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Qwen3 Next MoE by tiny-random

 »  All LLMs  »  tiny-random  »  Qwen3 Next MoE   URL Share it on

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Qwen3 Next MoE is an open-source language model by tiny-random. Features: 80b LLM, Context: 256K, Instruction-Based, LLM Explorer Score: 0.28.

Base model:finetune:qwen/qwen3... Base model:qwen/qwen3-next-80b...   Conversational   Endpoints compatible   Instruct   Qwen3 next   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/tiny-random/qwen3-next-moe 

Qwen3 Next MoE Benchmarks

LLME Score: 0.27947
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Qwen3 Next MoE (tiny-random/qwen3-next-moe)
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Qwen3 Next MoE Parameters and Internals

LLM NameQwen3 Next MoE
Repository 🤗https://huggingface.co/tiny-random/qwen3-next-moe 
Base Model(s)  Qwen3 Next 80B A3B Instruct   Qwen/Qwen3-Next-80B-A3B-Instruct
Model Size80b
Required VRAM0 GB
Updated2025-12-13
Maintainertiny-random
Model Typeqwen3_next
Instruction-BasedYes
Model Files  0.0 GB
Model ArchitectureQwen3NextForCausalLM
Context Length262144
Model Max Length262144
Transformers Version4.57.0.dev0
Tokenizer ClassQwen2Tokenizer
Padding Token<|endoftext|>
Vocabulary Size151936
Errorsreplace

Best Alternatives to Qwen3 Next MoE

Best Alternatives
Context / RAM
Downloads
Likes
Qwen3 Next 80B A3B Instruct256K / 162.7 GB1398721855
Qwen3 Next 80B A3B Instruct256K / 162.7 GB312673
...wen3 Next 80B A3B Instruct FP8256K / 81.8 GB19060085
...n3 Next 80B A3B Instruct NVFP4256K / 50.7 GB2020637
...ext 80B A3B Instruct Mxfp4 Mlx256K / 42 GB2268
...0B A3B Instruct Int4 AutoRound256K / 42.3 GB2069
... Instruct Int4 Mixed AutoRound256K / 43.1 GB12923
...t 80B A3B Instruct Qx86 Hi Mlx256K / 73.5 GB1272
...t 80B A3B Instruct FP8 Dynamic256K / 80.5 GB184
...Next 80B A3B Instruct Bnb 4bit256K / 42.1 GB2064927
Note: green Score (e.g. "73.2") means that the model is better than tiny-random/qwen3-next-moe.

Rank the Qwen3 Next MoE Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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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