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README.md
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---
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base_model:
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- Hjgugugjhuhjggg/mergekit-ties-qgcitfu
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- ValiantLabs/Llama3.2-3B-ShiningValiant2
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- CarrotAI/Llama-3.2-Rabbit-Ko-3B-Instruct
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- Atharva26/llama-3.2-3b-mathdaily-chatbot
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- bunnycore/Llama-3.2-3B-ProdigyPlusPlus
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- disi-unibo-nlp/llama3.2-3B-SFT-medqa-triples-cot
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- Hjgugugjhuhjggg/mergekit-ties-poovzrh
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- bunnycore/Llama-3.2-3B-Long-Think
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- noaebbot/llama3.2-3B-insights
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- ValiantLabs/Llama3.2-3B-Enigma
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- huihui-ai/Llama-3.2-3B-Instruct-abliterated
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- meta-llama/Llama-3.2-3B-Instruct
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- Hjgugugjhuhjggg/mergekit-ties-pghuyfi
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- Diluksha/Llama_3.2_3B_sql_finetuned_full
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- bunnycore/Llama-3.2-3B-Mix
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- Hjgugugjhuhjggg/mergekit-ties-xflmond
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- bunnycore/Llama-3.2-3B-Pure-RP
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- chuanli11/Llama-3.2-3B-Instruct-uncensored
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- EmTpro01/llama-3.2-Code-Generator
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- bunnycore/Llama-3.2-3B-Booval
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- bunnycore/Llama-3.2-3B-Prodigy
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- BrainWave-ML/llama3.2-3B-codemath-orpo
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- bunnycore/Llama-3.2-3B-TitanFusion
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- bunnycore/Llama-3.2-3B-CodeReactor
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- Hjgugugjhuhjggg/mergekit-ties-kmlzhzo
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- Hjgugugjhuhjggg/mergekit-ties-esawwda
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- bunnycore/Llama-3.2-3B-TitanFusion-v2
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- disi-unibo-nlp/llama3.2-3B-SFT-medmcqa-triples-cot
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- bunnycore/Llama-3.2-3B-Mix-Skill
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- bunnycore/Llama-3.2-3B-Sci-Think
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- AELLM/Llama-3.2-Chibi-3B
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- AcademieDuNumerique/Llama-3.2-3B-SQL-Instruct
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- roger33303/Best_Model-llama3.2-3b-Instruct-Finetune-website-QnA
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- Hjgugugjhuhjggg/mergekit-ties-dkhnzcn
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- Isotonic/reasoning-llama3.2-3b
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- meta-llama/Llama-3.2-3B
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- bunnycore/Llama-3.2-3B-Apex
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- TroyDoesAI/BlackSheep-Llama3.2-3B-Context_Obedient
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- CK0607/llama3.2-3B-CodeP
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- bunnycore/Llama-3.2-3B-Stock
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library_name: transformers
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tags:
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- mergekit
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- merge
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###
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- method: int4
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value: 100
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- layer_range: [0, 28]
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model: bunnycore/Llama-3.2-3B-Long-Think
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parameters:
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weight: 0.5
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density: 0.5
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gamma: 0.01
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normalize: true
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int8_mask: true
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random_seed: 0
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temperature: 0.5
