What are the hardware requirements for Smaug 34B V0.1 2.65bpw H6 EXL2?

Smaug 34B V0.1 2.65bpw H6 EXL2 requires approximately 12.4 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 Smaug 34B V0.1 2.65bpw H6 EXL2 and how large is it?

Smaug 34B V0.1 2.65bpw H6 EXL2 is developed by LoneStriker, 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 Smaug 34B V0.1 2.65bpw H6 EXL2?

Smaug 34B V0.1 2.65bpw H6 EXL2 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Smaug 34B V0.1 2.65bpw H6 EXL2 by LoneStriker — VRAM 12.4GB, 195K context

Name: Smaug 34B V0.1 2.65bpw H6 EXL2
Author: LoneStriker

Smaug 34B V0.1 2.65bpw H6 EXL2 is an open-source language model by LoneStriker. Features: 34b LLM, VRAM: 12.4GB, Context: 195K, License: other, Quantized, Merged, LLM Explorer Score: 0.11.

Merged Model Base model:finetune:jondurbin/... Base model:jondurbin/bagel-34b... Conversational Endpoints compatible Exl2 Llama Quantized Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/LoneStriker/Smaug-34B-v0.1-2.65bpw-h6-exl2

Smaug 34B V0.1 2.65bpw H6 EXL2 Parameters and Internals

Additional Notes

This model is a finetune of jondurbin's bagel model, trained with new datasets and a new technique to be shared soon. There is no merging.

LLM Name	Smaug 34B V0.1 2.65bpw H6 EXL2
Repository 🤗	https://huggingface.co/LoneStriker/Smaug-34B-v0.1-2.65bpw-h6-exl2
Base Model(s)	jondurbin/bagel-34b-v0.2 jondurbin/bagel-34b-v0.2
Merged Model	Yes
Model Size	34b
Required VRAM	12.4 GB
Updated	2026-05-05
Maintainer	LoneStriker
Model Type	llama
Model Files	8.6 GB: 1-of-2 3.8 GB: 2-of-2
Quantization Type	exl2
Model Architecture	LlamaForCausalLM
License	other
Context Length	200000
Model Max Length	200000
Transformers Version	4.36.2
Tokenizer Class	LlamaTokenizer
Padding Token	<unk>
Vocabulary Size	64000
Torch Data Type	bfloat16

Rank the Smaug 34B V0.1 2.65bpw H6 EXL2 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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...mi Raw Toxic 2702 4.65bpw EXL2	195K / 20.8 GB	7	2
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Opus V1 34B 5.0bpw H6 EXL2	195K / 22.3 GB	5	2
Opus V1 34B 8.0bpw H8 EXL2	195K / 34.8 GB	7	1
Opus V1 34B 4.0bpw H6 EXL2	195K / 18 GB	5	1
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Note: green Score (e.g. "73.2") means that the model is better than LoneStriker/Smaug-34B-v0.1-2.65bpw-h6-exl2.