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T3Q Qwen2.5 7B Instruct It 8 4 0.0001 Cosine 16 16 2epoch by JungZoona

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T3Q Qwen2.5 7B Instruct It 8 4 0.0001 Cosine 16 16 2epoch is an open-source language model by JungZoona. Features: 72b LLM, VRAM: 15.2GB, Context: 32K, License: other, Instruction-Based, LLM Explorer Score: 0.28.

  Autotrain compatible Base model:finetune:qwen/qwen2... Base model:qwen/qwen2.5-72b-in...   Chat   Conversational   En   Endpoints compatible   Instruct   Qwen2   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/JungZoona/T3Q-Qwen2.5-7B-Instruct-it-8-4-0.0001-cosine-16-16-2epoch 

T3Q Qwen2.5 7B Instruct It 8 4 0.0001 Cosine 16 16 2epoch Benchmarks

MMLU Pro: 44.16
GPQA: 8.05
MUSR: 23.7
BBH: 35.42
IFEval: 70.25 vs 88 (so35)-20.2%
MATH Lvl 5: 30.44
LLME Score: 0.28332
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
T3Q Qwen2.5 7B Instruct It 8 4 0.0001 Cosine 16 16 2epoch (JungZoona/T3Q-Qwen2.5-7B-Instruct-it-8-4-0.0001-cosine-16-16-2epoch)
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T3Q Qwen2.5 7B Instruct It 8 4 0.0001 Cosine 16 16 2epoch Parameters and Internals

LLM NameT3Q Qwen2.5 7B Instruct It 8 4 0.0001 Cosine 16 16 2epoch
Repository 🤗https://huggingface.co/JungZoona/T3Q-Qwen2.5-7B-Instruct-it-8-4-0.0001-cosine-16-16-2epoch 
Base Model(s)  Qwen/Qwen2.5-72B-Instruct   Qwen/Qwen2.5-72B-Instruct
Model Size72b
Required VRAM15.2 GB
Updated2025-03-06
MaintainerJungZoona
Model Typeqwen2
Instruction-BasedYes
Model Files  4.9 GB: 1-of-4   4.9 GB: 2-of-4   4.3 GB: 3-of-4   1.1 GB: 4-of-4
Supported Languagesen
Model ArchitectureQwen2ForCausalLM
Licenseother
Context Length32768
Model Max Length32768
Transformers Version4.49.0
Tokenizer ClassQwen2Tokenizer
Padding Token<|endoftext|>
Vocabulary Size152064
Torch Data Typebfloat16
Errorsreplace

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Note: green Score (e.g. "73.2") means that the model is better than JungZoona/T3Q-Qwen2.5-7B-Instruct-it-8-4-0.0001-cosine-16-16-2epoch.

Rank the T3Q Qwen2.5 7B Instruct It 8 4 0.0001 Cosine 16 16 2epoch 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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