What are the hardware requirements for MUSE News Target?

MUSE News Target requires approximately 27.1 GB of VRAM and supports a context window of 2K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed MUSE News Target and how large is it?

MUSE News Target is developed by swj0419, a model with 6.7b 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 MUSE News Target?

MUSE News Target is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

MUSE News Target by swj0419 — VRAM 27.1GB, 2K context

Name: MUSE News Target
Author: swj0419

MUSE News Target is an open-source language model by swj0419. Features: 6.7b LLM, VRAM: 27.1GB, Context: 2K, LLM Explorer Score: 0.17.

Arxiv:1910.09700 Endpoints compatible Llama Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/muse-bench/MUSE-news_target

MUSE News Target Parameters and Internals

LLM Name	MUSE News Target
Repository 🤗	https://huggingface.co/muse-bench/MUSE-news_target
Model Size	6.7b
Required VRAM	27.1 GB
Updated	2026-08-07
Maintainer	swj0419
Model Type	llama
Model Files	4.8 GB: 1-of-6 4.9 GB: 2-of-6 4.9 GB: 3-of-6 4.9 GB: 4-of-6 4.9 GB: 5-of-6 2.7 GB: 6-of-6
Model Architecture	LlamaForCausalLM
Context Length	2048
Model Max Length	2048
Transformers Version	4.41.1
Vocabulary Size	32000
Torch Data Type	float32

Rank the MUSE News Target 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 muse-bench/MUSE-news_target.