What are the hardware requirements for HamSter 0.2 8.0bpw H8 EXL2?

HamSter 0.2 8.0bpw H8 EXL2 requires approximately 7.4 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 HamSter 0.2 8.0bpw H8 EXL2 and how large is it?

HamSter 0.2 8.0bpw H8 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 HamSter 0.2 8.0bpw H8 EXL2?

HamSter 0.2 8.0bpw H8 EXL2 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

HamSter 0.2 8.0bpw H8 EXL2 by LoneStriker — VRAM 7.4GB, 32K context

Name: HamSter 0.2 8.0bpw H8 EXL2
Author: LoneStriker

HamSter 0.2 8.0bpw H8 EXL2 is an open-source language model by LoneStriker. Features: LLM, VRAM: 7.4GB, Context: 32K, License: apache-2.0, Quantized, LLM Explorer Score: 0.1.

Conversational En Endpoints compatible Exl2 Mistral Quantized Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/LoneStriker/HamSter-0.2-8.0bpw-h8-exl2

HamSter 0.2 8.0bpw H8 EXL2 Parameters and Internals

Model Type

fine-tuned, roleplay

Additional Notes

Fine-tuned with a newer dataset; next version planned to be better

Supported Languages

en (fluent)

Input Output

Input Format:

Mistral prompt template with chat-instruct

Accepted Modalities:

text

Performance Tips:

Use a detailed character card for better performance

LLM Name	HamSter 0.2 8.0bpw H8 EXL2
Repository 🤗	https://huggingface.co/LoneStriker/HamSter-0.2-8.0bpw-h8-exl2
Required VRAM	7.4 GB
Updated	2026-07-22
Maintainer	LoneStriker
Model Type	mistral
Model Files	7.4 GB
Supported Languages	en
Quantization Type	exl2
Model Architecture	MistralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.36.1
Tokenizer Class	LlamaTokenizer
Vocabulary Size	32000
Torch Data Type	float16

Rank the HamSter 0.2 8.0bpw H8 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/HamSter-0.2-8.0bpw-h8-exl2.