What are the hardware requirements for Sigmer 150M?

Sigmer 150M requires approximately 0.3 GB of VRAM and supports a context window of 2K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Sigmer 150M and how large is it?

Sigmer 150M is developed by mahiatlinux, a model with 150m 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 Sigmer 150M?

Sigmer 150M is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Sigmer 150M by mahiatlinux — VRAM 0.3GB, 2K context

Name: Sigmer 150M
Author: mahiatlinux

Sigmer 150M is an open-source language model by mahiatlinux. Features: 150m LLM, VRAM: 0.3GB, Context: 2K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.14.

Autotrain compatible Base model:finetune:outeai/lit... Base model:outeai/lite-mistral... Conversational En Endpoints compatible Instruct Mistral Pytorch Region:us Sft Trl Unsloth

Model Card on HF 🤗: https://huggingface.co/mahiatlinux/Sigmer-150M

Sigmer 150M Parameters and Internals

LLM Name	Sigmer 150M
Repository 🤗	https://huggingface.co/mahiatlinux/Sigmer-150M
Base Model(s)	Lite Mistral 150M V2 Instruct OuteAI/Lite-Mistral-150M-v2-Instruct
Model Size	150m
Required VRAM	0.3 GB
Updated	2024-08-22
Maintainer	mahiatlinux
Model Type	mistral
Instruction-Based	Yes
Model Files	0.3 GB
Supported Languages	en
Model Architecture	MistralForCausalLM
License	apache-2.0
Context Length	2048
Model Max Length	2048
Transformers Version	4.43.3
Tokenizer Class	LlamaTokenizer
Padding Token	<unk>
Vocabulary Size	32768
Torch Data Type	float16

Rank the Sigmer 150M 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 Sigmer 150M

Best Alternatives	Context / RAM	Downloads	Likes
Mistral 150M Instruct	32K / 0.7 GB	6	0
Lite Mistral 150M V2 Instruct	2K / 0.6 GB	87	47
BabyHercules 150M	2K / 0.3 GB	24	0

Note: green Score (e.g. "73.2") means that the model is better than mahiatlinux/Sigmer-150M.