What are the hardware requirements for Slm125m Base?

Slm125m Base requires approximately 0.5 GB of VRAM and supports a context window of 1K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Slm125m Base and how large is it?

Slm125m Base is developed by nitin1pawar, a model with 125.8m 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 Slm125m Base?

Slm125m Base is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Slm125m Base by nitin1pawar — VRAM 0.5GB, 1K context

Name: Slm125m Base
Author: nitin1pawar

Slm125m Base is an open-source language model by nitin1pawar. Features: 125.8m LLM, VRAM: 0.5GB, Context: 1K, License: apache-2.0, LLM Explorer Score: 0.24.

En Endpoints compatible Finance Legal Llama Pretrained-from-scratch Region:us Safetensors Small-language-model

Model Card on HF 🤗: https://huggingface.co/nitin1pawar/slm125m-base

Slm125m Base Parameters and Internals

LLM Name	Slm125m Base
Repository 🤗	https://huggingface.co/nitin1pawar/slm125m-base
Model Size	125.8m
Required VRAM	0.5 GB
Updated	2026-08-05
Maintainer	nitin1pawar
Model Type	llama
Model Files	0.5 GB
Supported Languages	en
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	1024
Model Max Length	1024
Transformers Version	4.46.3
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<\|pad\|>
Vocabulary Size	16384
Torch Data Type	float32

Rank the Slm125m Base 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

Best Alternatives to Slm125m Base

Best Alternatives	Context / RAM	Downloads	Likes
Slm125mlive Base	1K / 0.5 GB	236	0
Slm125m Live	1K / 0.3 GB	780	0

Note: green Score (e.g. "73.2") means that the model is better than nitin1pawar/slm125m-base.