What are the hardware requirements for Huozi 7B Sft?

Huozi 7B Sft requires approximately 14.1 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Huozi 7B Sft and how large is it?

Huozi 7B Sft is developed by HIT-SCIR, a model with 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 Huozi 7B Sft?

Huozi 7B Sft is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Huozi 7B Sft by HIT-SCIR — VRAM 14.1GB

Name: Huozi 7B Sft
Author: HIT-SCIR

Huozi 7B Sft is an open-source language model by HIT-SCIR. Features: 7b LLM, VRAM: 14.1GB, LLM Explorer Score: 0.08.

Bloom Endpoints compatible Pytorch Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/HIT-SCIR/huozi-7b-sft

Huozi 7B Sft Parameters and Internals

LLM Name	Huozi 7B Sft
Repository 🤗	https://huggingface.co/HIT-SCIR/huozi-7b-sft
Model Size	7b
Required VRAM	14.1 GB
Updated	2026-08-05
Maintainer	HIT-SCIR
Model Type	bloom
Model Files	14.1 GB 14.1 GB
Model Architecture	BloomForCausalLM
Transformers Version	4.28.1
Tokenizer Class	BloomTokenizer
Padding Token	<pad>
Vocabulary Size	250880
Torch Data Type	float16

Rank the Huozi 7B Sft 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

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Vietcuna 7B V3	0K / 14.2 GB	8	8

Note: green Score (e.g. "73.2") means that the model is better than HIT-SCIR/huozi-7b-sft.