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Humanish Qwen2.5 7B Instruct by HumanLLMs

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  Axolotl Base model:finetune:qwen/qwen2... Base model:qwen/qwen2.5-7b-ins...   Dpo   Generated from trainer   Instruct   Model-index   Qwen2   Region:us   Safetensors   Sharded   Tensorflow   Trl
Model Card on HF 🤗: https://huggingface.co/HumanLLMs/Humanish-Qwen2.5-7B-Instruct 

Humanish Qwen2.5 7B Instruct Benchmarks

MMLU Pro: 37.76
GPQA: 6.49
MUSR: 8.42
BBH: 34.48
IFEval: 72.84 vs 88 (so35)-17.2%
MATH Lvl 5: 50
LLME Score: 0.32543
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Humanish Qwen2.5 7B Instruct (HumanLLMs/Humanish-Qwen2.5-7B-Instruct)

Humanish Qwen2.5 7B Instruct Parameters and Internals

Model Type 
text-generation
Additional Notes 
This model is a fine-tuned version of Qwen/Qwen2.5-7B-Instruct on an unknown dataset.
Training Details 
Context Length:
8192
Hardware Used:
multi-GPU
LLM NameHumanish Qwen2.5 7B Instruct
Repository 🤗https://huggingface.co/HumanLLMs/Humanish-Qwen2.5-7B-Instruct 
Base Model(s)  Qwen/Qwen2.5-7B-Instruct   Qwen/Qwen2.5-7B-Instruct
Model Size7b
Required VRAM15.2 GB
Updated2024-10-15
MaintainerHumanLLMs
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
Model ArchitectureQwen2ForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.45.1
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 HumanLLMs/Humanish-Qwen2.5-7B-Instruct.

Rank the Humanish Qwen2.5 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  
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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