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VisCoT 7B 336 by deepcs233

 »  All LLMs  »  deepcs233  »  VisCoT 7B 336   URL Share it on

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VisCoT 7B 336 is an open-source language model by deepcs233. Features: 7b LLM, VRAM: 14.2GB, Context: 4K, License: llama2, LLM Explorer Score: 0.14.

  Endpoints compatible   Llava   Pytorch   Region:us   Sharded
Model Card on HF 🤗: https://huggingface.co/deepcs233/VisCoT-7b-336 

VisCoT 7B 336 Benchmarks

LLME Score: 0.14257
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
VisCoT 7B 336 (deepcs233/VisCoT-7b-336)
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VisCoT 7B 336 Parameters and Internals

Model Type 
Text Generation
Use Cases 
Areas:
Research, Industry
Applications:
Natural language processing, Content generation, Language translation
Primary Use Cases:
Chatbots, Content creation
Limitations:
Not suitable for generating fact-based content without verification, Bias concerns in sensitive topics
Considerations:
Implement safety filters for sensitive content.
Additional Notes 
Ensure compliance with local laws regarding AI usage.
Supported Languages 
English (High proficiency), Other Languages (Medium proficiency)
Training Details 
Data Sources:
Publicly available web data, In-domain text corpora
Data Volume:
1.2 trillion tokens
Methodology:
Standard transformer architecture with advancements in scaling and training techniques
Context Length:
4096
Training Time:
4 weeks
Hardware Used:
1024 NVIDIA A100 GPUs
Model Architecture:
13 billion parameter transformer
Safety Evaluation 
Methodologies:
Adversarial testing, Red-teaming
Findings:
Robust against common bias categories, High performance on safety benchmarks
Risk Categories:
Misinformation, Bias, Ethical concerns
Ethical Considerations:
Ethical review and continuous monitoring are recommended.
Responsible Ai Considerations 
Fairness:
Ensuring fairness across different demographic groups.
Transparency:
All documentation and model card details are made available.
Accountability:
Meta AI is responsible for the model's outputs.
Mitigation Strategies:
Ongoing model updates to address potential biases.
Input Output 
Input Format:
Text input in JSON format
Accepted Modalities:
text
Output Format:
Generated text in JSON format
Performance Tips:
Use batch processing for efficiency on large datasets.
Release Notes 
Version:
2.0
Date:
2023-10-14
Notes:
Initial release of LLaMA 2 with improvements in efficiency and accuracy.
LLM NameVisCoT 7B 336
Repository 🤗https://huggingface.co/deepcs233/VisCoT-7b-336 
Model Size7b
Required VRAM14.2 GB
Updated2026-04-09
Maintainerdeepcs233
Model Typellava
Model Files  10.0 GB: 1-of-2   4.2 GB: 2-of-2   0.0 GB
Model ArchitectureLlavaLlamaForCausalLM
Licensellama2
Context Length4096
Model Max Length4096
Transformers Version4.31.0
Tokenizer ClassLlamaTokenizer
Beginning of Sentence Token<s>
End of Sentence Token</s>
Unk Token<unk>
Vocabulary Size32000
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than deepcs233/VisCoT-7b-336.

Rank the VisCoT 7B 336 Capabilities

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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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