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top_p: 0.65
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inference: true
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max_tokens: 999999999
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stream: true
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quantization:
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- method: int8
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value: 100
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- method: int4
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value: 100
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- layer_range: [0, 28]
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model: bunnycore/Llama-3.2-3B-Pure-RP
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parameters:
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weight: 0.5
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density: 0.5
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gamma: 0.01
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normalize: true
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int8_mask: true
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random_seed: 0
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temperature: 0.5
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top_p: 0.65
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inference: true
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max_tokens: 999999999
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stream: true
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quantization:
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- method: int8
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value: 100
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- method: int4
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value: 100
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- layer_range: [0, 28]
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model: bunnycore/Llama-3.2-3B-Apex
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parameters:
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weight: 0.5
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density: 0.5
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gamma: 0.01
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normalize: true
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int8_mask: true
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random_seed: 0
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temperature: 0.5
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top_p: 0.65
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inference: true
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max_tokens: 999999999
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stream: true
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quantization:
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- method: int8
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value: 100
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- method: int4
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value: 100
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- layer_range: [0, 28]
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model: bunnycore/Llama-3.2-3B-Mix-Skill
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parameters:
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weight: 0.5
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density: 0.5
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gamma: 0.01
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normalize: true
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int8_mask: true
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random_seed: 0
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temperature: 0.5
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top_p: 0.65
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inference: true
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max_tokens: 999999999
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stream: true
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quantization:
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- method: int8
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value: 100
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- method: int4
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value: 100
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- layer_range: [0, 28]
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model: bunnycore/Llama-3.2-3B-Booval
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parameters:
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weight: 0.5
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density: 0.5
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gamma: 0.01
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normalize: true
