What are the hardware requirements for SJT 1.7B?

SJT 1.7B requires approximately 3.4 GB of VRAM and supports a context window of 128K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed SJT 1.7B and how large is it?

SJT 1.7B is developed by Sakalti, a model with 1.7b 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 SJT 1.7B?

SJT 1.7B is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

SJT 1.7B by Sakalti — VRAM 3.4GB, 128K context

Name: SJT 1.7B
Author: Sakalti

SJT 1.7B is an open-source language model by Sakalti. Features: 1.7b LLM, VRAM: 3.4GB, Context: 128K, License: apache-2.0, LLM Explorer Score: 0.17.

Base model:finetune:sakalti/qw... Base model:sakalti/qwen2.5-tes... Conversational En Endpoints compatible Qwen2 Region:us Safetensors Sft Trl Unsloth

Model Card on HF 🤗: https://huggingface.co/Sakalti/SJT-1.7B

SJT 1.7B Parameters and Internals

LLM Name	SJT 1.7B
Repository 🤗	https://huggingface.co/Sakalti/SJT-1.7B
Base Model(s)	Sakalti/Qwen2.5-test-2 Sakalti/Qwen2.5-test-2
Model Size	1.7b
Required VRAM	3.4 GB
Updated	2026-08-06
Maintainer	Sakalti
Model Type	qwen2
Model Files	3.4 GB
Supported Languages	en
Model Architecture	Qwen2ForCausalLM
License	apache-2.0
Context Length	131072
Model Max Length	131072
Transformers Version	4.47.1
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
Torch Data Type	float16
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
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

Best Alternatives to SJT 1.7B

Best Alternatives	Context / RAM	Downloads	Likes
Saba1.5 1.7B	128K / 3.5 GB	6	0