What are the hardware requirements for DPO VinaLlama Completed?

DPO VinaLlama Completed requires approximately 5.6 GB of VRAM and supports a context window of 4K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed DPO VinaLlama Completed and how large is it?

DPO VinaLlama Completed is developed by VanCan23, a model with 2.7b parameters. 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 DPO VinaLlama Completed?

DPO VinaLlama Completed is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

DPO VinaLlama Completed by VanCan23 — VRAM 5.6GB, 4K context

Name: DPO VinaLlama Completed
Author: VanCan23

DPO VinaLlama Completed is an open-source language model by VanCan23. Features: 2.7b LLM, VRAM: 5.6GB, Context: 4K, License: apache-2.0, LLM Explorer Score: 0.12.

Endpoints compatible Llama Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/VanCan23/DPO-VinaLlama-completed

DPO VinaLlama Completed Parameters and Internals

LLM Name	DPO VinaLlama Completed
Repository 🤗	https://huggingface.co/VanCan23/DPO-VinaLlama-completed
Model Size	2.7b
Required VRAM	5.6 GB
Updated	2026-08-03
Maintainer	VanCan23
Model Type	llama
Model Files	5.0 GB: 1-of-2 0.6 GB: 2-of-2
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	4096
Model Max Length	4096
Transformers Version	4.41.2
Vocabulary Size	46306
Torch Data Type	float16

Rank the DPO VinaLlama Completed 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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Note: green Score (e.g. "73.2") means that the model is better than VanCan23/DPO-VinaLlama-completed.