What are the hardware requirements for Meltemi 7B V1.5?

Meltemi 7B V1.5 requires approximately 15 GB of VRAM and supports a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Meltemi 7B V1.5 and how large is it?

Meltemi 7B V1.5 is developed by ilsp, 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 Meltemi 7B V1.5?

Meltemi 7B V1.5 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Meltemi 7B V1.5 by ilsp — VRAM 15GB, 32K context

Name: Meltemi 7B V1.5
Author: ilsp

Meltemi 7B V1.5 is an open-source language model by ilsp. Features: 7b LLM, VRAM: 15GB, Context: 32K, License: apache-2.0, LLM Explorer Score: 0.14.

Arxiv:1803.05457 Arxiv:1905.07830 Arxiv:2009.03300 Arxiv:2109.07958 Arxiv:2308.16884 Arxiv:2407.20743 El En Endpoints compatible Mistral Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/ilsp/Meltemi-7B-v1.5

Meltemi 7B V1.5 Parameters and Internals

Model Type

text generation

Use Cases

Areas:

research, commercial applications

Applications:

text generation

Primary Use Cases:

Language understanding and reasoning, Question answering

Limitations:

May generate misleading or harmful content

Considerations:

Aligned with human preferences but still can produce toxic content

Additional Notes

Developed using Amazon's cloud services provided via GRNET under the OCRE Cloud framework.

Supported Languages

el (high), en (medium)

Training Details

Data Sources:

Greek monolingual data, English monolingual data, Greek-English parallel data

Data Volume:

55 billion tokens

Methodology:

Continual pretraining with proficiency for Greek

Context Length:

8192

Training Time:

Less than 2/3rds the time of Meltemi 7B v1

Hardware Used:

Amazon Web Services

Model Architecture:

Based on Mistral 7B with extended vocabulary for Greek

Input Output

Input Format:

Tokenized prompts with BOS token

Accepted Modalities:

text

Output Format:

Text

Performance Tips:

Ensure BOS token is included in tokenized prompts

LLM Name	Meltemi 7B V1.5
Repository 🤗	https://huggingface.co/ilsp/Meltemi-7B-v1.5
Model Size	7b
Required VRAM	15 GB
Updated	2026-07-15
Maintainer	ilsp
Model Type	mistral
Model Files	5.0 GB: 1-of-4 4.9 GB: 2-of-4 4.6 GB: 3-of-4 0.5 GB: 4-of-4
Supported Languages	el en
Model Architecture	MistralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.43.2
Tokenizer Class	LlamaTokenizer
Padding Token	<pad>
Vocabulary Size	61384
Torch Data Type	bfloat16

Rank the Meltemi 7B V1.5 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 Meltemi 7B V1.5

Best Alternatives	Context / RAM	Downloads	Likes
...Nemo Instruct 2407 Abliterated	1000K / 24.5 GB	220	20
MegaBeam Mistral 7B 512K	512K / 14.4 GB	8470	54
SpydazWeb AI HumanAI RP	512K / 14.4 GB	16	1
SpydazWeb AI HumanAI 002	512K / 14.4 GB	18	1
...daz Web AI ChatML 512K Project	512K / 14.5 GB	12	0
MegaBeam Mistral 7B 300K	282K / 14.4 GB	3779	16
MegaBeam Mistral 7B 300K	282K / 14.4 GB	8018	17
Hebrew Mistral 7B 200K	256K / 30 GB	23	15
Astral 256K 7B	250K / 14.4 GB	9	0
Astral 256K 7B V2	250K / 14.4 GB	8	0

Note: green Score (e.g. "73.2") means that the model is better than ilsp/Meltemi-7B-v1.5.