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Mixtral AI Llava 4bit by LeroyDyer

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  4bit Base model:leroydyer/spydazweb... Base model:quantized:leroydyer...   En   Endpoints compatible   Gguf   Instruct   Mistral   Pytorch   Quantized   Region:us   Sft   Sharded   Trl   Unsloth   Vision

Mixtral AI Llava 4bit Benchmarks

LLME Score: 0.21644
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

Mixtral AI Llava 4bit Parameters and Internals

Model Type 
text-generation, multimodal
Use Cases 
Areas:
research, commercial applications
Applications:
image-to-text, chatbot interaction
Additional Notes 
This model includes vision/multimodal capabilities. To use these features, specific files need to be loaded as described.
LLM NameMixtral AI Llava 4bit
Repository 🤗https://huggingface.co/LeroyDyer/Mixtral_AI_llava_4bit 
Base Model(s)  LeroyDyer/Mixtral_AI_Vision-Instruct_X   LeroyDyer/Mixtral_AI_Vision-Instruct_X
Required VRAM14.4 GB
Updated2024-11-21
MaintainerLeroyDyer
Model Typemistral
Instruction-BasedYes
Model Files  4.4 GB   4.9 GB: 1-of-3   5.0 GB: 2-of-3   4.5 GB: 3-of-3
Supported Languagesen
GGUF QuantizationYes
Quantization Type4bit|gguf
Model ArchitectureAutoModel
Licenseapache-2.0
Tokenizer ClassLlamaTokenizer
Padding Token<pad>
Mixtral AI Llava 4bit (LeroyDyer/Mixtral_AI_llava_4bit)

Best Alternatives to Mixtral AI Llava 4bit

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...ixtral 8x7B Instruct V0.1 GGUF0K / 15.6 GB44016604
Dolphin 2.5 Mixtral 8x7b GGUF0K / 15.6 GB10071295
Dolphin 2.7 Mixtral 8x7b GGUF0K / 15.6 GB11177134
Phi 3 Mini 4K Instruct GGUF0K / 1.4 GB166816
Spydaz Web AI 0K / 4.4 GB1373
...i 3 Mini 4K Instruct V0.3 GGUF0K / 1.4 GB2275
Phi 3 Mini 4K Instruct0K / 2.3 GB250
Dolphin 2 6 Phi 2 GGUF0K / 1.2 GB613770
Dre Phi Gguf0K / 2.3 GB150
...i 3 Mini 4K Instruct V0.2 GGUF0K / 1.4 GB1462
Note: green Score (e.g. "73.2") means that the model is better than LeroyDyer/Mixtral_AI_llava_4bit.

Rank the Mixtral AI Llava 4bit Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
Text Generation  
Text Summarization and Feature Extraction  
Code Generation  
Multi-Language Support and Translation  

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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241110
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