What are the hardware requirements for SJT 2.4B?

SJT 2.4B requires approximately 5 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 2.4B and how large is it?

SJT 2.4B is developed by Sakalti, a model with 1.5b 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 2.4B?

SJT 2.4B is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

SJT 2.4B by Sakalti — VRAM 5GB, 128K context

Name: SJT 2.4B
Author: Sakalti

SJT 2.4B is an open-source language model by Sakalti. Features: 1.5b LLM, VRAM: 5GB, Context: 128K, Merged, LLM Explorer Score: 0.19.

Merged Model Base model:finetune:sakalti/sj... Base model:sakalti/sjt-1.5b-al... Conversational Endpoints compatible Qwen2 Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/Sakalti/SJT-2.4B

SJT 2.4B Parameters and Internals

LLM Name	SJT 2.4B
Repository 🤗	https://huggingface.co/Sakalti/SJT-2.4B
Base Model(s)	Sakalti/SJT-1.5B-Alpha Sakalti/SJT-1.5B-Alpha
Merged Model	Yes
Model Size	1.5b
Required VRAM	5 GB
Updated	2026-07-31
Maintainer	Sakalti
Model Type	qwen2
Model Files	0.5 GB: 1-of-10 0.5 GB: 2-of-10 0.5 GB: 3-of-10 0.5 GB: 4-of-10 0.5 GB: 5-of-10 0.5 GB: 6-of-10 0.5 GB: 7-of-10 0.5 GB: 8-of-10 0.5 GB: 9-of-10 0.5 GB: 10-of-10
Model Architecture	Qwen2ForCausalLM
Context Length	131072
Model Max Length	131072
Transformers Version	4.46.3
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
Torch Data Type	float16
Errors	replace

Rank the SJT 2.4B 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 SJT 2.4B

Best Alternatives	Context / RAM	Downloads	Likes
Qwen2.5 1.5B 1.3M Stretched	1280K / 3.1 GB	11	0
ReaderLM V2	500K / 3.1 GB	35356	804
Reader Lm 1.5B	250K / 3.1 GB	385	608
DeepSeek R1 Distill Qwen 1.5B	128K / 3.5 GB	600051	1545
Ko R1 1.5B Preview	128K / 7.1 GB	8	9
Qwen2.5 1.5B	128K / 3.1 GB	863295	203
VibeThinker 1.5B	128K / 3.5 GB	3417	552
Merge Cord V1	128K / 3.1 GB	155	1
...FT Tulu3 Decontaminated Masked	128K / 3.1 GB	167	0
Qwen2 1.5B	128K / 3.1 GB	195380	102

Note: green Score (e.g. "73.2") means that the model is better than Sakalti/SJT-2.4B.