What are the hardware requirements for Mistral 7B Instruct Vi Alpaca?

Mistral 7B Instruct Vi Alpaca requires approximately 0.4 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Mistral 7B Instruct Vi Alpaca and how large is it?

Mistral 7B Instruct Vi Alpaca is developed by date3k2, a model with 7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Mistral 7B Instruct Vi Alpaca perform on standard benchmarks?

Mistral 7B Instruct Vi Alpaca has the following published scores: LMSYS ELO 1109. Compare against reference models on this page or on the LLM Explorer leaderboards.

Mistral 7B Instruct Vi Alpaca by date3k2 — VRAM 0.4GB

Name: Mistral 7B Instruct Vi Alpaca
Author: date3k2

Mistral 7B Instruct Vi Alpaca is an open-source language model by date3k2. Features: 7b LLM, VRAM: 0.4GB, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.23, ELO: 1109.

Adapter Base model:adapter:mistralai/m... Base model:mistralai/mistral-7... Conversational Dataset:bkai-foundation-models... En Finetuned Generated from trainer Instruct Lora Peft Region:us Safetensors Sft Trl Vi

Model Card on HF 🤗: https://huggingface.co/date3k2/Mistral-7B-Instruct-vi-alpaca

Mistral 7B Instruct Vi Alpaca Benchmarks

LMSys ELO 1109 vs 1272 (so35) -12.8%

^nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

What is the LLM Explorer Rank (Score)

Mistral 7B Instruct Vi Alpaca Parameters and Internals

Model Type

text generation

Use Cases

Areas:

text generation applications

Additional Notes

This model is optimized for generating Vietnamese instructional content. The demonstration shows integration with the Hugging Face library.

Supported Languages

vi (proficient), en (proficient)

Training Details

Data Sources:

bkai-foundation-models/vi-alpaca

Methodology:

LoRA fine-tuning

Input Output

Input Format:

chat template in PT tensors

Accepted Modalities:

text

Output Format:

generated text

LLM Name	Mistral 7B Instruct Vi Alpaca
Repository 🤗	https://huggingface.co/date3k2/Mistral-7B-Instruct-vi-alpaca
Base Model(s)	mistralai/Mistral-7B-Instruct-v0.3 mistralai/Mistral-7B-Instruct-v0.3
Model Size	7b
Required VRAM	0.4 GB
Updated	2026-02-28
Maintainer	date3k2
Instruction-Based	Yes
Model Files	0.4 GB 0.0 GB
Supported Languages	vi en
Model Architecture	Adapter
License	apache-2.0
Is Biased	none
Tokenizer Class	LlamaTokenizer
Padding Token	</s>
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	v_proj\|o_proj\|k_proj\|gate_proj\|q_proj
LoRA Alpha	16
LoRA Dropout	0.1
R Param	64

Rank the Mistral 7B Instruct Vi Alpaca 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 Mistral 7B Instruct Vi Alpaca

Best Alternatives	Context / RAM	Downloads	Likes
Pallade Idrico 7B	0K / 0.3 GB	26	1
Antcoder Planner 7B	0K / 0.3 GB	37	1
Vilaw Llm DPO	0K / 0.2 GB	17	2
Zduhac Cyber 7B V2	0K / 0 GB	8	1
Llama2 7B Qlora Guanaco	0K / 0.1 GB	11	1
Qwen Megumin	0K / 0.1 GB	11	1
Vietnamese Amr Baseline 7B	0K / 0 GB	13	0
Mistral 7B Instruct Sa V0.1	0K / 0 GB	9	0
Deepthink Reasoning Adapter	0K / 0.2 GB	16	3
Qwen2.5 7b NotesCorrector	0K / 0.6 GB	10	0

Note: green Score (e.g. "73.2") means that the model is better than date3k2/Mistral-7B-Instruct-vi-alpaca.