What are the hardware requirements for D300e373 Bcd7 494e 93e3 A505aaf10d79?

D300e373 Bcd7 494e 93e3 A505aaf10d79 requires approximately 16.4 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 D300e373 Bcd7 494e 93e3 A505aaf10d79 and how large is it?

D300e373 Bcd7 494e 93e3 A505aaf10d79 is developed by johngreendr1, a model with 8.2b 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 D300e373 Bcd7 494e 93e3 A505aaf10d79?

D300e373 Bcd7 494e 93e3 A505aaf10d79 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

D300e373 Bcd7 494e 93e3 A505aaf10d79 by johngreendr1 — VRAM 16.4GB, 32K context

Name: D300e373 Bcd7 494e 93e3 A505aaf10d79
Author: johngreendr1

D300e373 Bcd7 494e 93e3 A505aaf10d79 is an open-source language model by johngreendr1. Features: 8.2b LLM, VRAM: 16.4GB, Context: 32K, LLM Explorer Score: 0.18.

Qwen3 Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/johngreendr1/d300e373-bcd7-494e-93e3-a505aaf10d79

D300e373 Bcd7 494e 93e3 A505aaf10d79 Parameters and Internals

LLM Name	D300e373 Bcd7 494e 93e3 A505aaf10d79
Repository 🤗	https://huggingface.co/johngreendr1/d300e373-bcd7-494e-93e3-a505aaf10d79
Model Size	8.2b
Required VRAM	16.4 GB
Updated	2026-07-02
Maintainer	johngreendr1
Model Type	qwen3
Model Files	4.9 GB: 1-of-4 4.9 GB: 2-of-4 5.0 GB: 3-of-4 1.6 GB: 4-of-4
Model Architecture	Qwen3ForCausalLM
Context Length	32768
Model Max Length	32768
Transformers Version	4.51.3
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
Torch Data Type	bfloat16
Errors	replace

Rank the D300e373 Bcd7 494e 93e3 A505aaf10d79 capabilities

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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 johngreendr1/d300e373-bcd7-494e-93e3-a505aaf10d79.