What are the hardware requirements for Llama2 Ja Small Instruct?

Llama2 Ja Small Instruct requires approximately 1.7 GB of VRAM and supports a context window of 2K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Llama2 Ja Small Instruct and how large is it?

Llama2 Ja Small Instruct is developed by if001. 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 Llama2 Ja Small Instruct?

Llama2 Ja Small Instruct is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Llama2 Ja Small Instruct by if001 — VRAM 1.7GB, 2K context

Name: Llama2 Ja Small Instruct
Author: if001

Llama2 Ja Small Instruct is an open-source language model by if001. Features: LLM, VRAM: 1.7GB, Context: 2K, License: cc-by-4.0, Instruction-Based, LLM Explorer Score: 0.09.

Conversational Dataset:kunishou/databricks-do... Dataset:kunishou/oasst1-89k-ja En Instruct Ja Japanese Llama Llama2 Lm Pytorch Region:us

Model Card on HF 🤗: https://huggingface.co/if001/llama2_ja_small_instruct

Llama2 Ja Small Instruct Parameters and Internals

Model Type

text generation, lm, nlp, conversational

Use Cases

Areas:

research, educational

Applications:

text generation, conversational AI, language understanding

Primary Use Cases:

Japanese text generation, Instruction following in Japanese

Limitations:

May not perform well outside trained domains

Additional Notes

The model has been specifically trained and fine-tuned for Japanese language generation and instruction-following tasks.

Supported Languages

ja (fluent), en (competent)

Training Details

Data Sources:

kunishou/databricks-dolly-15k-ja, kunishou/oasst1-89k-ja

Methodology:

SFT using instruction datasets

Model Architecture:

Modified LLaMA2 architecture for Japanese context

Input Output

Input Format:

structured prompts

Accepted Modalities:

text

Output Format:

Japanese text responses

Performance Tips:

Ensure prompts are well-structured Japanese text

LLM Name	Llama2 Ja Small Instruct
Repository 🤗	https://huggingface.co/if001/llama2_ja_small_instruct
Required VRAM	1.7 GB
Updated	2026-07-22
Maintainer	if001
Model Type	llama
Instruction-Based	Yes
Model Files	1.7 GB
Supported Languages	ja en
Model Architecture	LlamaForCausalLM
License	cc-by-4.0
Context Length	2048
Model Max Length	2048
Transformers Version	4.34.1
Vocabulary Size	35008
Torch Data Type	float32

Rank the Llama2 Ja Small Instruct 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 Llama2 Ja Small Instruct

Best Alternatives	Context / RAM	Downloads	Likes
Reverse Instruct	32K / 27 GB	8	3
Etri Ones Solar	4K / 42.9 GB	50	0
Phi 3 Orpo V8.16	4K / 7.6 GB	7	0
Tinyllama Python	4K / 2.2 GB	13	2
Small Instruct	4K / 2.9 GB	10	1
Taiwan LLaMa V1.0	4K / 26 GB	35	78
Taiwan LLaMa V0.0	4K / 26 GB	14	1
Taiwan LLaMa V0.9	4K / 26 GB	13	0
Kolong Llama V0.1	2K / 13.7 GB	7	0
...ama Miniguanaco Instruct 121v2	2K / 2.2 GB	11	0

Note: green Score (e.g. "73.2") means that the model is better than if001/llama2_ja_small_instruct.