Gemma 7B Instruct GPTQ 4bit by stan-hua

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Gemma 7B Instruct GPTQ 4bit is an open-source language model by stan-hua. Features: 7b LLM, VRAM: 5.6GB, Context: 8K, Quantized, Instruction-Based, LLM Explorer Score: 0.14.

  Arxiv:1910.09700   4-bit   4bit   Autotrain compatible   Conversational   Endpoints compatible   Gemma   Gptq   Instruct   Quantized   Region:us   Safetensors   Sharded   Tensorflow

Gemma 7B Instruct GPTQ 4bit Benchmarks

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

Gemma 7B Instruct GPTQ 4bit Parameters and Internals

LLM NameGemma 7B Instruct GPTQ 4bit
Repository 🤗https://huggingface.co/stan-hua/Gemma-7B-Instruct-GPTQ-4bit 
Model Size7b
Required VRAM5.6 GB
Updated2025-09-23
Maintainerstan-hua
Model Typegemma
Instruction-BasedYes
Model Files  5.0 GB: 1-of-2   0.6 GB: 2-of-2
GPTQ QuantizationYes
Quantization Typegptq|4bit
Model ArchitectureGemmaForCausalLM
Context Length8192
Model Max Length8192
Transformers Version4.41.1
Tokenizer ClassGemmaTokenizer
Padding Token<pad>
Vocabulary Size256000
Torch Data Typefloat16

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...ruct Malayalam Model Vllm 4bit16K / 5.6 GB70
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Gemma Ko 7B Instruct V0.718K / 17 GB101
Gemma Ko 7B Instruct V0.628K / 17 GB212
X Instruction 7B Ta8K / 17.1 GB50
X Instruction 7B Id8K / 17.1 GB50
RoGemma 7B Instruct8K / 17.1 GB431
X Instruction 7B 10langs8K / 17.1 GB160
Gemma Ko 7B Instruct V0.528K / 17 GB150
Gemma Ko 7B Instruct V0.508K / 17 GB120
Note: green Score (e.g. "73.2") means that the model is better than stan-hua/Gemma-7B-Instruct-GPTQ-4bit.

Rank the Gemma 7B Instruct GPTQ 4bit 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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Release v20260328a