What are the hardware requirements for ELYZA Japanese Llama 2 7B Fast Instruct GPTQ?

ELYZA Japanese Llama 2 7B Fast Instruct GPTQ requires approximately 4.1 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 ELYZA Japanese Llama 2 7B Fast Instruct GPTQ and how large is it?

ELYZA Japanese Llama 2 7B Fast Instruct GPTQ is developed by dahara1, 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 ELYZA Japanese Llama 2 7B Fast Instruct GPTQ perform on standard benchmarks?

ELYZA Japanese Llama 2 7B Fast Instruct GPTQ has the following published scores: MMLU 47.07. Compare against reference models on this page or on the LLM Explorer leaderboards.

ELYZA Japanese Llama 2 7B Fast Instruct GPTQ by dahara1 — VRAM 4.1GB, 4K context

Name: ELYZA Japanese Llama 2 7B Fast Instruct GPTQ
Author: dahara1

ELYZA Japanese Llama 2 7B Fast Instruct GPTQ is an open-source language model by dahara1. Features: 7b LLM, VRAM: 4.1GB, Context: 4K, License: llama2, Quantized, Instruction-Based, LLM Explorer Score: 0.11, Arc: 53.2, HellaSwag: 78.3, MMLU: 47.1, GSM8K: 7.9.

Arxiv:2307.09288 4bit En Gptq Instruct Ja Llama Quantized Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/dahara1/ELYZA-japanese-Llama-2-7b-fast-instruct-GPTQ

ELYZA Japanese Llama 2 7B Fast Instruct GPTQ Parameters and Internals

Model Type

text generation

Additional Notes

The model is a quantized version of the original ELYZA-japanese-Llama-2-7b model, specifically tuned for Japanese instructions.

Supported Languages

ja (Japanese), en (English)

Training Details

Methodology:

Quantization and additional pre-training in Japanese followed by post-training and speed-up tuning.

Input Output

Accepted Modalities:

text

LLM Name	ELYZA Japanese Llama 2 7B Fast Instruct GPTQ
Repository 🤗	https://huggingface.co/dahara1/ELYZA-japanese-Llama-2-7b-fast-instruct-GPTQ
Base Model(s)	...A Japanese Llama 2 7B Instruct elyza/ELYZA-japanese-Llama-2-7b-instruct
Model Size	7b
Required VRAM	4.1 GB
Updated	2026-07-28
Maintainer	dahara1
Model Type	llama
Instruction-Based	Yes
Model Files	4.1 GB 4.1 GB
Supported Languages	ja en
GPTQ Quantization	Yes
Quantization Type	gptq\|4bit
Model Architecture	LlamaForCausalLM
License	llama2
Context Length	4096
Model Max Length	4096
Transformers Version	4.35.0
Tokenizer Class	LlamaTokenizer
Padding Token	</s>
Vocabulary Size	45043
Torch Data Type	float16

Rank the ELYZA Japanese Llama 2 7B Fast Instruct GPTQ 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

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Note: green Score (e.g. "73.2") means that the model is better than dahara1/ELYZA-japanese-Llama-2-7b-fast-instruct-GPTQ.