What are the hardware requirements for Smaugv0.1 AWQ?

Smaugv0.1 AWQ requires approximately 19.3 GB of VRAM and supports a context window of 195K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Smaugv0.1 AWQ and how large is it?

Smaugv0.1 AWQ is developed by LoneStriker, 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 Smaugv0.1 AWQ?

Smaugv0.1 AWQ is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Smaugv0.1 AWQ by LoneStriker — VRAM 19.3GB, 195K context

Name: Smaugv0.1 AWQ
Author: LoneStriker

Smaugv0.1 AWQ is an open-source language model by LoneStriker. Features: 7b LLM, VRAM: 19.3GB, Context: 195K, License: apache-2.0, Quantized, LLM Explorer Score: 0.11.

4-bit Awq Base model:mistralai/mistral-7... Base model:quantized:mistralai... Conversational Endpoints compatible Llama Quantized Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/LoneStriker/Smaugv0.1-AWQ

Smaugv0.1 AWQ Parameters and Internals

LLM Name	Smaugv0.1 AWQ
Repository 🤗	https://huggingface.co/LoneStriker/Smaugv0.1-AWQ
Base Model(s)	mistralai/Mistral-7B-v0.1 mistralai/Mistral-7B-v0.1
Model Size	7b
Required VRAM	19.3 GB
Updated	2026-05-15
Maintainer	LoneStriker
Model Type	llama
Model Files	10.0 GB: 1-of-2 9.3 GB: 2-of-2
AWQ Quantization	Yes
Quantization Type	awq
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	200000
Model Max Length	200000
Transformers Version	4.37.1
Tokenizer Class	LlamaTokenizer
Padding Token	<unk>
Vocabulary Size	64000
Torch Data Type	float16

Rank the Smaugv0.1 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 Smaugv0.1 AWQ

Best Alternatives	Context / RAM	Downloads	Likes
Yarn Llama 2 7B 64K AWQ	64K / 3.9 GB	14	0
Calm2 7B Chat AWQ	32K / 4.4 GB	9	1
Llama 2 7B 32K Instruct AWQ	32K / 3.9 GB	3	2
... SWE Llama 7B Updated 4bit AWQ	16K / 3.9 GB	5	0
Pandalyst 7B V1.2 AWQ	16K / 3.9 GB	6	1
Tora Code 7B V1.0 AWQ	16K / 3.9 GB	5	0
...eechless Tora Code 7B V1.0 AWQ	16K / 3.9 GB	6	1
CodeLlama 7B Instruct AWQ	16K / 3.9 GB	278	4
Pandalyst 7B V1.1 AWQ	16K / 3.9 GB	11	1
CodeLlama 7B AWQ	16K / 3.9 GB	75	6

Note: green Score (e.g. "73.2") means that the model is better than LoneStriker/Smaugv0.1-AWQ.