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int8_mask: true
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random_seed: 0
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temperature: 0.5
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top_p: 0.65
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inference: true
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max_tokens: 999999999
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stream: true
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quantization:
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- method: int8
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value: 100
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- method: int4
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value: 100
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- layer_range: [0, 28]
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model: bunnycore/Llama-3.2-3B-ProdigyPlusPlus
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parameters:
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weight: 0.5
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density: 0.5
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gamma: 0.01
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normalize: true
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int8_mask: true
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random_seed: 0
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temperature: 0.5
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top_p: 0.65
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inference: true
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max_tokens: 999999999
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stream: true
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quantization:
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- method: int8
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value: 100
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- method: int4
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value: 100
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- layer_range: [0, 28]
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model: bunnycore/Llama-3.2-3B-Prodigy
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parameters:
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weight: 0.5
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density: 0.5
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gamma: 0.01
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normalize: true
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int8_mask: true
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random_seed: 0
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temperature: 0.5
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top_p: 0.65
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inference: true
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max_tokens: 999999999
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stream: true
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quantization:
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- method: int8
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value: 100
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- method: int4
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value: 100
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- layer_range: [0, 28]
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model: bunnycore/Llama-3.2-3B-Sci-Think
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parameters:
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weight: 0.5
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density: 0.5
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gamma: 0.01
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normalize: true
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int8_mask: true
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random_seed: 0
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temperature: 0.5
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top_p: 0.65
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inference: true
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max_tokens: 999999999
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stream: true
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quantization:
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- method: int8
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value: 100
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- method: int4
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value: 100
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- layer_range: [0, 28]
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model: bunnycore/Llama-3.2-3B-Stock
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parameters:
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weight: 0.5
