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CodeGemma 7B by TechxGenus

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CodeGemma 7B is an open-source language model by TechxGenus. Features: 7b LLM, VRAM: 17.1GB, Context: 8K, License: other, LLM Explorer Score: 0.12.

  Code   Endpoints compatible   Gemma   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/TechxGenus/CodeGemma-7b 

CodeGemma 7B Benchmarks

LLME Score: 0.12306
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
CodeGemma 7B (TechxGenus/CodeGemma-7b)
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CodeGemma 7B Parameters and Internals

Model Type 
text generation
Use Cases 
Primary Use Cases:
code generation
Additional Notes 
Model may sometimes make errors, produce misleading contents, or struggle to manage tasks that are not related to coding. It has undergone very limited testing. Additional safety testing should be performed before any real-world deployments.
Training Details 
Data Volume:
0.7 billion high-quality, code-related tokens
Methodology:
Fine-tuning
Hardware Used:
DeepSpeed ZeRO 3, Flash Attention 2
Input Output 
Input Format:
Alpaca instruction format (excluding the system prompt)
Accepted Modalities:
text
Output Format:
text
LLM NameCodeGemma 7B
Repository 🤗https://huggingface.co/TechxGenus/CodeGemma-7b 
Model Size7b
Required VRAM17.1 GB
Updated2026-04-02
MaintainerTechxGenus
Model Typegemma
Model Files  5.0 GB: 1-of-4   5.0 GB: 2-of-4   5.0 GB: 3-of-4   2.1 GB: 4-of-4
Model ArchitectureGemmaForCausalLM
Licenseother
Context Length8192
Model Max Length8192
Transformers Version4.38.1
Tokenizer ClassGemmaTokenizer
Padding Token<eos>
Vocabulary Size256000
Torch Data Typebfloat16

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Context / RAM
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Zephyr 7B Gemma DPO Avg8K / 17.1 GB150
Zephyr 7B Gemma Rpo Avg8K / 17.1 GB60
... Codegemma 2 7B It Alpaca V1.38K / 17.1 GB61
... 7B Finetuned Sft Navarasa 2.08K / 34 GB121822
Zephyr 7B Gemma V0.18K / 17.1 GB302124
Note: green Score (e.g. "73.2") means that the model is better than TechxGenus/CodeGemma-7b.

Rank the CodeGemma 7B 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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