What are the hardware requirements for LWM Text Chat 128K 6.0bpw H6 EXL2?

LWM Text Chat 128K 6.0bpw H6 EXL2 requires approximately 5.2 GB of VRAM and supports a context window of 128K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed LWM Text Chat 128K 6.0bpw H6 EXL2 and how large is it?

LWM Text Chat 128K 6.0bpw H6 EXL2 is developed by LoneStriker. 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 LWM Text Chat 128K 6.0bpw H6 EXL2?

LWM Text Chat 128K 6.0bpw H6 EXL2 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

LWM Text Chat 128K 6.0bpw H6 EXL2 by LoneStriker — VRAM 5.2GB, 128K context

Name: LWM Text Chat 128K 6.0bpw H6 EXL2
Author: LoneStriker

LWM Text Chat 128K 6.0bpw H6 EXL2 is an open-source language model by LoneStriker. Features: LLM, VRAM: 5.2GB, Context: 128K, Quantized, LLM Explorer Score: 0.11.

Exl2 Llama Pytorch Quantized Region:us

Model Card on HF 🤗: https://huggingface.co/LoneStriker/LWM-Text-Chat-128K-6.0bpw-h6-exl2

LWM Text Chat 128K 6.0bpw H6 EXL2 Parameters and Internals

Model Type

auto-regressive language model

Training Details

Data Sources:

Books3

Data Volume:

92K subset of Books3 documents

Model Architecture:

transformer architecture

LLM Name	LWM Text Chat 128K 6.0bpw H6 EXL2
Repository 🤗	https://huggingface.co/LoneStriker/LWM-Text-Chat-128K-6.0bpw-h6-exl2
Required VRAM	5.2 GB
Updated	2026-08-08
Maintainer	LoneStriker
Model Type	llama
Model Files	5.2 GB
Quantization Type	exl2
Model Architecture	LlamaForCausalLM
Context Length	131072
Model Max Length	131072
Transformers Version	4.29.2
Tokenizer Class	LlamaTokenizer
Beginning of Sentence Token	<s>
End of Sentence Token	</s>
Unk Token	<unk>
Vocabulary Size	32000
Torch Data Type	float16

Rank the LWM Text Chat 128K 6.0bpw 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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Note: green Score (e.g. "73.2") means that the model is better than LoneStriker/LWM-Text-Chat-128K-6.0bpw-h6-exl2.