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density: 0.5
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gamma: 0.01
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normalize: true
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int8_mask: true
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random_seed: 0
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temperature: 0.5
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top_p: 0.65
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inference: true
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max_tokens: 999999999
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stream: true
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quantization:
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- method: int8
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value: 100
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- method: int4
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value: 100
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- layer_range: [0, 28]
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model: chuanli11/Llama-3.2-3B-Instruct-uncensored
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parameters:
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weight: 0.5
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density: 0.5
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gamma: 0.01
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normalize: true
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int8_mask: true
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random_seed: 0
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temperature: 0.5
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top_p: 0.65
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inference: true
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max_tokens: 999999999
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stream: true
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quantization:
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- method: int8
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value: 100
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- method: int4
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value: 100
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- layer_range: [0, 28]
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model: ValiantLabs/Llama3.2-3B-Enigma
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parameters:
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weight: 0.5
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density: 0.5
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gamma: 0.01
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normalize: true
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int8_mask: true
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random_seed: 0
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temperature: 0.5
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top_p: 0.65
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inference: true
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max_tokens: 999999999
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stream: true
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quantization:
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- method: int8
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value: 100
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- method: int4
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value: 100
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- layer_range: [0, 28]
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model: CarrotAI/Llama-3.2-Rabbit-Ko-3B-Instruct
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parameters:
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weight: 0.5
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density: 0.5
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gamma: 0.01
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normalize: true
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int8_mask: true
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random_seed: 0
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temperature: 0.5
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top_p: 0.65
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inference: true
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max_tokens: 999999999
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stream: true
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quantization:
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- method: int8
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value: 100
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- method: int4
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value: 100
