What are the hardware requirements for Qwen1.5 MoE Sft Math7k?

Qwen1.5 MoE Sft Math7k requires approximately 57.3 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 Qwen1.5 MoE Sft Math7k and how large is it?

Qwen1.5 MoE Sft Math7k is developed by HectorHe, a model with 14.3b 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 Qwen1.5 MoE Sft Math7k?

Qwen1.5 MoE Sft Math7k is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen1.5 MoE Sft Math7k by HectorHe — VRAM 57.3GB, 8K context

Name: Qwen1.5 MoE Sft Math7k
Author: HectorHe

Qwen1.5 MoE Sft Math7k is an open-source language model by HectorHe. Features: 14.3b LLM, VRAM: 57.3GB, Context: 8K, MoE, LLM Explorer Score: 0.19.

Arxiv:1910.09700 Conversational Endpoints compatible Moe Qwen2 moe Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/HectorHe/Qwen1.5-MOE-sft-math7k

Qwen1.5 MoE Sft Math7k Parameters and Internals

LLM Name	Qwen1.5 MoE Sft Math7k
Repository 🤗	https://huggingface.co/HectorHe/Qwen1.5-MOE-sft-math7k
Model Size	14.3b
Required VRAM	57.3 GB
Updated	2026-05-30
Maintainer	HectorHe
Model Type	qwen2_moe
Model Files	10.0 GB: 1-of-6 10.0 GB: 2-of-6 10.0 GB: 3-of-6 10.0 GB: 4-of-6 10.0 GB: 5-of-6 7.3 GB: 6-of-6
Model Architecture	Qwen2MoeForCausalLM
Context Length	8192
Model Max Length	8192
Transformers Version	4.52.1
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
Torch Data Type	float32
Errors	replace

Rank the Qwen1.5 MoE Sft Math7k 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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...Aux Free Sft Math7k 1e 3 Gamma	8K / 28.7 GB	6	1
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Note: green Score (e.g. "73.2") means that the model is better than HectorHe/Qwen1.5-MOE-sft-math7k.