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Gemma 1.1 7B It by unsloth

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Model Card on HF 🤗: https://huggingface.co/unsloth/gemma-1.1-7b-it 

Gemma 1.1 7B It Benchmarks

LLME Score: 0.14986
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Gemma 1.1 7B It (unsloth/gemma-1.1-7b-it)
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Gemma 1.1 7B It Parameters and Internals

Model Type 
text generation
Use Cases 
Areas:
research, commercial applications
Applications:
text generation, finetuning experiments
Primary Use Cases:
finetuning models with reduced memory and increased speed
Considerations:
Designed to be used with specific modification and fine-tuning tools.
Additional Notes 
The model can be exported to GGUF, vLLM, or uploaded to Hugging Face.
LLM NameGemma 1.1 7B It
Repository 🤗https://huggingface.co/unsloth/gemma-1.1-7b-it 
Model Size7b
Required VRAM17 GB
Updated2025-09-08
Maintainerunsloth
Model Typegemma
Model Files  5.8 GB: 1-of-3   5.9 GB: 2-of-3   5.3 GB: 3-of-3
Supported Languagesen
Model ArchitectureGemmaForCausalLM
Licenseapache-2.0
Context Length8192
Model Max Length8192
Transformers Version4.44.2
Tokenizer ClassGemmaTokenizer
Padding Token<pad>
Vocabulary Size256000
Torch Data Typebfloat16

Quantized Models of the Gemma 1.1 7B It

Model
Likes
Downloads
VRAM
Gemma 1.1 7B It GGUF1215 GB
Gemma 1.1 7B It GPTQ177 GB
Gemma 1.1 7B It 4bit4185 GB
Gemma 1.1 7B It AWQ057 GB
Gemma 1.1 7B It 4bit155 GB

Best Alternatives to Gemma 1.1 7B It

Best Alternatives
Context / RAM
Downloads
Likes
Kaggle Math Model Gemma V112K / 17.1 GB50
Gemma 1.1 7B It8K / 17.1 GB14858274
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Zephyr 7B Gemma DPO Avg8K / 17.1 GB150
Zephyr 7B Gemma Rpo Avg8K / 17.1 GB60
Zephyr 7B Gemma V0.18K / 17.1 GB1267123
... Codegemma 2 7B It Alpaca V1.38K / 17.1 GB81
... 7B Finetuned Sft Navarasa 2.08K / 34 GB137322
Codegemma 7B It8K / 17.1 GB6712231
Codegemma 7B8K / 17.1 GB4924199
Note: green Score (e.g. "73.2") means that the model is better than unsloth/gemma-1.1-7b-it.

Rank the Gemma 1.1 7B It Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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 v20241124
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