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GPT J 6B by EleutherAI

 »  All LLMs  »  EleutherAI  »  GPT J 6B   URL Share it on

  213188   
  Arxiv:2101.00027   Arxiv:2104.09864   Autotrain compatible   Dataset:eleutherai/pile   En   Endpoints compatible   Gptj   Jax   Pytorch   Region:us   Tf
Model Card on HF 🤗: https://huggingface.co/EleutherAI/gpt-j-6b 

GPT J 6B Benchmarks

MMLU Pro: 2.68
MUSR: 5.25
BBH: 4.91
IFEval: 25.22 vs 88 (so35)-71.3%
ARC: 41.38 vs 96.7 (so35)-57.2%
HellaSwag: 67.54 vs 95.3 (gpt4)-29.1%
MMLU: 26.78 vs 88.3 (so35)-69.7%
TruthfulQA: 35.96 vs 59 (gpt4)-39.1%
WinoGrande: 65.98 vs 87.5 (gpt4)-24.6%
GSM8K: 2.96 vs 96.4 (so35)-96.9%
MATH Lvl 5: 1.28
LLME Score: 0.18841
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
GPT J 6B (EleutherAI/gpt-j-6b)

GPT J 6B Parameters and Internals

Model Type 
Autoregressive, Transformer, Causal Language Model
Use Cases 
Areas:
Research
Primary Use Cases:
Generating text from prompts
Limitations:
Not suitable for use without fine-tuning and supervision for downstream tasks., Not fit for human-facing interactions without moderation.
Considerations:
Users should evaluate risks with using the model in particular contexts.
Additional Notes 
The model requires fine-tuning to improve its use in specific applications such as chatbots.
Supported Languages 
English (High)
Training Details 
Data Sources:
EleutherAI's the Pile dataset
Methodology:
Autoregressive language modeling with cross-entropy loss.
Context Length:
2048
Hardware Used:
TPU v3-256 pod
Model Architecture:
The model consists of 28 layers. Each layer has one feedforward block and one self-attention block. The model has a dimension of 4096 and a feedforward dimension of 16384.
Responsible Ai Considerations 
Fairness:
The Pile dataset, used for training, contains biases which might reflect in the model's output.
Transparency:
The model is intended for research and transparency in experimental settings.
Accountability:
Users must supervise the use of the model to ensure suitable output.
Mitigation Strategies:
It is recommended to have human curation or filtering of outputs before deployment.
Input Output 
Input Format:
Text
Accepted Modalities:
Text
Output Format:
Generated Text
Performance Tips:
Ensure monitoring for quality and undesired outputs.
LLM NameGPT J 6B
Repository 🤗https://huggingface.co/EleutherAI/gpt-j-6b 
Model Size6b
Required VRAM24.2 GB
Updated2024-12-22
MaintainerEleutherAI
Model Typegptj
Model Files  24.2 GB
Supported Languagesen
Model ArchitectureGPTJForCausalLM
Licenseapache-2.0
Model Max Length2048
Transformers Version4.18.0.dev0
Tokenizer ClassGPT2Tokenizer
Beginning of Sentence Token<|endoftext|>
End of Sentence Token<|endoftext|>
Unk Token<|endoftext|>
Vocabulary Size50400
Activation Functiongelu_new
Errorsreplace

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Note: green Score (e.g. "73.2") means that the model is better than EleutherAI/gpt-j-6b.

Rank the GPT J 6B 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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Release v20241217
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