What are the hardware requirements for Arsh Llm 14B?

Arsh Llm 14B requires approximately 29.4 GB of VRAM and supports a context window of 16K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Arsh Llm 14B and how large is it?

Arsh Llm 14B is developed by arshiaafshani, a model with 14b 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 14B?

Arsh Llm 14B is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Arsh Llm 14B by arshiaafshani — VRAM 29.4GB, 16K context

Name: Arsh Llm 14B
Author: arshiaafshani

Arsh Llm 14B is an open-source language model by arshiaafshani. Features: 14b LLM, VRAM: 29.4GB, Context: 16K, License: mit, LLM Explorer Score: 0.18.

Conversational Custom code Llama Pytorch Region:us Sft Sharded Trl

Model Card on HF 🤗: https://huggingface.co/arshiaafshani/Arsh-llm-14b

Arsh Llm 14B Parameters and Internals

LLM Name	Arsh Llm 14B
Repository 🤗	https://huggingface.co/arshiaafshani/Arsh-llm-14b
Model Size	14b
Required VRAM	29.4 GB
Updated	2025-05-14
Maintainer	arshiaafshani
Model Type	llama
Model Files	4.9 GB: 1-of-6 5.0 GB: 2-of-6 4.9 GB: 3-of-6 5.0 GB: 4-of-6 5.0 GB: 5-of-6 4.6 GB: 6-of-6
Model Architecture	LlamaForCausalLM
License	mit
Context Length	16384
Model Max Length	16384
Transformers Version	4.51.3
Tokenizer Class	GPT2Tokenizer
Padding Token	<\|dummy_87\|>
Vocabulary Size	100352
Torch Data Type	float16
Errors	replace

Rank the Arsh Llm 14B 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 arshiaafshani/Arsh-llm-14b.