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Adding Evaluation Results (#2)
Browse files- Adding Evaluation Results (7f4576115c2b76c2a3d4755d535e118d3463c204)
- Update README.md (eab8f3ac7fe5145e63e872d6377fe4106c8debb1)
Co-authored-by: Open LLM Leaderboard PR Bot <[email protected]>
README.md
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---
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license: apache-2.0
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base_model: Felladrin/Minueza-32M-Base
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pipeline_tag: text-generation
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language:
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- en
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datasets:
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- databricks/databricks-dolly-15k
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- Felladrin/ChatML-databricks-dolly-15k
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- euclaise/reddit-instruct-curated
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- Felladrin/ChatML-reddit-instruct-curated
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- THUDM/webglm-qa
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- Felladrin/ChatML-WebGLM-QA
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- starfishmedical/webGPT_x_dolly
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- Felladrin/ChatML-webGPT_x_dolly
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- LDJnr/Capybara
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- Felladrin/ChatML-Capybara
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- Open-Orca/SlimOrca-Dedup
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- Felladrin/ChatML-SlimOrca-Dedup
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- HuggingFaceH4/ultrachat_200k
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- Felladrin/ChatML-ultrachat_200k
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- nvidia/HelpSteer
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- Felladrin/ChatML-HelpSteer
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- sablo/oasst2_curated
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- Felladrin/ChatML-oasst2_curated
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- CohereForAI/aya_dataset
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- Felladrin/ChatML-aya_dataset
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- argilla/distilabel-capybara-dpo-7k-binarized
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- Felladrin/ChatML-distilabel-capybara-dpo-7k-binarized
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- argilla/distilabel-intel-orca-dpo-pairs
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- Felladrin/ChatML-distilabel-intel-orca-dpo-pairs
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- argilla/ultrafeedback-binarized-preferences
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- Felladrin/ChatML-ultrafeedback-binarized-preferences
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- sablo/oasst2_dpo_pairs_en
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- Felladrin/ChatML-oasst2_dpo_pairs_en
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- NeuralNovel/Neural-DPO
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- Felladrin/ChatML-Neural-DPO
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widget:
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inference:
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parameters:
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max_new_tokens: 250
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top_p: 0.55
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top_k: 35
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repetition_penalty: 1.176
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---
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# Minueza-32M-Chat: A chat model with 32 million parameters
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| weight_decay | 0 |
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| warmup_ratio | 0.02 |
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| beta | 0.1 |
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---
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language:
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- en
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license: apache-2.0
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datasets:
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- databricks/databricks-dolly-15k
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- Felladrin/ChatML-databricks-dolly-15k
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- euclaise/reddit-instruct-curated
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- Felladrin/ChatML-reddit-instruct-curated
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- THUDM/webglm-qa
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- Felladrin/ChatML-WebGLM-QA
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- starfishmedical/webGPT_x_dolly
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- Felladrin/ChatML-webGPT_x_dolly
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- LDJnr/Capybara
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- Felladrin/ChatML-Capybara
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- Open-Orca/SlimOrca-Dedup
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- Felladrin/ChatML-SlimOrca-Dedup
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- HuggingFaceH4/ultrachat_200k
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- Felladrin/ChatML-ultrachat_200k
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- nvidia/HelpSteer
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- Felladrin/ChatML-HelpSteer
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- sablo/oasst2_curated
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- Felladrin/ChatML-oasst2_curated
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- CohereForAI/aya_dataset
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- Felladrin/ChatML-aya_dataset
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- argilla/distilabel-capybara-dpo-7k-binarized
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- Felladrin/ChatML-distilabel-capybara-dpo-7k-binarized
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- argilla/distilabel-intel-orca-dpo-pairs
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- Felladrin/ChatML-distilabel-intel-orca-dpo-pairs
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- argilla/ultrafeedback-binarized-preferences
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- Felladrin/ChatML-ultrafeedback-binarized-preferences
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- sablo/oasst2_dpo_pairs_en
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- Felladrin/ChatML-oasst2_dpo_pairs_en
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- NeuralNovel/Neural-DPO
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- Felladrin/ChatML-Neural-DPO
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base_model: Felladrin/Minueza-32M-Base
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pipeline_tag: text-generation
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widget:
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- messages:
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- role: system
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content: You are a career counselor. The user will provide you with an individual
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looking for guidance in their professional life, and your task is to assist
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them in determining what careers they are most suited for based on their skills,
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interests, and experience. You should also conduct research into the various
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options available, explain the job market trends in different industries, and
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advice on which qualifications would be beneficial for pursuing particular fields.
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- role: user
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content: Heya!
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- role: assistant
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content: Hi! How may I help you?
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- role: user
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content: I am interested in developing a career in software engineering. What
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would you recommend me to do?
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- messages:
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- role: system
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content: You are a highly knowledgeable assistant. Help the user as much as you
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can.
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- role: user
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content: How can I become a healthier person?
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- messages:
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- role: system
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content: You are a helpful assistant who gives creative responses.
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- role: user
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content: Write the specs of a game about mages in a fantasy world.
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- messages:
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- role: system
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content: You are a helpful assistant who answers user's questions with details.
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- role: user
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content: Tell me about the pros and cons of social media.
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- messages:
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- role: system
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content: You are a helpful assistant who answers user's questions with details
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and curiosity.
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- role: user
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content: What are some potential applications for quantum computing?
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inference:
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parameters:
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max_new_tokens: 250
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top_p: 0.55
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top_k: 35
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repetition_penalty: 1.176
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model-index:
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- name: Minueza-32M-Chat
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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: 20.39
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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=Felladrin/Minueza-32M-Chat
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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: 26.54
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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=Felladrin/Minueza-32M-Chat
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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: 25.75
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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=Felladrin/Minueza-32M-Chat
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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: 47.27
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Minueza-32M-Chat
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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: 50.99
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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=Felladrin/Minueza-32M-Chat
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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: 0.0
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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=Felladrin/Minueza-32M-Chat
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name: Open LLM Leaderboard
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---
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# Minueza-32M-Chat: A chat model with 32 million parameters
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| weight_decay | 0 |
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| warmup_ratio | 0.02 |
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| beta | 0.1 |
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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_Felladrin__Minueza-32M-Chat)
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |28.49|
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|AI2 Reasoning Challenge (25-Shot)|20.39|
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|HellaSwag (10-Shot) |26.54|
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|MMLU (5-Shot) |25.75|
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|TruthfulQA (0-shot) |47.27|
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|Winogrande (5-shot) |50.99|
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|GSM8k (5-shot) | 0.00|
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