What are the hardware requirements for QWEN2.5 14B Instruct MSQ1000 Step1 5 With N Neg Batchsize256 Best Epoch?

QWEN2.5 14B Instruct MSQ1000 Step1 5 With N Neg Batchsize256 Best Epoch requires approximately 29.7 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.

Where can I download or evaluate QWEN2.5 14B Instruct MSQ1000 Step1 5 With N Neg Batchsize256 Best Epoch?

QWEN2.5 14B Instruct MSQ1000 Step1 5 With N Neg Batchsize256 Best Epoch is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

QWEN2.5 14B Instruct MSQ1000 Step1 5 With N Neg Batchsize256 Best Epoch by Glow-AI — VRAM 29.7GB, 32K context

Name: QWEN2.5 14B Instruct MSQ1000 Step1 5 With N Neg Batchsize256 Best Epoch
Author: Glow-AI

QWEN2.5 14B Instruct MSQ1000 Step1 5 With N Neg Batchsize256 Best Epoch is an open-source language model by Glow-AI. Features: 14b LLM, VRAM: 29.7GB, Context: 32K, Instruction-Based, LLM Explorer Score: 0.24.

Instruct Qwen2 Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/Glow-AI/QWEN2.5-14b_instruct-MSQ1000_step1-5_with_N_neg_batchsize256_best_epoch

QWEN2.5 14B Instruct MSQ1000 Step1 5 With N Neg Batchsize256 Best Epoch Parameters and Internals

LLM Name	QWEN2.5 14b Instruct MSQ1000 Step1 5 With N Neg Batchsize256 Best Epoch
Repository 🤗	https://huggingface.co/Glow-AI/QWEN2.5-14b_instruct-MSQ1000_step1-5_with_N_neg_batchsize256_best_epoch
Model Size	14b
Required VRAM	29.7 GB
Updated	2026-07-31
Maintainer	Glow-AI
Model Type	qwen2
Instruction-Based	Yes
Model Files	5.0 GB: 1-of-6 5.0 GB: 2-of-6 5.0 GB: 3-of-6 5.0 GB: 4-of-6 5.0 GB: 5-of-6 4.7 GB: 6-of-6
Model Architecture	Qwen2ForCausalLM
Context Length	32768
Model Max Length	32768
Transformers Version	4.57.6
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	152064
Errors	replace

Rank the QWEN2.5 14B Instruct MSQ1000 Step1 5 With N Neg Batchsize256 Best Epoch 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

Best Alternatives to QWEN2.5 14B Instruct MSQ1000 Step1 5 With N Neg Batchsize256 Best Epoch

Best Alternatives	Context / RAM	Downloads	Likes
Qwen2.5 14B Instruct 1M	986K / 29.6 GB	10388	342
T3Q Qwen2.5 14B V1.0 E3	986K / 29.7 GB	852	28
ZYH LLM Qwen2.5 14B V4	986K / 29.7 GB	27	8
Qwen2.5 14B 1M YOYO V3	986K / 29.7 GB	254	4
...hts Qwen2.5 14B Coder Creative	986K / 29.3 GB	267	0
Qwen2.5 14B YOYO V4	986K / 29.7 GB	20	5
Qwen2.5 14B YOYO Latest V2	986K / 29.7 GB	129	0
ZYH LLM Qwen2.5 14B V3	986K / 29.7 GB	23	8
...14B Instruct 1M GRPO Reasoning	986K / 29.7 GB	11	4
Impish QWEN 14B 1M	986K / 29.7 GB	56	26

Note: green Score (e.g. "73.2") means that the model is better than Glow-AI/QWEN2.5-14b_instruct-MSQ1000_step1-5_with_N_neg_batchsize256_best_epoch.