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Model: LeroyDyer/Mixtral_AI_Cyber_Matrix_2_0 Source: Original Platform
2026-05-04 01:33:41 +08:00
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--- a/README.md
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+---
+base_model:
+- LeroyDyer/Mixtral_AI_Multi_TEST
+- LeroyDyer/Mixtral_AI_Cyber_Dolphin_2.0
+- LeroyDyer/Mixtral_AI_CyberLAW
+- LeroyDyer/Mixtral_AI_CyberBrain_3_0
+- LeroyDyer/Mixtral_AI_Cyber_5.0
+- LeroyDyer/Mixtral_AI_CyberBrain_2.0
+- ezelikman/quietstar-8-ahead
+- KoboldAI/Mistral-7B-Erebus-v3
+library_name: transformers
+tags:
+- mergekit
+- megamerge
+- code
+- Cyber-Series
+license: mit
+language:
+- en
+datasets:
+- Open-Orca/OpenOrca
+- cognitivecomputations/dolphin
+- WhiteRabbitNeo/WRN-Chapter-2
+- WhiteRabbitNeo/WRN-Chapter-1
+- gate369/Alpaca-Star
+- gate369/alpaca-star-ascii
+---
+
+<img src="https://cdn-avatars.huggingface.co/v1/production/uploads/65d883893a52cd9bcd8ab7cf/tRsCJlHNZo1D02kBTmfy9.jpeg" width="300"/>
+https://github.com/spydaz
+
+Currently undegoing Fine tuning ! as this model contains all Previous models !
+
+
+This model contains many hidden tensors : 
+As it was emrged with many lora adapter for various task such as vision and sound . 
+The problem was that for some reason i could not get the extra heads to show up like other models.
+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 !
+so hidden talents: 
+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!
+
+
+THIS VERSION HAS BEEN UPDATED TO INCLUDE CYBERBRAIN ! (Hidden Tensors)
+
+## Extended capabilities:
+  * mistralai/Mistral-7B-Instruct-v0.1 - Prime-Base
+
+  * ChaoticNeutrals/Eris-LelantaclesV2-7b - role play
+ 
+  * ChaoticNeutrals/Eris_PrimeV3-Vision-7B - vision
+
+  * rvv-karma/BASH-Coder-Mistral-7B - coding
+
+  * Locutusque/Hercules-3.1-Mistral-7B - Unhinging
+
+  * KoboldAI/Mistral-7B-Erebus-v3 - NSFW
+
+  * Locutusque/Hyperion-2.1-Mistral-7B - CHAT
+
+  * Severian/Nexus-IKM-Mistral-7B-Pytorch - Thinking
+
+  * NousResearch/Hermes-2-Pro-Mistral-7B - Generalizing
+ 
+  * mistralai/Mistral-7B-Instruct-v0.2 - BASE
+
+  * Nitral-AI/ProdigyXBioMistral_7B - medical
+
+  * Nitral-AI/Infinite-Mika-7b - 128k - Context Expansion enforcement
+
+  * Nous-Yarn-Mistral-7b-128k - 128k - Context Expansion
+ 
+  * yanismiraoui/Yarn-Mistral-7b-128k-sharded
+
+  * ChaoticNeutrals/Eris_Prime-V2-7B - Roleplay
+
+
+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.
+
+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.
+
+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.
+
+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.
+
+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.
+
+Experience the future of language models with the MEGA_MIND 24b CyberSeries, where innovation meets performance, and possibilities are limitless.
+### Models Merged
+
+The following models were included in the merge:
+* [LeroyDyer/Mixtral_AI_Multi_TEST](https://huggingface.co/LeroyDyer/Mixtral_AI_Multi_TEST)
+* [LeroyDyer/Mixtral_AI_CyberLAW](https://huggingface.co/LeroyDyer/Mixtral_AI_CyberLAW)
+* [LeroyDyer/Mixtral_AI_CyberBrain_3_0](https://huggingface.co/LeroyDyer/Mixtral_AI_CyberBrain_3_0)
+* [LeroyDyer/Mixtral_AI_Cyber_5.0](https://huggingface.co/LeroyDyer/Mixtral_AI_Cyber_5.0)
+
+### Configuration
+
+The following YAML configuration was used to produce this model:
+
+```yaml
+
+models:
+  - model: LeroyDyer/Mixtral_AI_Cyber_Dolphin_2.0
+    parameters:
+      density: [0.256, 0.512, 0.128] # density gradient
+      weight: 0.382
+  - model: LeroyDyer/Mixtral_AI_CyberLAW
+    parameters:
+      density: 0.382
+      weight: [0.256, 0.128, 0.256, 0.128] # weight gradient
+  - model: LeroyDyer/Mixtral_AI_CyberBrain_3_0
+    parameters:
+      density: 0.382
+      weight: [0.128, 0.512, 0.128, 0.128] # weight gradient
+  - model: LeroyDyer/Mixtral_AI_Multi_TEST
+    parameters:
+      density: 0.382
+      weight: [0.128, 0.512, 0.128, 0.128] # weight gradient
+  - model: LeroyDyer/Mixtral_AI_Cyber_5.0
+    parameters:
+      density: 0.382
+      weight:
+        - filter: mlp
+          value: 0.5
+        - value: 0
+merge_method: ties
+base_model:  LeroyDyer/Mixtral_AI_Cyber_Dolphin_2.0
+parameters:
+  normalize: true
+  int8_mask: true
+dtype: float16
+
+```
--- a/config.json
+++ b/config.json
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+{
+  "_name_or_path": "LeroyDyer/Mixtral_AI_Cyber_Matrix_2_0",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pad_token_id": 2,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.38.2",
+  "unsloth_version": "2024.3",
+  "use_cache": true,
+  "vocab_size": 32000
+}
--- a/generation_config.json
+++ b/generation_config.json
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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 2,
+  "transformers_version": "4.38.2"
+}
--- a/mergekit_config.yml
+++ b/mergekit_config.yml
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+
+models:
+  - model: LeroyDyer/Mixtral_AI_Cyber_Matrix
+    parameters:
+      weight: 0.512
+  - model: LeroyDyer/Mixtral_AI_CyberBrain_3_0
+    parameters:
+      weight: 0.512
+merge_method: linear
+dtype: float16
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+{
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+    "lstrip": false,
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+}
--- a/tokenizer.json
+++ b/tokenizer.json
--- a/tokenizer.model
+++ b/tokenizer.model
--- a/tokenizer_config.json
+++ b/tokenizer_config.json
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+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "0": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+  },
+  "additional_special_tokens": [],
+  "bos_token": "<s>",
+  "chat_template": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": true,
+  "model_max_length": 32768,
+  "pad_token": "<unk>",
+  "padding_side": "right",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}