What are the hardware requirements for Cotype Nano?

Cotype Nano requires approximately 3.1 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 Cotype Nano and how large is it?

Cotype Nano is developed by MTSAIR, a model with 1.5b 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 Cotype Nano?

Cotype Nano is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Cotype Nano by MTSAIR — VRAM 3.1GB, 32K context

Name: Cotype Nano
Author: MTSAIR

Cotype Nano is an open-source language model by MTSAIR. Features: 1.5b LLM, VRAM: 3.1GB, Context: 32K, License: other, LLM Explorer Score: 0.22.

Conversational Deploy:azure En Endpoints compatible Eval-results Qwen2 Region:us Ru Safetensors

Model Card on HF 🤗: https://huggingface.co/MTSAIR/Cotype-Nano

Cotype Nano Parameters and Internals

Model Type

text-generation

Additional Notes

Cotype-Nano is optimized for fast and efficient interaction with users, providing high performance even under resource-constrained conditions.

Supported Languages

ru (unknown), en (unknown)

Training Details

Data Sources:

internal and open synthetic instructional datasets

Methodology:

The model was trained in two stages. In the first stage, MLP layers were trained on mathematics and code. In the second stage, the entire model was trained

Input Output

Input Format:

text

Accepted Modalities:

text

Output Format:

text

LLM Name	Cotype Nano
Repository 🤗	https://huggingface.co/MTSAIR/Cotype-Nano
Model Size	1.5b
Required VRAM	3.1 GB
Updated	2026-07-18
Maintainer	MTSAIR
Model Type	qwen2
Model Files	3.1 GB
Supported Languages	ru en
Model Architecture	Qwen2ForCausalLM
License	other
Context Length	32768
Model Max Length	32768
Transformers Version	4.46.2
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
Torch Data Type	bfloat16
Errors	replace

Rank the Cotype Nano 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 Cotype Nano

Best Alternatives	Context / RAM	Downloads	Likes
Qwen2.5 1.5B 1.3M Stretched	1280K / 3.1 GB	11	0
ReaderLM V2	500K / 3.1 GB	35356	804
Reader Lm 1.5B	250K / 3.1 GB	385	608
DeepSeek R1 Distill Qwen 1.5B	128K / 3.5 GB	600051	1545
Ko R1 1.5B Preview	128K / 7.1 GB	8	9
Qwen2.5 1.5B	128K / 3.1 GB	863295	203
Merge Cord V1	128K / 3.1 GB	155	1
VibeThinker 1.5B	128K / 3.5 GB	3417	552
...FT Tulu3 Decontaminated Masked	128K / 3.1 GB	167	0
Qwen2 1.5B	128K / 3.1 GB	195380	102

Note: green Score (e.g. "73.2") means that the model is better than MTSAIR/Cotype-Nano.