What are the hardware requirements for LLaMA3 Iterative DPO Final GGUF?

LLaMA3 Iterative DPO Final GGUF requires approximately 16.1 GB of VRAM and supports a context window of 8K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed LLaMA3 Iterative DPO Final GGUF and how large is it?

LLaMA3 Iterative DPO Final GGUF is developed by sirovub. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate LLaMA3 Iterative DPO Final GGUF?

LLaMA3 Iterative DPO Final GGUF is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

LLaMA3 Iterative DPO Final GGUF by sirovub — VRAM 16.1GB, 8K context

Name: LLaMA3 Iterative DPO Final GGUF
Author: sirovub

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: 0.13.

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 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 Name	LLaMA3 Iterative DPO Final GGUF
Repository 🤗	https://huggingface.co/sirovub/LLaMA3-iterative-DPO-final-GGUF
Required VRAM	16.1 GB
Updated	2026-08-04
Maintainer	sirovub
Model Type	llama
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 Quantization	Yes
Quantization Type	gguf\|q8
Model Architecture	LlamaForCausalLM
License	llama3
Context Length	8192
Model Max Length	8192
Transformers Version	4.40.0.dev0
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<\|end_of_text\|>
Vocabulary Size	128256
Torch Data Type	bfloat16

Rank the LLaMA3 Iterative DPO Final GGUF capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

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

Best Alternatives to LLaMA3 Iterative DPO Final GGUF

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KernelLLM GGUF	128K / 2.2 GB	454	2
LLAMA2 GOOD GGUF	16K / 4.8 GB	15	0
Codellama Cairo Instruct GGUF	16K / 4.1 GB	32	1
Aware Ai 1st	8K / 16.1 GB	30	0
MFANNv0.6 GGUF	8K / 4.7 GB	14	0
UlizaLlama Q4 K M Gguf	4K / 4.2 GB	76	0
Tinyllama Coder Py V15	4K / 0.7 GB	98	0
Tinyllama Coder Py V16	4K / 0.7 GB	12	0
Cancer Llama.5 Llm	4K / 4.1 GB	25	0
Airavata GGUF	4K / 4.2 GB	43	2

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