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LLaMA3 Iterative DPO Final GGUF by sirovub

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LLaMA3 Iterative DPO Final GGUF is an open-source language model by sirovub. Features: LLM, VRAM: 16.1GB, Context: 8K, License: llama3, Quantized, LLM Explorer Score: 0.14.

  Arxiv:2312.11456   Arxiv:2405.07863   Conversational   Endpoints compatible   Gguf   Llama   Q8   Quantized   Region:us   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/sirovub/LLaMA3-iterative-DPO-final-GGUF 

LLaMA3 Iterative DPO Final GGUF Benchmarks

LLME Score: 0.13632
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
LLaMA3 Iterative DPO Final GGUF (sirovub/LLaMA3-iterative-DPO-final-GGUF)
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LLaMA3 Iterative DPO Final GGUF Parameters and Internals

Model Type 
text generation, instruction-following
Additional Notes 
RLHFlow\LLaMA3-iterative-DPO-final is an unofficial checkpoint developed for research purposes. While safety and ethical considerations are integral to the alignment process, there remains the possibility that the model could generate offensive or unethical content under adversarial conditions.
Training Details 
Data Sources:
https://huggingface.co/datasets/hendrydong/preference_700K, https://huggingface.co/datasets/RLHFlow/prompt-collection-v0.1
Methodology:
Iterative DPO
LLM NameLLaMA3 Iterative DPO Final GGUF
Repository 🤗https://huggingface.co/sirovub/LLaMA3-iterative-DPO-final-GGUF 
Required VRAM16.1 GB
Updated2026-04-08
Maintainersirovub
Model Typellama
Model Files  8.5 GB   16.1 GB   5.0 GB: 1-of-4   5.0 GB: 2-of-4   4.9 GB: 3-of-4   1.2 GB: 4-of-4
GGUF QuantizationYes
Quantization Typegguf|q8
Model ArchitectureLlamaForCausalLM
Licensellama3
Context Length8192
Model Max Length8192
Transformers Version4.40.0.dev0
Tokenizer ClassPreTrainedTokenizerFast
Padding Token<|end_of_text|>
Vocabulary Size128256
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than sirovub/LLaMA3-iterative-DPO-final-GGUF.

Rank the LLaMA3 Iterative DPO Final GGUF 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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