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Gemma 7B It GGUF by alokabhishek

 »  All LLMs  »  alokabhishek  »  Gemma 7B It GGUF   URL Share it on

  127   
  Arxiv:1705.03551   Arxiv:1804.06876   Arxiv:1804.09301   Arxiv:1809.02789   Arxiv:1811.00937   Arxiv:1904.09728   Arxiv:1905.07830   Arxiv:1905.10044   Arxiv:1907.10641   Arxiv:1911.01547   Arxiv:1911.11641   Arxiv:2009.03300   Arxiv:2009.11462   Arxiv:2101.11718   Arxiv:2107.03374   Arxiv:2108.07732   Arxiv:2109.07958   Arxiv:2110.08193   Arxiv:2110.14168   Arxiv:2203.09509   Arxiv:2206.04615   Arxiv:2304.06364   Arxiv:2312.11805   4bit   5bit   Autotrain compatible   Conversational   Endpoints compatible   Gemma   Gemma-7b   Gguf   Google   Q4   Q4 k m   Q5 k m   Quantized   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/alokabhishek/gemma-7b-it-GGUF 

Gemma 7B It GGUF Benchmarks

LLME Score: 0.14637
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Gemma 7B It GGUF (alokabhishek/gemma-7b-it-GGUF)
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Gemma 7B It GGUF Parameters and Internals

Model Type 
text-to-text, decoder-only
Use Cases 
Areas:
Research, Commercial applications
Applications:
Content Creation, Chatbots, NLP Research, Text Summarization
Primary Use Cases:
Question answering, Summarization, Reasoning
Limitations:
Biases or gaps in responses, Challenges with open-ended tasks
Considerations:
Guidelines provided for responsible use.
Additional Notes 
Pre-trained variants and instruction-tuned for diverse text generation tasks.
Supported Languages 
English (Full proficiency)
Training Details 
Data Sources:
Web Documents, Code, Mathematics
Data Volume:
6 trillion tokens
Methodology:
Instruction-tuned on UltraChat dataset using QLoRA
Hardware Used:
Tensor Processing Unit (TPU), TPUv5e
Model Architecture:
Not specified
Safety Evaluation 
Methodologies:
Red-teaming, Human evaluation on safety policies
Findings:
Within acceptable thresholds for meeting internal policies
Risk Categories:
Text-to-Text Content Safety, Text-to-Text Representational Harms, Memorization, Large-scale harm
Responsible Ai Considerations 
Fairness:
Evaluations against WinoBias and BBQ Dataset for representational harms.
Transparency:
This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes.
Accountability:
Guidelines for responsible use with the model provided.
Mitigation Strategies:
Continuous monitoring and de-biasing techniques suggested.
Input Output 
Input Format:
Text string (e.g., questions, prompts)
Accepted Modalities:
text
Output Format:
Generated English-language text
Performance Tips:
Provide well-defined prompts and sufficient context for complex tasks.
LLM NameGemma 7B It GGUF
Repository 🤗https://huggingface.co/alokabhishek/gemma-7b-it-GGUF 
Model Size7b
Required VRAM5.3 GB
Updated2025-10-08
Maintaineralokabhishek
Model Typegemma
Model Files  17.1 GB   5.3 GB   6.1 GB
GGUF QuantizationYes
Quantization Typegguf|q4|q4_k|q5_k
Model ArchitectureGemmaForCausalLM
Licensegemma
Context Length8192
Model Max Length8192
Transformers Version4.38.0.dev0
Tokenizer ClassGemmaTokenizer
Padding Token<pad>
Vocabulary Size256000
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than alokabhishek/gemma-7b-it-GGUF.

Rank the Gemma 7B It GGUF 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 v20241124
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