What are the hardware requirements for Negentropy Claude Opus 4.7 9B?

Negentropy Claude Opus 4.7 9B requires approximately 18.8 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Negentropy Claude Opus 4.7 9B and how large is it?

Negentropy Claude Opus 4.7 9B is developed by Jackrong, a model with 9b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Negentropy Claude Opus 4.7 9B perform on standard benchmarks?

Negentropy Claude Opus 4.7 9B has the following published scores: LMSYS ELO 1493. Compare against reference models on this page or on the LLM Explorer leaderboards.

Negentropy Claude Opus 4.7 9B by Jackrong — VRAM 18.8GB

Name: Negentropy Claude Opus 4.7 9B
Author: Jackrong

Negentropy Claude Opus 4.7 9B is an open-source language model by Jackrong. Features: 9b LLM, VRAM: 18.8GB, License: apache-2.0, LLM Explorer Score: 0.4, ELO: 1493.

Model Card on HF 🤗: https://huggingface.co/Jackrong/Negentropy-claude-opus-4.7-9B

Negentropy Claude Opus 4.7 9B Benchmarks

LMSys ELO 1493 vs 1272 (so35) 17.4%

^nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

What is the LLM Explorer Rank (Score)

Negentropy Claude Opus 4.7 9B Parameters and Internals

LLM Name	Negentropy Claude Opus 4.7 9B
Repository 🤗	https://huggingface.co/Jackrong/Negentropy-claude-opus-4.7-9B
Model Name	Qwen/Qwen3.5-9B
Base Model(s)	Qwen/Qwen3.5-9B Qwen/Qwen3.5-9B
Model Size	9b
Required VRAM	18.8 GB
Updated	2026-07-22
Maintainer	Jackrong
Model Type	qwen3_5
Model Files	4.9 GB: 1-of-4 5.0 GB: 2-of-4 5.0 GB: 3-of-4 3.9 GB: 4-of-4
Supported Languages	en zh ko ja ru
Model Architecture	Qwen3_5ForConditionalGeneration
License	apache-2.0
Model Max Length	262144
Transformers Version	5.3.0
Tokenizer Class	TokenizersBackend
Padding Token	<\|endoftext\|>
Errors	replace

Rank the Negentropy Claude Opus 4.7 9B 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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