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Qwen2.5 0.5B Test Ft by KingNish

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Qwen2.5 0.5B Test Ft is an open-source language model by KingNish. Features: 0.5b LLM, VRAM: 1GB, Context: 32K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.2.

  Ara   Autotrain compatible Base model:finetune:qwen/qwen2... Base model:qwen/qwen2.5-0.5b-i...   Conversational   Deu   Endpoints compatible   Eng   Fra   Instruct   Ita   Jpn   Kor   Por   Qwen2   Region:us   Rus   Safetensors   Sft   Spa   Tha   Trl   Unsloth   Vie   Zho
Model Card on HF 🤗: https://huggingface.co/KingNish/Qwen2.5-0.5b-Test-ft 

Qwen2.5 0.5B Test Ft Benchmarks

MMLU Pro: 7.65
GPQA: 1.79
MUSR: 1.43
BBH: 6.06
IFEval: 26.71 vs 88 (so35)-69.6%
MATH Lvl 5: 3.55
LLME Score: 0.19745
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Qwen2.5 0.5B Test Ft (KingNish/Qwen2.5-0.5b-Test-ft)
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Qwen2.5 0.5B Test Ft Parameters and Internals

Model Type 
text-generation-inference, transformers
Additional Notes 
Developers plan to further train the model on larger datasets.
Supported Languages 
en (fluent)
Training Details 
Data Sources:
Magpie 300k Dataset
Data Volume:
12,800 rows
Model Architecture:
Compact language model architecture employing unsloth for faster training.
Input Output 
Input Format:
Messages structured in a system/user prompt format; supports chat templates.
Accepted Modalities:
text
Output Format:
Text responses
LLM NameQwen2.5 0.5B Test Ft
Repository 🤗https://huggingface.co/KingNish/Qwen2.5-0.5b-Test-ft 
Base Model(s)  Qwen/Qwen2.5-0.5B-Instruct   Qwen/Qwen2.5-0.5B-Instruct
Model Size0.5b
Required VRAM1 GB
Updated2025-09-23
MaintainerKingNish
Model Typeqwen2
Instruction-BasedYes
Model Files  1.0 GB
Supported Languagesen
Model ArchitectureQwen2ForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.43.1
Tokenizer ClassQwen2Tokenizer
Padding Token<|endoftext|>
Vocabulary Size151936
Torch Data Typefloat16
Errorsreplace

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Note: green Score (e.g. "73.2") means that the model is better than KingNish/Qwen2.5-0.5b-Test-ft.

Rank the Qwen2.5 0.5B Test Ft 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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