What are the hardware requirements for StructLM 34B GGUF?

StructLM 34B GGUF requires approximately 12.5 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed StructLM 34B GGUF and how large is it?

StructLM 34B GGUF is developed by MaziyarPanahi, a model with 34b 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 StructLM 34B GGUF?

StructLM 34B GGUF is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

StructLM 34B GGUF by MaziyarPanahi — VRAM 12.5GB

Name: StructLM 34B GGUF
Author: MaziyarPanahi

StructLM 34B GGUF is an open-source language model by MaziyarPanahi. Features: 34b LLM, VRAM: 12.5GB, Quantized, LLM Explorer Score: 0.11.

Arxiv:2402.16671 2-bit 3-bit 4-bit 5-bit 6-bit 8-bit Autotrain compatible Base model:quantized:tiger-lab... Base model:tiger-lab/structlm-... Dataset:tiger-lab/skginstruct En Endpoints compatible Gguf Llama Mistral Quantized Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/MaziyarPanahi/StructLM-34B-GGUF

StructLM 34B GGUF Parameters and Internals

Model Type

text-generation

Additional Notes

Model originally developed by TIGER-Lab, quantized by Maziyar Panahi.

LLM Name	StructLM 34B GGUF
Repository 🤗	https://huggingface.co/MaziyarPanahi/StructLM-34B-GGUF
Model Name	StructLM-34B-GGUF
Model Creator	TIGER-Lab
Base Model(s)	StructLM 34B TIGER-Lab/StructLM-34B
Model Size	34b
Required VRAM	12.5 GB
Updated	2026-08-07
Maintainer	MaziyarPanahi
Model Type	mistral
Model Files	12.5 GB 17.8 GB 16.3 GB 14.6 GB 20.2 GB 19.2 GB 23.8 GB 23.2 GB 27.7 GB 35.9 GB
GGUF Quantization	Yes
Quantization Type	gguf
Model Architecture	AutoModel

Rank the StructLM 34B GGUF 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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Note: green Score (e.g. "73.2") means that the model is better than MaziyarPanahi/StructLM-34B-GGUF.