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Llava V1.6 Mistral 7B PATCHED by Trelis

 »  All LLMs  »  Trelis  »  Llava V1.6 Mistral 7B PATCHED   URL Share it on

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Llava V1.6 Mistral 7B PATCHED is an open-source language model by Trelis. Features: 7b LLM, VRAM: 15.1GB, Context: 32K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.12.

  Conversational   Image-text-to-text   Instruct   Llava   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/Trelis/llava-v1.6-mistral-7b-PATCHED 

Llava V1.6 Mistral 7B PATCHED Benchmarks

LLME Score: 0.11998
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Llava V1.6 Mistral 7B PATCHED (Trelis/llava-v1.6-mistral-7b-PATCHED)
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Llava V1.6 Mistral 7B PATCHED Parameters and Internals

Model Type 
text generation, multimodal
Use Cases 
Primary Use Cases:
Research on large multimodal models and chatbots.
Additional Notes 
Patched version with patches from aliencaocao applied.
Training Details 
Data Sources:
558K filtered image-text pairs from LAION/CC/SBU, 158K GPT-generated multimodal instruction-following data, 500K academic-task-oriented VQA data mixture, 50K GPT-4V data mixture, 40K ShareGPT data
Model Architecture:
Transformer architecture
LLM NameLlava V1.6 Mistral 7B PATCHED
Repository 🤗https://huggingface.co/Trelis/llava-v1.6-mistral-7b-PATCHED 
Model Size7b
Required VRAM15.1 GB
Updated2026-04-06
MaintainerTrelis
Model Typellava
Instruction-BasedYes
Model Files  4.9 GB: 1-of-4   5.0 GB: 2-of-4   4.9 GB: 3-of-4   0.3 GB: 4-of-4
Model ArchitectureLlavaLlamaForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.36.2
Tokenizer ClassLlamaTokenizer
Padding Token<pad>
Vocabulary Size32000
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than Trelis/llava-v1.6-mistral-7b-PATCHED.

Rank the Llava V1.6 Mistral 7B PATCHED 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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