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SJT 900M by Sakalti

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  Merged Model   Autotrain compatible Base model:finetune:sakalti/sj...   Base model:sakalti/sjt-0.5b   Conversational   Endpoints compatible   Qwen2   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/Sakalti/SJT-900M 

SJT 900M Benchmarks

MMLU Pro: 1.58
GPQA: 0.45
MUSR: 2.97
BBH: 4.3
IFEval: 24.1 vs 88 (so35)-72.6%
MATH Lvl 5: 1.36
LLME Score: 0.23636
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
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SJT 900M Parameters and Internals

LLM NameSJT 900M
Repository 🤗https://huggingface.co/Sakalti/SJT-900M 
Base Model(s)  Sakalti/SJT-0.5B   Sakalti/SJT-0.5B
Merged ModelYes
Model Size0.5b
Required VRAM1.8 GB
Updated2025-06-09
MaintainerSakalti
Model Typeqwen2
Model Files  0.3 GB: 1-of-5   0.5 GB: 2-of-5   0.5 GB: 3-of-5   0.5 GB: 4-of-5   0.0 GB: 5-of-5
Model ArchitectureQwen2ForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.46.3
Tokenizer ClassQwen2Tokenizer
Padding Token<|endoftext|>
Vocabulary Size151936
Torch Data Typefloat16
Errorsreplace
SJT 900M (Sakalti/SJT-900M)

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...PRYMMAL 0.5B FT V4 MUSR Mathis128K / 1 GB621
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....5B FT V4 MUSR ENSEMBLE Mathis128K / 2 GB301
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Note: green Score (e.g. "73.2") means that the model is better than Sakalti/SJT-900M.

Rank the SJT 900M 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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