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--- |
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base_model: |
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- LeroyDyer/Mixtral_AI_Multi_TEST |
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- LeroyDyer/Mixtral_AI_Cyber_Dolphin_2.0 |
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- LeroyDyer/Mixtral_AI_CyberLAW |
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- LeroyDyer/Mixtral_AI_CyberBrain_3_0 |
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- LeroyDyer/Mixtral_AI_Cyber_5.0 |
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- LeroyDyer/Mixtral_AI_CyberBrain_2.0 |
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- ezelikman/quietstar-8-ahead |
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- KoboldAI/Mistral-7B-Erebus-v3 |
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library_name: transformers |
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tags: |
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- mergekit |
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- megamerge |
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- code |
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- Cyber-Series |
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license: mit |
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language: |
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- en |
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datasets: |
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- Open-Orca/OpenOrca |
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- cognitivecomputations/dolphin |
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- WhiteRabbitNeo/WRN-Chapter-2 |
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- WhiteRabbitNeo/WRN-Chapter-1 |
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- gate369/Alpaca-Star |
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- gate369/alpaca-star-ascii |
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--- |
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<img src="https://cdn-avatars.huggingface.co/v1/production/uploads/65d883893a52cd9bcd8ab7cf/tRsCJlHNZo1D02kBTmfy9.jpeg" width="300"/> |
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https://github.com/spydaz |
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Currently undegoing Fine tuning ! as this model contains all Previous models ! |
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This model contains many hidden tensors : |
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As it was emrged with many lora adapter for various task such as vision and sound . |
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The problem was that for some reason i could not get the extra heads to show up like other models. |
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such as the llava model ... i suppose this model can change the config.json to be a llava model and yes ! it works! ie it can think and has hidden think heads ? but you need to config it up !, It has vision heads but also i could not set the config up ! |
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so hidden talents: |
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It was also merged with the mothers of these models for QUiet(thoughts) and (llava vision etc ) so the tensors are there . i just did not understand how to fine tne the addtional funcitonalitys. as they need a single trainign example to populate the hidden tensor hence te merges. and yet when the model is put in train mode , ie by setting the model after loading to model.TRAIN ... the tensors apear waiting for training so just add a peft and start the training! |
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THIS VERSION HAS BEEN UPDATED TO INCLUDE CYBERBRAIN ! (Hidden Tensors) |
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## Extended capabilities: |
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* mistralai/Mistral-7B-Instruct-v0.1 - Prime-Base |
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* ChaoticNeutrals/Eris-LelantaclesV2-7b - role play |
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* ChaoticNeutrals/Eris_PrimeV3-Vision-7B - vision |
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* rvv-karma/BASH-Coder-Mistral-7B - coding |
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* Locutusque/Hercules-3.1-Mistral-7B - Unhinging |
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* KoboldAI/Mistral-7B-Erebus-v3 - NSFW |
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* Locutusque/Hyperion-2.1-Mistral-7B - CHAT |
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* Severian/Nexus-IKM-Mistral-7B-Pytorch - Thinking |
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* NousResearch/Hermes-2-Pro-Mistral-7B - Generalizing |
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* mistralai/Mistral-7B-Instruct-v0.2 - BASE |
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* Nitral-AI/ProdigyXBioMistral_7B - medical |
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* Nitral-AI/Infinite-Mika-7b - 128k - Context Expansion enforcement |
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* Nous-Yarn-Mistral-7b-128k - 128k - Context Expansion |
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* yanismiraoui/Yarn-Mistral-7b-128k-sharded |
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* ChaoticNeutrals/Eris_Prime-V2-7B - Roleplay |
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This Expert is a companon to the MEGA_MIND 24b CyberSeries represents a groundbreaking leap in the realm of language models, integrating a diverse array of expert models into a unified framework. At its core lies the Mistral-7B-Instruct-v0.2, a refined instructional model designed for versatility and efficiency. |
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Enhanced with an expanded context window and advanced routing mechanisms, the Mistral-7B-Instruct-v0.2 exemplifies the power of Mixture of Experts, allowing seamless integration of specialized sub-models. This architecture facilitates unparalleled performance and scalability, enabling the CyberSeries to tackle a myriad of tasks with unparalleled speed and accuracy. |
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Among its illustrious sub-models, the OpenOrca - Mistral-7B-8k shines as a testament to fine-tuning excellence, boasting top-ranking performance in its class. Meanwhile, the Hermes 2 Pro introduces cutting-edge capabilities such as Function Calling and JSON Mode, catering to diverse application needs. |
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Driven by Reinforcement Learning from AI Feedback, the Starling-LM-7B-beta demonstrates remarkable adaptability and optimization, while the Phi-1.5 Transformer model stands as a beacon of excellence across various domains, from common sense reasoning to medical inference. |
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With models like BioMistral tailored specifically for medical applications and Nous-Yarn-Mistral-7b-128k excelling in handling long-context data, the MEGA_MIND 24b CyberSeries emerges as a transformative force in the landscape of language understanding and artificial intelligence. |
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Experience the future of language models with the MEGA_MIND 24b CyberSeries, where innovation meets performance, and possibilities are limitless. |
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### Models Merged |
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The following models were included in the merge: |
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* [LeroyDyer/Mixtral_AI_Multi_TEST](https://huggingface.co/LeroyDyer/Mixtral_AI_Multi_TEST) |
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* [LeroyDyer/Mixtral_AI_CyberLAW](https://huggingface.co/LeroyDyer/Mixtral_AI_CyberLAW) |
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* [LeroyDyer/Mixtral_AI_CyberBrain_3_0](https://huggingface.co/LeroyDyer/Mixtral_AI_CyberBrain_3_0) |
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* [LeroyDyer/Mixtral_AI_Cyber_5.0](https://huggingface.co/LeroyDyer/Mixtral_AI_Cyber_5.0) |
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### Configuration |
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The following YAML configuration was used to produce this model: |
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```yaml |
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models: |
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- model: LeroyDyer/Mixtral_AI_Cyber_Dolphin_2.0 |
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parameters: |
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density: [0.256, 0.512, 0.128] # density gradient |
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weight: 0.382 |
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- model: LeroyDyer/Mixtral_AI_CyberLAW |
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parameters: |
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density: 0.382 |
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weight: [0.256, 0.128, 0.256, 0.128] # weight gradient |
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- model: LeroyDyer/Mixtral_AI_CyberBrain_3_0 |
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parameters: |
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density: 0.382 |
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weight: [0.128, 0.512, 0.128, 0.128] # weight gradient |
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- model: LeroyDyer/Mixtral_AI_Multi_TEST |
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parameters: |
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density: 0.382 |
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weight: [0.128, 0.512, 0.128, 0.128] # weight gradient |
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- model: LeroyDyer/Mixtral_AI_Cyber_5.0 |
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parameters: |
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density: 0.382 |
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weight: |
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- filter: mlp |
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value: 0.5 |
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- value: 0 |
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merge_method: ties |
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base_model: LeroyDyer/Mixtral_AI_Cyber_Dolphin_2.0 |
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parameters: |
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normalize: true |
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int8_mask: true |
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dtype: float16 |
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``` |
