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Gemma 2B Inst Aipi by rootsec1

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Gemma 2B Inst Aipi is an open-source language model by rootsec1. Features: 2b LLM, VRAM: 5.1GB, Context: 8K, License: apache-2.0, Quantized, Instruction-Based, LLM Explorer Score: 0.13.

  Conversational Dataset:sail/symbolic-instruct...   Endpoints compatible   Gemma   Gguf   Instruct   Quantized   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/rootsec1/gemma-2B-inst-aipi 

Gemma 2B Inst Aipi Benchmarks

LLME Score: 0.12677
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Gemma 2B Inst Aipi (rootsec1/gemma-2B-inst-aipi)
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Gemma 2B Inst Aipi Parameters and Internals

Model Type 
Causal Language Model
Use Cases 
Areas:
code generation, automated reasoning, AI instruction following
Additional Notes 
The model is fine-tuned for symbolic instruction interpretation and execution.
Training Details 
Data Sources:
sail/symbolic-instruction-tuning
Methodology:
Fine-tuning
Input Output 
Input Format:
Encoded symbolic instructions
Output Format:
Decoded textual instructions
LLM NameGemma 2B Inst Aipi
Repository 🤗https://huggingface.co/rootsec1/gemma-2B-inst-aipi 
Model Size2b
Required VRAM5.1 GB
Updated2026-05-11
Maintainerrootsec1
Model Typegemma
Instruction-BasedYes
Model Files  10.0 GB   5.0 GB: 1-of-2   0.1 GB: 2-of-2
GGUF QuantizationYes
Quantization Typegguf
Model ArchitectureGemmaForCausalLM
Licenseapache-2.0
Context Length8192
Model Max Length8192
Transformers Version4.38.0.dev0
Tokenizer ClassGemmaTokenizer
Padding Token<pad>
Vocabulary Size256000
Torch Data Typebfloat16

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Context / RAM
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...Code 2B Instruct Finetune Test8K / 5.1 GB280
Gita SastraGPT V18K / 5.1 GB191
Pandas Tutor Gemma 2B8K / 5.1 GB81
... 2B Finetuned Sft Navarasa 2.08K / 10 GB100626
...ma Code Instruct Finetune Test8K / 5.1 GB50
Gemma 2B Sft Preview8K / 5 GB80
Note: green Score (e.g. "73.2") means that the model is better than rootsec1/gemma-2B-inst-aipi.

Rank the Gemma 2B Inst Aipi 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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