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RedPajama INCITE Instruct 7B V0.1 GGML by keldenl

 »  All LLMs  »  keldenl  »  RedPajama INCITE Instruct 7B V0.1 GGML   URL Share it on

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  Autotrain compatible Dataset:muennighoff/natural-in...   Dataset:muennighoff/p3 Dataset:togethercomputer/redpa...   En   Endpoints compatible   Fp16   Ggml   Gpt neox   Instruct   Pytorch   Q4   Quantized   Red pajama   Region:us
Model Card on HF 🤗: https://huggingface.co/keldenl/RedPajama-INCITE-Instruct-7B-v0.1-GGML 

RedPajama INCITE Instruct 7B V0.1 GGML Benchmarks

LLME Score: 0.08176
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
RedPajama INCITE Instruct 7B V0.1 GGML (keldenl/RedPajama-INCITE-Instruct-7B-v0.1-GGML)
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RedPajama INCITE Instruct 7B V0.1 GGML Parameters and Internals

Model Type 
Language Model
Additional Notes 
The model will NOT work with llama.cpp as of 5/8/2023. It requires the GGML fork in https://github.com/ggerganov/ggml/pull/134.
Supported Languages 
English (fluent)
Training Details 
Data Sources:
togethercomputer/RedPajama-Data-1T, Muennighoff/P3, Muennighoff/natural-instructions
Methodology:
Fine-tuned for few-shot applications
Input Output 
Input Format:
instruction format + few shot prompting
Output Format:
text generation
LLM NameRedPajama INCITE Instruct 7B V0.1 GGML
Repository 🤗https://huggingface.co/keldenl/RedPajama-INCITE-Instruct-7B-v0.1-GGML 
Model Size7b
Required VRAM4.3 GB
Updated2025-10-28
Maintainerkeldenl
Model Typegpt_neox
Instruction-BasedYes
Model Files  4.3 GB   5.2 GB   4.3 GB   4.7 GB   5.2 GB   7.7 GB
Supported Languagesen
GGML QuantizationYes
Quantization Typefp16|ggml|q4
Model ArchitectureGPTNeoXForCausalLM
Licenseapache-2.0
Context Length2048
Model Max Length2048
Transformers Version4.28.1
Tokenizer ClassGPTNeoXTokenizer
Vocabulary Size50432
Torch Data Typefloat16

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RedPajama INCITE 7B Instruct2K / 13.8 GB1933106
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Note: green Score (e.g. "73.2") means that the model is better than keldenl/RedPajama-INCITE-Instruct-7B-v0.1-GGML.

Rank the RedPajama INCITE Instruct 7B V0.1 GGML 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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