What are the hardware requirements for Arsh Llm 0.7B?

Arsh Llm 0.7B requires approximately 2.8 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 Arsh Llm 0.7B and how large is it?

Arsh Llm 0.7B is developed by arshiaafshani, a model with 0.7b 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 Arsh Llm 0.7B?

Arsh Llm 0.7B is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Arsh Llm 0.7B by arshiaafshani — VRAM 2.8GB, 2K context

Name: Arsh Llm 0.7B
Author: arshiaafshani

Arsh Llm 0.7B is an open-source language model by arshiaafshani. Features: 0.7b LLM, VRAM: 2.8GB, Context: 2K, License: mit, LLM Explorer Score: 0.18.

Conversational Endpoints compatible Llama Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/arshiaafshani/Arsh-llm-0.7b

Arsh Llm 0.7B Parameters and Internals

LLM Name	Arsh Llm 0.7B
Repository 🤗	https://huggingface.co/arshiaafshani/Arsh-llm-0.7b
Model Size	0.7b
Required VRAM	2.8 GB
Updated	2026-07-30
Maintainer	arshiaafshani
Model Type	llama
Model Files	2.8 GB 0.0 GB
Model Architecture	LlamaForCausalLM
License	mit
Context Length	2048
Model Max Length	2048
Transformers Version	4.52.4
Tokenizer Class	PreTrainedTokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	32768
Torch Data Type	float32
Errors	replace

Rank the Arsh Llm 0.7B 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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