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FashionGPT 70B V1.2 by ICBU-NPU

 »  All LLMs  »  ICBU-NPU  »  FashionGPT 70B V1.2   URL Share it on

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  Autotrain compatible   Endpoints compatible   Llama   Pytorch   Region:us   Sharded
Model Card on HF 🤗: https://huggingface.co/ICBU-NPU/FashionGPT-70B-V1.2 

FashionGPT 70B V1.2 Benchmarks

ARC: 73.04 vs 96.7 (so35)-24.5%
HellaSwag: 88.15 vs 95.3 (gpt4)-7.5%
MMLU: 70.11 vs 88.3 (so35)-20.6%
TruthfulQA: 65.15 vs 59 (gpt4)10.4%
WinoGrande: 82.56 vs 87.5 (gpt4)-5.6%
GSM8K: 24.03 vs 96.4 (so35)-75.1%
LLME Score: 0.14762
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
FashionGPT 70B V1.2 (ICBU-NPU/FashionGPT-70B-V1.2)
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FashionGPT 70B V1.2 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 NameFashionGPT 70B V1.2
Repository 🤗https://huggingface.co/ICBU-NPU/FashionGPT-70B-V1.2 
Model Size70b
Required VRAM138 GB
Updated2025-09-14
MaintainerICBU-NPU
Model Typellama
Model Files  9.8 GB: 1-of-15   9.8 GB: 2-of-15   10.0 GB: 3-of-15   9.8 GB: 4-of-15   9.8 GB: 5-of-15   9.8 GB: 6-of-15   10.0 GB: 7-of-15   9.8 GB: 8-of-15   9.8 GB: 9-of-15   9.8 GB: 10-of-15   10.0 GB: 11-of-15   9.8 GB: 12-of-15   9.8 GB: 13-of-15   9.5 GB: 14-of-15   0.5 GB: 15-of-15
Model ArchitectureLlamaForCausalLM
Licensellama2
Context Length4096
Model Max Length4096
Transformers Version4.32.0.dev0
Tokenizer ClassLlamaTokenizer
Beginning of Sentence Token<s>
End of Sentence Token</s>
Unk Token<unk>
Vocabulary Size32000
Torch Data Typefloat16

Quantized Models of the FashionGPT 70B V1.2

Model
Likes
Downloads
VRAM
FashionGPT 70B V1.2 GGUF85129 GB
FashionGPT 70B V1.2 GPTQ6435 GB
FashionGPT 70B V1.2 AWQ1436 GB

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Note: green Score (e.g. "73.2") means that the model is better than ICBU-NPU/FashionGPT-70B-V1.2.

Rank the FashionGPT 70B V1.2 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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