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GPT J GGML by rustformers

 »  All LLMs  »  rustformers  »  GPT J GGML   URL Share it on

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  Arxiv:2104.09864   Dataset:the pile   En   Endpoints compatible   Ggml   Llm-rs   Q4   Q4 0   Quantized   Region:us
Model Card on HF 🤗: https://huggingface.co/rustformers/gpt-j-ggml 

GPT J GGML Benchmarks

LLME Score: 0.08553
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
GPT J GGML (rustformers/gpt-j-ggml)
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GPT J GGML Parameters and Internals

Model Type 
text generation
Additional Notes 
The model uses the same BPE tokenizer as GPT-2/GPT-3 with a vocabulary size of 50257 out of 50400 possible embeddings. It uses Rotary Position Embeddings for 64 dimensions per head.
Training Details 
Data Sources:
the_pile
Methodology:
Trained using Mesh Transformer JAX
Context Length:
2048
Model Architecture:
Transformer with 28 layers, RoPE positional encoding
Input Output 
Input Format:
Text
Output Format:
Text
LLM NameGPT J GGML
Repository 🤗https://huggingface.co/rustformers/gpt-j-ggml 
Model Size6b
Required VRAM3.4 GB
Updated2025-09-23
Maintainerrustformers
Model Files  12.1 GB   3.4 GB   3.4 GB   4.5 GB   4.5 GB
Supported Languagesen
GGML QuantizationYes
Quantization Typeggml|q4|q4_0|q5_1
Model ArchitectureAutoModel

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Note: green Score (e.g. "73.2") means that the model is better than rustformers/gpt-j-ggml.

Rank the GPT J GGML 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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