What are the hardware requirements for Tinybrainbot 10M V2 Base?

Tinybrainbot 10M V2 Base requires a context window of 1K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Tinybrainbot 10M V2 Base and how large is it?

Tinybrainbot 10M V2 Base is developed by nkthebass, a model with 10m 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 Tinybrainbot 10M V2 Base?

Tinybrainbot 10M V2 Base is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Tinybrainbot 10M V2 Base by nkthebass — 1K context

Name: Tinybrainbot 10M V2 Base
Author: nkthebass

Tinybrainbot 10M V2 Base is an open-source language model by nkthebass. Features: 10m LLM, Context: 1K, License: apache-2.0, Quantized, LLM Explorer Score: 0.32.

Dataset:arrowlabs/dclm-ultra Dataset:arrowlabs/dclm-ultra-1... Dataset:huggingfacefw/fineweb-... Dataset:huggingfacetb/cosmoped... Dataset:microsoft/orca-math-wo... Dataset:openbmb/ultradata-math Dataset:roneneldan/tinystories Dataset:wikimedia/wikipedia En Endpoints compatible From-scratch Gguf Llama Model-index Q8 Quantized Region:us Safetensors Small-language-model Tiny

Model Card on HF 🤗: https://huggingface.co/nkthebass/tinybrainbot-10m-v2-base

Tinybrainbot 10M V2 Base Parameters and Internals

LLM Name	Tinybrainbot 10M V2 Base
Repository 🤗	https://huggingface.co/nkthebass/tinybrainbot-10m-v2-base
Model Size	10m
Required VRAM	0 GB
Updated	2026-10-31
Maintainer	nkthebass
Model Type	llama
Model Files	0.0 GB 0.0 GB 0.0 GB
Supported Languages	en
GGUF Quantization	Yes
Quantization Type	gguf\|q8
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	1024
Model Max Length	1024
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<\|pad\|>
Vocabulary Size	4096
Torch Data Type	float32

Rank the Tinybrainbot 10M V2 Base 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 Tinybrainbot 10M V2 Base

Best Alternatives	Context / RAM	Downloads	Likes
Phi4 MedIT 10B O1	16K / 20.6 GB	27	4
Falcon3 10B Instruct 1.58bit	32K / 4 GB	422	27
...truct Gptqmodel 4bit Vortex V1	32K / 5.6 GB	9	3
...enbuddy Falcon3 10B V24.2 131K	128K / 20.7 GB	6	0
Priya 10B	128K / 20.5 GB	11	2
HelpingAI2.5 10B	128K / 20.5 GB	66	2
L3.1 Mochav2 10B	128K / 42.8 GB	21	0
HELVETE X	128K / 20.5 GB	104	11
HelpingAI2.5 10B	128K / 20.5 GB	9	8
Yarn Solar 10B 64K	64K / 21.4 GB	150	16