" |
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- "dismisses important details" |
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- "avoids the question's nuance" |
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- "skips essential steps in the solution" |
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- "takes this statement too literally" |
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- "incorrect" |
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- "inaccurate" |
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- "assumed without proof" |
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- "uses jargon without explanation" |
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- "rushed calculation" |
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- "confuses mathematical concepts" |
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- "draws illogical conclusions" |
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- "circular reasoning" |
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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 = "jsfs11/MixtureofMerges-MoE-2x7b-v7" |
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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_jsfs11__MixtureofMerges-MoE-2x7b-v7) |
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| Metric |Value| |
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|---------------------------------|----:| |
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|Avg. |76.54| |
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|AI2 Reasoning Challenge (25-Shot)|73.21| |
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|HellaSwag (10-Shot) |89.05| |
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|MMLU (5-Shot) |64.63| |
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|TruthfulQA (0-shot) |78.34| |
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|Winogrande (5-shot) |84.93| |
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|GSM8k (5-shot) |69.07| |
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