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- layer_range: [0, 28]
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model: AELLM/Llama-3.2-Chibi-3B
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parameters:
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weight: 0.5
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density: 0.5
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gamma: 0.01
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normalize: true
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int8_mask: true
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random_seed: 0
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temperature: 0.5
|
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top_p: 0.65
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inference: true
|
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max_tokens: 999999999
|
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stream: true
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quantization:
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- method: int8
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-
value: 100
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- method: int4
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value: 100
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- layer_range: [0, 28]
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model: EmTpro01/llama-3.2-Code-Generator
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parameters:
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weight: 0.5
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density: 0.5
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gamma: 0.01
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normalize: true
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int8_mask: true
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random_seed: 0
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temperature: 0.5
|
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-
top_p: 0.65
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inference: true
|
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max_tokens: 999999999
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stream: true
|
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quantization:
|
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-
- method: int8
|
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value: 100
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- method: int4
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value: 100
|
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-
- layer_range: [0, 28]
|
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model: disi-unibo-nlp/llama3.2-3B-SFT-medmcqa-triples-cot
|
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parameters:
|
517 |
-
weight: 0.5
|
518 |
-
density: 0.5
|
519 |
-
gamma: 0.01
|
520 |
-
normalize: true
|
521 |
-
int8_mask: true
|
522 |
-
random_seed: 0
|
523 |
-
temperature: 0.5
|
524 |
-
top_p: 0.65
|
525 |
-
inference: true
|
526 |
-
max_tokens: 999999999
|
527 |
-
stream: true
|
528 |
-
quantization:
|
529 |
-
- method: int8
|
530 |
-
value: 100
|
531 |
-
- method: int4
|
532 |
-
value: 100
|
533 |
-
- layer_range: [0, 28]
|
534 |
-
model: Atharva26/llama-3.2-3b-mathdaily-chatbot
|
535 |
-
parameters:
|
536 |
-
weight: 0.5
|
537 |
-
density: 0.5
|
538 |
-
gamma: 0.01
|
539 |
-
normalize: true
|
540 |
-
int8_mask: true
|
541 |
-
random_seed: 0
|
542 |
-
temperature: 0.5
|
543 |
-
top_p: 0.65
|
544 |
-
inference: true
|
545 |
-
max_tokens: 999999999
|
546 |
-
stream: true
|
547 |
-
quantization:
|
548 |
-
- method: int8
|
549 |
-
value: 100
|
550 |
-
- method: int4
|
551 |
-
value: 100
|
552 |
-
- layer_range: [0, 28]
|
553 |
-
model: Diluksha/Llama_3.2_3B_sql_finetuned_full
|
554 |
-
parameters:
|
555 |
-
weight: 0.5
|
556 |
-
density: 0.5
|
557 |
-
gamma: 0.01
|
558 |
-
normalize: true
|
559 |
-
int8_mask: true
|
560 |
-
random_seed: 0
|
561 |
-
temperature: 0.5
|
562 |
-
top_p: 0.65
|
563 |
-
inference: true
|
564 |
-
max_tokens: 999999999
|
565 |
-
stream: true
|
566 |
-
quantization:
|
567 |
-
- method: int8
|
568 |
-
value: 100
|
569 |
-
- method: int4
|
570 |
-
value: 100
|
571 |
-
- layer_range: [0, 28]
|
572 |
-
model: bunnycore/Llama-3.2-3B-CodeReactor
|
573 |
-
parameters:
|
574 |
-
weight: 0.5
|
575 |
-
density: 0.5
|
576 |
-
gamma: 0.01
|
577 |
-
normalize: true
|
578 |
-
int8_mask: true
|
579 |
-
random_seed: 0
|
580 |
-
temperature: 0.5
|
581 |
-
top_p: 0.65
|
582 |
-
inference: true
|
583 |
-
max_tokens: 999999999
|
584 |
-
stream: true
|
585 |
-
quantization:
|
586 |
-
- method: int8
|
587 |
-
value: 100
|
588 |
-
- method: int4
|
589 |
-
value: 100
|
590 |
-
- layer_range: [0, 28]
|
591 |
-
model: AcademieDuNumerique/Llama-3.2-3B-SQL-Instruct
|
592 |
-
parameters:
|
593 |
-
weight: 0.5
|
594 |
-
density: 0.5
|
595 |
-
gamma: 0.01
|
596 |
-
normalize: true
|
597 |
-
int8_mask: true
|
598 |
-
