What are the hardware requirements for CodeGemma 7B GPTQ?

CodeGemma 7B GPTQ requires approximately 7.2 GB of VRAM and supports a context window of 8K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed CodeGemma 7B GPTQ and how large is it?

CodeGemma 7B GPTQ is developed by TechxGenus, a model with 7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does CodeGemma 7B GPTQ perform on standard benchmarks?

CodeGemma 7B GPTQ has the following published scores: MMLU 56.1, HumanEval (Python) 52.74. Compare against reference models on this page or on the LLM Explorer leaderboards.

CodeGemma 7B GPTQ by TechxGenus — VRAM 7.2GB, 8K context

Name: CodeGemma 7B GPTQ
Author: TechxGenus

CodeGemma 7B GPTQ is an open-source language model by TechxGenus. Features: 7b LLM, VRAM: 7.2GB, Context: 8K, License: other, Quantized, LLM Explorer Score: 0.13, Arc: 53.8, HellaSwag: 72.5, MMLU: 56.1, GSM8K: 52.5, HumanEval: 52.7.

4-bit Autotrain compatible Code Endpoints compatible Gemma Gptq Quantized Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/TechxGenus/CodeGemma-7b-GPTQ

CodeGemma 7B GPTQ Parameters and Internals

Model Type

text generation

Use Cases

Primary Use Cases:

Text Generation, Code Generation

Limitations:

Errors in non-coding tasks, Misleading content

Considerations:

Model has undergone limited testing; additional safety testing recommended before real-world deployment.

Additional Notes

Model is the GPTQ quantized version and makes use of DeepSpeed ZeRO 3 and Flash Attention 2 for training acceleration.

Training Details

Data Sources:

High-quality, code-related tokens

Data Volume:

0.7 billion tokens

Methodology:

Fine-tuning

Input Output

Input Format:

Alpaca instruction format, excluding the system prompt

Output Format:

Generated text

Performance Tips:

Additional safety testing should be performed before deployment.

LLM Name	CodeGemma 7B GPTQ
Repository 🤗	https://huggingface.co/TechxGenus/CodeGemma-7b-GPTQ
Base Model(s)	Codegemma 7B It google/codegemma-7b-it
Model Size	7b
Required VRAM	7.2 GB
Updated	2025-09-23
Maintainer	TechxGenus
Model Type	gemma
Model Files	7.2 GB
GPTQ Quantization	Yes
Quantization Type	gptq
Model Architecture	GemmaForCausalLM
License	other
Context Length	8192
Model Max Length	8192
Transformers Version	4.39.0.dev0
Tokenizer Class	GemmaTokenizer
Padding Token	<eos>
Vocabulary Size	256000
Torch Data Type	float16

Rank the CodeGemma 7B GPTQ capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

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

Best Alternatives to CodeGemma 7B GPTQ

Best Alternatives	Context / RAM	Downloads	Likes
Gemma 1.1 7B It GPTQ	8K / 7.2 GB	3	1
Codegemma 7B It GPTQ	8K / 7.2 GB	4	1
Google Gemma 7B 4 Bit Gptq	8K / 5.6 GB	12	1
Google Gemma 7B 8 Bit Gptq	8K / 9.5 GB	5	0
Gemma 7B Instruct GPTQ 4bit	8K / 5.6 GB	12	0
Codegemma 1.1 7B It GPTQ	8K / 7.2 GB	4	1
SeaLLM 7B V2.5 4bit	8K / 7.2 GB	6	2
Gemma 7B GPTQ	8K / 7.2 GB	89	0
Gemma 7B It GPTQ	8K / 7.2 GB	5	0
...t Cleaner Gemma 32k Merged 16b	31K / 17.1 GB	7	0

Note: green Score (e.g. "73.2") means that the model is better than TechxGenus/CodeGemma-7b-GPTQ.