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Gemma 1.1 7B It GGUF by alokabhishek

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Gemma 1.1 7B It GGUF is an open-source language model by alokabhishek. Features: 7b LLM, VRAM: 5.3GB, Context: 8K, License: gemma, Quantized, HF Score: 59.8, LLM Explorer Score: 0.15, Arc: 60, HellaSwag: 76.2, MMLU: 60.4, TruthfulQA: 50.4, WinoGrande: 69.9, GSM8K: 41.8.

  Arxiv:1705.03551   Arxiv:1804.06876   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:2107.03374   Arxiv:2108.07732   Arxiv:2110.08193   Arxiv:2110.14168   Arxiv:2206.04615   Arxiv:2304.06364   Arxiv:2312.11805   4bit   5bit   Autotrain compatible   Conversational   Endpoints compatible   Gemma   Gemma-1.1   Gemma-1.1-7b   Gemma-7b   Gguf   Google   Q4   Q4 k m   Q5 k m   Quantized   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/alokabhishek/gemma-1.1-7b-it-GGUF 

Gemma 1.1 7B It GGUF Benchmarks

ARC: 59.98 vs 96.7 (so35)-38%
HellaSwag: 76.21 vs 95.3 (gpt4)-20%
MMLU: 60.39 vs 88.3 (so35)-31.6%
TruthfulQA: 50.4 vs 59 (gpt4)-14.6%
WinoGrande: 69.93 vs 87.5 (gpt4)-20.1%
GSM8K: 41.77 vs 96.4 (so35)-56.7%
LLME Score: 0.14889
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 GGUF (alokabhishek/gemma-1.1-7b-it-GGUF)
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Gemma 1.1 7B It GGUF Parameters and Internals

Model Type 
text generation, decoder-only
Use Cases 
Areas:
Research, Commercial applications
Applications:
NLP research, Language learning tools, Content creation, Chatbots, Text summarization
Primary Use Cases:
Text generation, Question answering, Summarization
Limitations:
Biases from training data, Factual inaccuracies, Complex open-ended task challenges
Considerations:
Consider using tools for de-biasing and content moderation
Additional Notes 
Gemma is part of the foundation models, offering benefits in Responsible AI development for accessibility and fostering innovation.
Supported Languages 
English (High proficiency)
Training Details 
Data Sources:
Web Documents, Code, Mathematics
Data Volume:
6 trillion tokens
Methodology:
RLHF
Hardware Used:
TPU
Model Architecture:
decoder-only
Safety Evaluation 
Methodologies:
Red-teaming, Structured evaluations
Findings:
Within acceptable thresholds for Google's internal policies
Risk Categories:
Content safety, Representational harms, Memorization, Large-scale harms
Ethical Considerations:
Addressed filters for CSAM, sensitive information, aligning with Google AI principles
Responsible Ai Considerations 
Fairness:
Socio-cultural biases scrutinized, data pre-processing and evaluations reported.
Transparency:
Summary details on architecture, capabilities, and limitations provided.
Accountability:
Google
Mitigation Strategies:
Continuous monitoring, guidelines for content safety
Input Output 
Input Format:
Text string input
Accepted Modalities:
text
Output Format:
Generated English-language text
Performance Tips:
Use "$\tau$" with higher values for creative tasks and lower for educational tasks.
Release Notes 
Version:
1.1
Notes:
Improvement over the original model, using RLHF leading to quality improvements. Addressed a bug for multi-turn conversations.
LLM NameGemma 1.1 7B It GGUF
Repository 🤗https://huggingface.co/alokabhishek/gemma-1.1-7b-it-GGUF 
Base Model(s)  unsloth/gemma-1.1-7b-it   unsloth/gemma-1.1-7b-it
Model Size7b
Required VRAM5.3 GB
Updated2025-09-23
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 Version4.38.1
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-1.1-7b-it-GGUF.

Rank the Gemma 1.1 7B It GGUF 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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