random_seed: 0
|
599 |
-
temperature: 0.5
|
600 |
-
top_p: 0.65
|
601 |
-
inference: true
|
602 |
-
max_tokens: 999999999
|
603 |
-
stream: true
|
604 |
-
quantization:
|
605 |
-
- method: int8
|
606 |
-
value: 100
|
607 |
-
- method: int4
|
608 |
-
value: 100
|
609 |
-
- layer_range: [0, 28]
|
610 |
-
model: roger33303/Best_Model-llama3.2-3b-Instruct-Finetune-website-QnA
|
611 |
-
parameters:
|
612 |
-
weight: 0.5
|
613 |
-
density: 0.5
|
614 |
-
gamma: 0.01
|
615 |
-
normalize: true
|
616 |
-
int8_mask: true
|
617 |
-
random_seed: 0
|
618 |
-
temperature: 0.5
|
619 |
-
top_p: 0.65
|
620 |
-
inference: true
|
621 |
-
max_tokens: 999999999
|
622 |
-
stream: true
|
623 |
-
quantization:
|
624 |
-
- method: int8
|
625 |
-
value: 100
|
626 |
-
- method: int4
|
627 |
-
value: 100
|
628 |
-
- layer_range: [0, 28]
|
629 |
-
model: noaebbot/llama3.2-3B-insights
|
630 |
-
parameters:
|
631 |
-
weight: 0.5
|
632 |
-
density: 0.5
|
633 |
-
gamma: 0.01
|
634 |
-
normalize: true
|
635 |
-
int8_mask: true
|
636 |
-
random_seed: 0
|
637 |
-
temperature: 0.5
|
638 |
-
top_p: 0.65
|
639 |
-
inference: true
|
640 |
-
max_tokens: 999999999
|
641 |
-
stream: true
|
642 |
-
quantization:
|
643 |
-
- method: int8
|
644 |
-
value: 100
|
645 |
-
- method: int4
|
646 |
-
value: 100
|
647 |
-
- layer_range: [0, 28]
|
648 |
-
model: bunnycore/Llama-3.2-3B-TitanFusion-v2
|
649 |
-
parameters:
|
650 |
-
weight: 0.5
|
651 |
-
density: 0.5
|
652 |
-
gamma: 0.01
|
653 |
-
normalize: true
|
654 |
-
int8_mask: true
|
655 |
-
random_seed: 0
|
656 |
-
temperature: 0.5
|
657 |
-
top_p: 0.65
|
658 |
-
inference: true
|
659 |
-
max_tokens: 999999999
|
660 |
-
stream: true
|
661 |
-
quantization:
|
662 |
-
- method: int8
|
663 |
-
value: 100
|
664 |
-
- method: int4
|
665 |
-
value: 100
|
666 |
-
- layer_range: [0, 28]
|
667 |
-
model: bunnycore/Llama-3.2-3B-TitanFusion
|
668 |
-
parameters:
|
669 |
-
weight: 0.5
|
670 |
-
density: 0.5
|
671 |
-
gamma: 0.01
|
672 |
-
normalize: true
|
673 |
-
int8_mask: true
|
674 |
-
random_seed: 0
|
675 |
-
temperature: 0.5
|
676 |
-
top_p: 0.65
|
677 |
-
inference: true
|
678 |
-
max_tokens: 999999999
|
679 |
-
stream: true
|
680 |
-
quantization:
|
681 |
-
- method: int8
|
682 |
-
value: 100
|
683 |
-
- method: int4
|
684 |
-
value: 100
|
685 |
-
- layer_range: [0, 28]
|
686 |
-
model: bunnycore/Llama-3.2-3B-Mix
|
687 |
-
parameters:
|
688 |
-
weight: 0.5
|
689 |
-
density: 0.5
|
690 |
-
gamma: 0.01
|
691 |
-
normalize: true
|
692 |
-
int8_mask: true
|
693 |
-
random_seed: 0
|
694 |
-
temperature: 0.5
|
695 |
-
top_p: 0.65
|
696 |
-
inference: true
|
697 |
-
max_tokens: 999999999
|
698 |
-
stream: true
|
699 |
-
quantization:
|
700 |
-
- method: int8
|
701 |
-
value: 100
|
702 |
-
- method: int4
|
703 |
-
value: 100
|
704 |
-
- layer_range: [0, 28]
|
705 |
-
model: ValiantLabs/Llama3.2-3B-ShiningValiant2
|
706 |
-
parameters:
|
707 |
-
weight: 0.5
|
708 |
-
density: 0.5
|
709 |
-
gamma: 0.01
|
710 |
-
normalize: true
|
711 |
-
int8_mask: true
|
712 |
-
random_seed: 0
|
713 |
-
temperature: 0.5
|
714 |
-
top_p: 0.65
|
715 |
-
inference: true
|
716 |
-
max_tokens: 999999999
|
717 |
-
stream: true
|
718 |
-
quantization:
|
719 |
-
- method: int8
|
720 |
-
value: 100
|
721 |
-
- method: int4
|
722 |
-
value: 100
|
723 |
-
- layer_range: [0, 28]
|
724 |
-
model: TroyDoesAI/BlackSheep-Llama3.2-3B-Context_Obedient
|
725 |
-
parameters:
|
726 |
-
weight: 0.5
|
727 |
-
density: 0.5
|
728 |
-
gamma: 0.01
|
729 |
-
normalize: true
|
730 |
-
int8_mask: true
|
731 |
-
random_seed: 0
|
732 |
-
temperature: 0.5
|
733 |
-
top_p: 0.65
|
734 |
-
inference: true
|
735 |
-
max_tokens: 999999999
|
736 |
-
stream: true
|
737 |
-
quantization:
|
738 |
-
- method: int8
|
739 |
-
value: 100
|
740 |
-
- method: int4
|
741 |
-
value: 100
|
742 |
-
- layer_range: [0, 28]
|
743 |
-
model: BrainWave-ML/llama3.2-3B-codemath-orpo
|
744 |
-
parameters:
|
745 |
-
weight: 0.5
|
746 |
-
density: 0.5
|
747 |
-
gamma: 0.01
|
748 |
-
normalize: true
|
749 |
-
int8_mask: true
|
750 |
-
random_seed: 0
|
751 |
-
temperature: 0.5
|
752 |
-
top_p: 0.65
|
753 |
-
inference: true
|
754 |
-
max_tokens: 999999999
|
755 |
-
stream: true
|
756 |
-
quantization:
|
757 |
-
- method: int8
|
758 |
-
value: 100
|
759 |
-
- method: int4
|
760 |
-
value: 100
|
761 |
-
- layer_range: [0, 28]
|
762 |
-
model: CK0607/llama3.2-3B-CodeP
|
763 |
-
parameters:
|
764 |
-
weight: 0.5
|
765 |
-
density: 0.5
|
766 |
-
gamma: 0.01
|
767 |
-
normalize: true
|
768 |
-
int8_mask: true
|
769 |
-
random_seed: 0
|
770 |
-
temperature: 0.5
|
771 |
-
top_p: 0.65
|
772 |
-
inference: true
|
773 |
-
max_tokens: 999999999
|
774 |
-
stream: true
|
775 |
-
quantization:
|
776 |
-
- method: int8
|
777 |
-
value: 100
|
778 |
-
- method: int4
|
779 |
-
value: 100
|
780 |
-
- layer_range: [0, 28]
|
781 |
-
model: disi-unibo-nlp/llama3.2-3B-SFT-medqa-triples-cot
|
782 |
-
parameters:
|
783 |
-
weight: 0.5
|
784 |
-
density: 0.5
|
785 |
-
gamma: 0.01
|
786 |
-
normalize: true
|
787 |
-
int8_mask: true
|
788 |
-
random_seed: 0
