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Open-weight model · Text generation

logi-1.5b

by Alves guiiwfz/logi-1.5b

logi-1.5b is an open-weight model for text generation from Alves, released under Apache License 2.0. It has 1.5B parameters and a 32,768-token context. At 16-bit it needs about 3.7 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index. It draws 368 downloads a month.

This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.

Parameters1.5B
Context32,768
Weights3.1 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads368

Runs On

What it takes to serve logi-1.5b (1.5B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

PrecisionWeightsMemory neededCheapest setupPer hourAlso fits
16-bit 3.1 GB 3.7 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 1.5 GB 1.9 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 0.8 GB 0.9 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 18, 2026.

logi-1.5b on every accelerator the SAVRN Index prices, at every precision

Model Card

By Alves, published under apache-2.0, revision 6890066fe59b.

This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.

Read Alves's full model card

Uploaded finetuned model

  • Developed by: guiiwfz
  • License: apache-2.0
  • Finetuned from model : unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit

This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.

Configuration

Architecture
Qwen2ForCausalLM
Context length (tokens)
32,768
Layers
28
Hidden size
1,536
Feed-forward size
8,960
Attention heads
12
Key/value heads
2
Vocabulary size
151,936
Stored precision
bfloat16
Model type
qwen2

Identity and Version

Repository
guiiwfz/logi-1.5b
Publisher
Alves
Task
Text generation
Modality
Text
Library
transformers
Parameters
1.5B parameters
Languages
en
Revision
6890066fe59bc6ef09a6cfc024020bb9de81e8db
First published
2026-09-07
Last updated
2026-09-26

Files and Weights

8 files, 3.1 GB in total. The weights are 1 file totalling 3.1 GB in safetensors.

Weights1 file · 3.1 GB
Configuration2 files · 1.8 KB
Tokenizer2 files · 11.4 MB
Documentation1 file · 616 B
Other1 file · 2.5 KB
Repository1 file · 1.6 KB
Every file
FileTypeSizeSHA-256
model.safetensorsWeights3.1 GB ce1b8c0c6bca
config.jsonConfiguration1.5 KB —
generation_config.jsonConfiguration238 B —
README.mdDocumentation616 B —
chat_template.jinjaOther2.5 KB —
.gitattributesRepository1.6 KB —
tokenizer.jsonTokenizer11.4 MB 6b4360dd6a18
tokenizer_config.jsonTokenizer7.0 KB —

License and Download

License
apache-2.0
Access
Open weights, no gate
Download size
3.1 GB
Download from Alves

Released by Alves through its official repository on Hugging Face. Read the license.

Memory Requirements

PrecisionWeights in memory
As published3.1 GB
16-bit3.1 GB
8-bit1.5 GB
4-bit0.8 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Built on This Model

Questions About logi-1.5b

How much GPU memory does logi-1.5b need?

About 3.7 GB at 16-bit and 0.9 GB at 4-bit: the weights (1.5B parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run logi-1.5b on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

Can I use logi-1.5b commercially?

Yes. logi-1.5b is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.

What is logi-1.5b's context length?

32,768 tokens, from the maximum position embeddings in its published configuration.

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