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Pythia 2.8B Deduped Int3 Step107000 GPTQ Wikitext2 by Xu-Ouyang

 »  All LLMs  »  Xu-Ouyang  »  Pythia 2.8B Deduped Int3 Step107000 GPTQ Wikitext2   URL Share it on

  76   
  Arxiv:1910.09700   3-bit   Autotrain compatible   Endpoints compatible   Gpt neox   Gptq   Quantized   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/Xu-Ouyang/pythia-2.8b-deduped-int3-step107000-GPTQ-wikitext2 

Pythia 2.8B Deduped Int3 Step107000 GPTQ Wikitext2 Benchmarks

LLME Score: 0.199
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Pythia 2.8B Deduped Int3 Step107000 GPTQ Wikitext2 (Xu-Ouyang/pythia-2.8b-deduped-int3-step107000-GPTQ-wikitext2)

Pythia 2.8B Deduped Int3 Step107000 GPTQ Wikitext2 Parameters and Internals

LLM NamePythia 2.8B Deduped Int3 Step107000 GPTQ Wikitext2
Repository 🤗https://huggingface.co/Xu-Ouyang/pythia-2.8b-deduped-int3-step107000-GPTQ-wikitext2 
Model Size2.8b
Required VRAM1.5 GB
Updated2025-02-22
MaintainerXu-Ouyang
Model Typegpt_neox
Model Files  1.5 GB
GPTQ QuantizationYes
Quantization Typegptq
Model ArchitectureGPTNeoXForCausalLM
Context Length2048
Model Max Length2048
Transformers Version4.41.2
Tokenizer ClassGPTNeoXTokenizer
Vocabulary Size50304
Torch Data Typefloat16

Best Alternatives to Pythia 2.8B Deduped Int3 Step107000 GPTQ Wikitext2

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Context / RAM
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...Int4 Step143000 GPTQ Wikitext22K / 1.8 GB790
... Int3 Step71000 GPTQ Wikitext22K / 1.5 GB780
...Int3 Step110000 GPTQ Wikitext22K / 1.5 GB780
...Int4 Step110000 GPTQ Wikitext22K / 1.8 GB780
... Int4 Step71000 GPTQ Wikitext22K / 1.8 GB780
...Int3 Step143000 GPTQ Wikitext22K / 1.5 GB770
Pythia 2.8B Deduped GPTQ2K / 1.8 GB810
Cassandra 2.8B4K / 1.7 GB75
Hh Full2K / 11.1 GB120
... Llm Pythia 2.8B Pm Gen Ian Nd2K / 11.1 GB50

Rank the Pythia 2.8B Deduped Int3 Step107000 GPTQ Wikitext2 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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Release v20241227
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