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SupaMATH by acbdkk

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  4-bit   4bit   Autotrain compatible Base model:quantized:unsloth/l... Base model:unsloth/llama-3-8b-...   Bitsandbytes   Conversational   En   Endpoints compatible   Llama   Quantized   Qwen2   Region:us   Safetensors   Sft   Sharded   Tensorflow   Trl   Unsloth
Model Card on HF 🤗: https://huggingface.co/acbdkk/SupaMATH 

SupaMATH Benchmarks

LLME Score: 0.19624
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
SupaMATH (acbdkk/SupaMATH)

SupaMATH Parameters and Internals

Model Type 
text-generation-inference, transformers, sft
Additional Notes 
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library. Note: PLEASE use Unsloth. It is 2X FASTER and llama-3-8b can even be fine-tuned FOR FREE in google colab! Additionally, Codellama-34b can be fine-tuned inside AN A100 in google colab! There is simply no excuse not to use Unsloth.
Supported Languages 
en (fluent)
LLM NameSupaMATH
Repository 🤗https://huggingface.co/acbdkk/SupaMATH 
Base Model(s)  Llama 3 8B Bnb 4bit   unsloth/llama-3-8b-bnb-4bit
Model Size8b
Required VRAM5.6 GB
Updated2025-02-22
Maintaineracbdkk
Model Typeqwen2
Model Files  4.5 GB: 1-of-2   1.1 GB: 2-of-2
Supported Languagesen
Quantization Type4bit
Model ArchitectureQwen2ForCausalLM
Licenseapache-2.0
Context Length131072
Model Max Length131072
Transformers Version4.42.3
Tokenizer ClassQwen2Tokenizer
Padding Token<|PAD_TOKEN|>
Vocabulary Size152064
Torch Data Typefloat16
Errorsreplace

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Note: green Score (e.g. "73.2") means that the model is better than acbdkk/SupaMATH.

Rank the SupaMATH 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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241227
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