What are the hardware requirements for EasyContext 256K Danube2 1.8B?

EasyContext 256K Danube2 1.8B requires approximately 3.7 GB of VRAM and supports a context window of 8K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed EasyContext 256K Danube2 1.8B and how large is it?

EasyContext 256K Danube2 1.8B is developed by PY007, a model with 1.8b 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 EasyContext 256K Danube2 1.8B?

EasyContext 256K Danube2 1.8B is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

EasyContext 256K Danube2 1.8B by PY007 — VRAM 3.7GB, 8K context

Name: EasyContext 256K Danube2 1.8B
Author: PY007

EasyContext 256K Danube2 1.8B is an open-source language model by PY007. Features: 1.8b LLM, VRAM: 3.7GB, Context: 8K, LLM Explorer Score: 0.12.

Endpoints compatible Llama Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/PY007/EasyContext-256K-danube2-1.8b

EasyContext 256K Danube2 1.8B Parameters and Internals

Model Type

context-extrapolated base model

Additional Notes

Memory optimization and training recipes to extrapolate language models' context length to 1 million tokens, with minimal hardware.

Training Details

Methodology:

Finetuned with EasyContext on context length 256K

Context Length:

256000

Input Output

Performance Tips:

You can use this model with context length longer than 4096.

LLM Name	EasyContext 256K Danube2 1.8B
Repository 🤗	https://huggingface.co/PY007/EasyContext-256K-danube2-1.8b
Model Size	1.8b
Required VRAM	3.7 GB
Updated	2026-07-30
Maintainer	PY007
Model Type	llama
Model Files	3.7 GB
Model Architecture	LlamaForCausalLM
Context Length	8192
Model Max Length	8192
Transformers Version	4.39.1
Tokenizer Class	LlamaTokenizer
Vocabulary Size	32000
Torch Data Type	bfloat16

Rank the EasyContext 256K Danube2 1.8B 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 PY007/EasyContext-256K-danube2-1.8b.