What are the hardware requirements for Totem Slm Sft?

Totem Slm Sft requires approximately 8.1 GB of VRAM and supports a context window of 40K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Totem Slm Sft and how large is it?

Totem Slm Sft is developed by giocorte, a model with 4b 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 Totem Slm Sft?

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

Totem Slm Sft by giocorte — VRAM 8.1GB, 40K context

Name: Totem Slm Sft
Author: giocorte

Totem Slm Sft is an open-source language model by giocorte. Features: 4b LLM, VRAM: 8.1GB, Context: 40K, License: apache-2.0, Quantized, LLM Explorer Score: 0.31.

4bit Conversational En Endpoints compatible Quantized Qwen3 Region:us Safetensors Sharded Tensorflow Unsloth

Model Card on HF 🤗: https://huggingface.co/giocorte/totem-slm-sft

Totem Slm Sft Parameters and Internals

LLM Name	Totem Slm Sft
Repository 🤗	https://huggingface.co/giocorte/totem-slm-sft
Base Model(s)	unsloth/qwen3-4b-unsloth-bnb-4bit unsloth/qwen3-4b-unsloth-bnb-4bit
Model Size	4b
Required VRAM	8.1 GB
Updated	2026-09-23
Maintainer	giocorte
Model Type	qwen3
Model Files	5.0 GB: 1-of-2 3.1 GB: 2-of-2
Supported Languages	en
Quantization Type	4bit
Model Architecture	Qwen3ForCausalLM
License	apache-2.0
Context Length	40960
Model Max Length	40960
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|vision_pad\|>
Vocabulary Size	151936
Torch Data Type	bfloat16
Errors	replace

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Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
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Multi-Language Support and Translation

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