What are the hardware requirements for Smaug 72B V0.1 AWQ?

Smaug 72B V0.1 AWQ requires approximately 41.3 GB of VRAM and supports a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Smaug 72B V0.1 AWQ and how large is it?

Smaug 72B V0.1 AWQ is developed by LoneStriker, a model with 72b 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 Smaug 72B V0.1 AWQ?

Smaug 72B V0.1 AWQ is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Smaug 72B V0.1 AWQ by LoneStriker — VRAM 41.3GB, 32K context

Name: Smaug 72B V0.1 AWQ
Author: LoneStriker

Smaug 72B V0.1 AWQ is an open-source language model by LoneStriker. Features: 72b LLM, VRAM: 41.3GB, Context: 32K, License: mit, Quantized, LLM Explorer Score: 0.1.

4-bit Awq Base model:moreh/momo-72b-lora... Base model:quantized:moreh/mom... Endpoints compatible Llama Lora Quantized Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/LoneStriker/Smaug-72B-v0.1-AWQ

Smaug 72B V0.1 AWQ Parameters and Internals

Training Details

Methodology:

finetuning

LLM Name	Smaug 72B V0.1 AWQ
Repository 🤗	https://huggingface.co/LoneStriker/Smaug-72B-v0.1-AWQ
Base Model(s)	moreh/MoMo-72B-lora-1.8.7-DPO moreh/MoMo-72B-lora-1.8.7-DPO
Model Size	72b
Required VRAM	41.3 GB
Updated	2026-07-22
Maintainer	LoneStriker
Model Type	llama
Model Files	10.0 GB: 1-of-5 10.0 GB: 2-of-5 10.0 GB: 3-of-5 8.8 GB: 4-of-5 2.5 GB: 5-of-5
AWQ Quantization	Yes
Quantization Type	awq
Model Architecture	LlamaForCausalLM
License	mit
Context Length	32768
Model Max Length	32768
Transformers Version	4.37.1
Tokenizer Class	GPT2Tokenizer
Vocabulary Size	152064
LoRA Model	Yes
Torch Data Type	float16

Rank the Smaug 72B V0.1 AWQ 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 Smaug 72B V0.1 AWQ

Best Alternatives	Context / RAM	Downloads	Likes
Smaug 72B V0.1 AWQ	32K / 41.3 GB	4	3
Smaug 72B V0.1 2.4bpw H6 EXL2	32K / 24.5 GB	4	1
2 Pro Math	128K / 141.9 GB	10	0
TW3 JRGL V2	32K / 79.7 GB	16209	0
Le Triomphant ECE TW3	32K / 79.7 GB	16266	5
Smaug 72B V0.1	32K / 144.5 GB	8736	468
ECE TW3 JRGL V5	32K / 159.6 GB	8245	1
MoMo 72B Lora 1.8.7 DPO	32K / 208.5 GB	314	68
Rhea 72B V0.5	32K / 144.5 GB	8194	134
JuliusCesar 72B BeyonderV.0	32K / 74.2 GB	77	0

Note: green Score (e.g. "73.2") means that the model is better than LoneStriker/Smaug-72B-v0.1-AWQ.