|
789 |
-
temperature: 0.5
|
790 |
-
top_p: 0.65
|
791 |
-
inference: true
|
792 |
-
max_tokens: 999999999
|
793 |
-
stream: true
|
794 |
-
quantization:
|
795 |
-
- method: int8
|
796 |
-
value: 100
|
797 |
-
- method: int4
|
798 |
-
value: 100
|
799 |
-
- layer_range: [0, 28]
|
800 |
-
model: Isotonic/reasoning-llama3.2-3b
|
801 |
-
parameters:
|
802 |
-
weight: 0.5
|
803 |
-
density: 0.5
|
804 |
-
gamma: 0.01
|
805 |
-
normalize: true
|
806 |
-
int8_mask: true
|
807 |
-
random_seed: 0
|
808 |
-
temperature: 0.5
|
809 |
-
top_p: 0.65
|
810 |
-
inference: true
|
811 |
-
max_tokens: 999999999
|
812 |
-
stream: true
|
813 |
-
quantization:
|
814 |
-
- method: int8
|
815 |
-
value: 100
|
816 |
-
- method: int4
|
817 |
-
value: 100
|
818 |
-
- layer_range: [0, 28]
|
819 |
-
model: meta-llama/Llama-3.2-3B-Instruct
|
820 |
-
parameters:
|
821 |
-
weight: 0.5
|
822 |
-
density: 0.5
|
823 |
-
gamma: 0.01
|
824 |
-
normalize: true
|
825 |
-
int8_mask: true
|
826 |
-
random_seed: 0
|
827 |
-
temperature: 0.5
|
828 |
-
top_p: 0.65
|
829 |
-
inference: true
|
830 |
-
max_tokens: 999999999
|
831 |
-
stream: true
|
832 |
-
quantization:
|
833 |
-
- method: int8
|
834 |
-
value: 100
|
835 |
-
- method: int4
|
836 |
-
value: 100
|
837 |
-
- layer_range: [0, 28]
|
838 |
-
model: meta-llama/Llama-3.2-3B
|
839 |
-
parameters:
|
840 |
-
weight: 0.5
|
841 |
-
density: 0.5
|
842 |
-
gamma: 0.01
|
843 |
-
normalize: true
|
844 |
-
int8_mask: true
|
845 |
-
random_seed: 0
|
846 |
-
temperature: 0.5
|
847 |
-
top_p: 0.65
|
848 |
-
inference: true
|
849 |
-
max_tokens: 999999999
|
850 |
-
stream: true
|
851 |
-
quantization:
|
852 |
-
- method: int8
|
853 |
-
value: 100
|
854 |
-
- method: int4
|
855 |
-
value: 100
|
856 |
-
|
857 |
-
merge_method: linear
|
858 |
-
base_model: huihui-ai/Llama-3.2-3B-Instruct-abliterated
|
859 |
-
weight: 1
|
860 |
-
density: 0.9
|
861 |
-
gamma: 0.01
|
862 |
-
normalize: true
|
863 |
-
int8_mask: true
|
864 |
-
random_seed: 0
|
865 |
-
temperature: 0.5
|
866 |
-
top_p: 0.65
|
867 |
-
inference: true
|
868 |
-
max_tokens: 999999999
|
869 |
-
stream: true
|
870 |
-
quantization:
|
871 |
-
- method: int8
|
872 |
-
value: 100
|
873 |
-
- method: int4
|
874 |
-
value: 100
|
875 |
-
parameters:
|
876 |
-
weight: 1
|
877 |
-
density: 0.9
|
878 |
-
gamma: 0.01
|
879 |
-
normalize: true
|
880 |
-
int8_mask: true
|
881 |
-
random_seed: 0
|
882 |
-
temperature: 0.5
|
883 |
-
top_p: 0.65
|
884 |
-
inference: true
|
885 |
-
max_tokens: 999999999
|
886 |
-
stream: true
|
887 |
-
quantization:
|
888 |
-
- method: int8
|
889 |
-
value: 100
|
890 |
-
- method: int4
|
891 |
-
value: 100
|
892 |
-
dtype: float16
|
893 |
-
```
|
|
|
1 |
---
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|
2 |
library_name: transformers
|
3 |
+
tags: []
|
|
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|
|
|
4 |
---
|
5 |
+
|
6 |
+
# Model Card for Model ID
|
7 |
+
|
8 |
+
<!-- Provide a quick summary of what the model is/does. -->
|
9 |
+
|
10 |
+
|
11 |
+
|
12 |
+
## Model Details
|
13 |
+
|
14 |
+
### Model Description
|
15 |
+
|
16 |
+
<!-- Provide a longer summary of what this model is. -->
|
17 |
+
|
18 |
+
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
|
19 |
+
|
20 |
+
- **Developed by:** [More Information Needed]
|
21 |
+
- **Funded by [optional]:** [More Information Needed]
|
22 |
+
- **Shared by [optional]:** [More Information Needed]
|
23 |
+
- **Model type:** [More Information Needed]
|
24 |
+
- **Language(s) (NLP):** [More Information Needed]
|
25 |
+
- **License:** [More Information Needed]
|
26 |
+
- **Finetuned from model [optional]:** [More Information Needed]
|
27 |
+
|
28 |
+
### Model Sources [optional]
|
29 |
+
|
30 |
+
<!-- Provide the basic links for the model. -->
|
31 |
+
|
32 |
+
- **Repository:** [More Information Needed]
|
33 |
+
- **Paper [optional]:** [More Information Needed]
|
34 |
+
- **Demo [optional]:** [More Information Needed]
|
35 |
+
|
36 |
+
## Uses
|
37 |
+
|
38 |
+
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
|
39 |
+
|
40 |
+
### Direct Use
|
41 |
+
|
42 |
+
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
|
43 |
+
|
44 |
+
[More Information Needed]
|
45 |
+
|
46 |
+
### Downstream Use [optional]
|
47 |
+
|
48 |
+
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
|
49 |
+
|
50 |
+
[More Information Needed]
|
51 |
+
|
52 |
+
### Out-of-Scope Use
|
53 |
+
|
54 |
+
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
|
55 |
+
|
56 |
+
[More Information Needed]
|
57 |
+
|
58 |
+
## Bias, Risks, and Limitations
|
59 |
+
|
60 |
+
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
|
61 |
+
|
62 |
+
[More Information Needed]
|
63 |
+
|
64 |
+
### Recommendations
|
65 |
+
|
66 |
+
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
|
67 |
+
|
68 |
+
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
|
69 |
+
|
70 |
+
## How to Get Started with the Model
|
71 |
+
|
72 |
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Use the code below to get started with the model.
