What are the hardware requirements for Allam 7B Mental Health Merged?

Allam 7B Mental Health Merged requires approximately 14 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 Allam 7B Mental Health Merged and how large is it?

Allam 7B Mental Health Merged is developed by mabaashar, a model with 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 Allam 7B Mental Health Merged?

Allam 7B Mental Health Merged is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Allam 7B Mental Health Merged by mabaashar — VRAM 14GB, 4K context

Name: Allam 7B Mental Health Merged
Author: mabaashar

Allam 7B Mental Health Merged is an open-source language model by mabaashar. Features: 7b LLM, VRAM: 14GB, Context: 4K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.32.

Ar Base model:finetune:humain-ai/... Base model:humain-ai/allam-7b-... Conversational En Endpoints compatible Instruct Llama Region:us Safetensors Sharded Tensorflow Trl Unsloth

Model Card on HF 🤗: https://huggingface.co/mabaashar/allam-7b-mental-health-merged

Allam 7B Mental Health Merged Parameters and Internals

LLM Name	Allam 7B Mental Health Merged
Repository 🤗	https://huggingface.co/mabaashar/allam-7b-mental-health-merged
Base Model(s)	ALLaM 7B Instruct Preview humain-ai/ALLaM-7B-Instruct-preview
Model Size	7b
Required VRAM	14 GB
Updated	2026-09-16
Maintainer	mabaashar
Model Type	llama
Instruction-Based	Yes
Model Files	5.0 GB: 1-of-3 5.0 GB: 2-of-3 4.0 GB: 3-of-3
Supported Languages	en ar
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	4096
Model Max Length	4096
Transformers Version	4.57.2
Tokenizer Class	LlamaTokenizer
Padding Token	<unk>
Vocabulary Size	64000

Rank the Allam 7B Mental Health Merged 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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