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Qwen2.5 Math 7B Instruct by unsloth

 »  All LLMs  »  unsloth  »  Qwen2.5 Math 7B Instruct   URL Share it on

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  Arxiv:2409.12122   Autotrain compatible Base model:finetune:qwen/qwen2... Base model:qwen/qwen2.5-math-7...   Conversational   En   Endpoints compatible   Instruct   Qwen2   Region:us   Safetensors   Sharded   Tensorflow   Unsloth
Model Card on HF 🤗: https://huggingface.co/unsloth/Qwen2.5-Math-7B-Instruct 

Qwen2.5 Math 7B Instruct Benchmarks

LLME Score: 0.27259
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Qwen2.5 Math 7B Instruct (unsloth/Qwen2.5-Math-7B-Instruct)

Qwen2.5 Math 7B Instruct Parameters and Internals

Model Type 
Instruction-tuned, Mathematical, Chat Model
Use Cases 
Areas:
Research, Educational Tools
Primary Use Cases:
Solving mathematical problems in English and Chinese using CoT and TIR
Limitations:
Not recommended for tasks outside mathematical problem-solving
Additional Notes 
The model can be fine-tuned and optimized using Unsloth techniques which reduce memory usage by up to 70%.
Supported Languages 
English (proficient), Chinese (proficient)
Input Output 
Input Format:
Messages in JSON format with roles: system and user
Accepted Modalities:
Text
Output Format:
Text response
LLM NameQwen2.5 Math 7B Instruct
Repository 🤗https://huggingface.co/unsloth/Qwen2.5-Math-7B-Instruct 
Base Model(s)  Qwen/Qwen2.5-Math-7B-Instruct   Qwen/Qwen2.5-Math-7B-Instruct
Model Size7b
Required VRAM15.3 GB
Updated2024-12-22
Maintainerunsloth
Model Typeqwen2
Instruction-BasedYes
Model Files  6.9 GB: 1-of-3   7.0 GB: 2-of-3   1.4 GB: 3-of-3
Supported Languagesen
Model ArchitectureQwen2ForCausalLM
Licenseapache-2.0
Context Length4096
Model Max Length4096
Transformers Version4.44.2
Tokenizer ClassQwen2Tokenizer
Padding Token<|PAD_TOKEN|>
Vocabulary Size152064
Torch Data Typebfloat16
Errorsreplace

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Note: green Score (e.g. "73.2") means that the model is better than unsloth/Qwen2.5-Math-7B-Instruct.

Rank the Qwen2.5 Math 7B Instruct 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  
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Release v20241217
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