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, HF Score: 49.8, LLM Explorer Score: 0.12, Arc: 53.2, HellaSwag: 78.3, MMLU: 47.1, TruthfulQA: 39.1, WinoGrande: 73.2, 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 Benchmarks

ARC: 53.16 vs 96.7 (so35)^-45%

HellaSwag: 78.25 vs 95.3 (gpt4)^-17.9%

MMLU: 47.07 vs 88.3 (so35)^-46.7%

TruthfulQA: 39.08 vs 59 (gpt4)^-33.8%

WinoGrande: 73.24 vs 87.5 (gpt4)^-16.3%

GSM8K: 7.88 vs 96.4 (so35)^-91.8%

LLME Score: 0.12486

^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)

ELYZA Japanese Llama 2 7B Fast Instruct GPTQ (dahara1/ELYZA-japanese-Llama-2-7b-fast-instruct-GPTQ)

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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	2026-04-18
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

Best Alternatives to ELYZA Japanese Llama 2 7B Fast Instruct GPTQ

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... 7B 32K Instructions V4 Marlin	32K / 4.1 GB	8	0
Llama 2 7B 32K Instruct GPTQ	32K / 3.9 GB	15	27
...a 7B Instruct GPTQ Calib Ja 1K	16K / 3.9 GB	6	0
...echless Tora Code 7B V1.0 GPTQ	16K / 3.9 GB	17	1
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CausalLM 7B GPTQ	8K / 5.9 GB	8	5
...ssianai 7B Chat Bilingual GPTQ	8K / 3.9 GB	23	0
Leo Hessianai 7B Chat GPTQ	8K / 3.9 GB	25	0
Xgen 7B 8K Open Instruct Gptq	8K / 4.2 GB	5	4
Deepseek Math 7B Instruct GPTQ	4K / 4.8 GB	5	0

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

Rank the ELYZA Japanese Llama 2 7B Fast Instruct GPTQ Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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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Release v20260328a

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ELYZA Japanese Llama 2 7B Fast Instruct GPTQ by dahara1

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ELYZA Japanese Llama 2 7B Fast Instruct GPTQ Benchmarks

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

Best Alternatives to ELYZA Japanese Llama 2 7B Fast Instruct GPTQ

Rank the ELYZA Japanese Llama 2 7B Fast Instruct GPTQ Capabilities

What open-source LLMs or SLMs are you in search of? 53472 in total.