|
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+
|
74 |
+
[More Information Needed]
|
75 |
+
|
76 |
+
## Training Details
|
77 |
+
|
78 |
+
### Training Data
|
79 |
+
|
80 |
+
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
|
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+
|
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+
[More Information Needed]
|
83 |
+
|
84 |
+
### Training Procedure
|
85 |
+
|
86 |
+
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
|
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+
|
88 |
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#### Preprocessing [optional]
|
89 |
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|
90 |
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[More Information Needed]
|
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|
92 |
+
|
93 |
+
#### Training Hyperparameters
|
94 |
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|
95 |
+
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
|
96 |
+
|
97 |
+
#### Speeds, Sizes, Times [optional]
|
98 |
+
|
99 |
+
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
|
100 |
+
|
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+
[More Information Needed]
|
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+
|
103 |
+
## Evaluation
|
104 |
+
|
105 |
+
<!-- This section describes the evaluation protocols and provides the results. -->
|
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|
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### Testing Data, Factors & Metrics
|
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#### Testing Data
|
110 |
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|
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+
<!-- This should link to a Dataset Card if possible. -->
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|
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[More Information Needed]
|
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|
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#### Factors
|
116 |
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|
117 |
+
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
|
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+
|
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[More Information Needed]
|
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+
|
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+
#### Metrics
|
122 |
+
|
123 |
+
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
|
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|
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[More Information Needed]
|
126 |
+
|
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+
### Results
|
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|
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[More Information Needed]
|
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|
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#### Summary
|
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|
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+
|
135 |
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## Model Examination [optional]
|
136 |
+
|
137 |
+
<!-- Relevant interpretability work for the model goes here -->
|
138 |
+
|
139 |
+
[More Information Needed]
|
140 |
+
|
141 |
+
## Environmental Impact
|
142 |
+
|
143 |
+
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
|
144 |
+
|
145 |
+
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
|
146 |
+
|
147 |
+
- **Hardware Type:** [More Information Needed]
|
148 |
+
- **Hours used:** [More Information Needed]
|
149 |
+
- **Cloud Provider:** [More Information Needed]
|
150 |
+
- **Compute Region:** [More Information Needed]
|
151 |
+
- **Carbon Emitted:** [More Information Needed]
|
152 |
+
|
153 |
+
## Technical Specifications [optional]
|
154 |
+
|
155 |
+
### Model Architecture and Objective
|
156 |
+
|
157 |
+
[More Information Needed]
|
158 |
+
|
159 |
+
### Compute Infrastructure
|
160 |
+
|
161 |
+
[More Information Needed]
|
162 |
+
|
163 |
+
#### Hardware
|
164 |
+
|
165 |
+
[More Information Needed]
|
166 |
+
|
167 |
+
#### Software
|
168 |
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|
169 |
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[More Information Needed]
|
170 |
+
|
171 |
+
## Citation [optional]
|
172 |
+
|
173 |
+
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
|
174 |
+
|
175 |
+
**BibTeX:**
|
176 |
+
|
177 |
+
[More Information Needed]
|
178 |
+
|
179 |
+
**APA:**
|
180 |
+
|
181 |
+
[More Information Needed]
|
182 |
+
|
183 |
+
## Glossary [optional]
|
184 |
+
|
185 |
+
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
|
186 |
+
|
187 |
+
[More Information Needed]
|
188 |
+
|
189 |
+
## More Information [optional]
|
190 |
+
|
191 |
+
[More Information Needed]
|
192 |
+
|
193 |
+
## Model Card Authors [optional]
|
194 |
+
|
195 |
+
[More Information Needed]
|
196 |
+
|
197 |
+
## Model Card Contact
|
198 |
+
|
199 |
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[More Information Needed]
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