What are the hardware requirements for Codegemma 1.1 2B AWQ?

Codegemma 1.1 2B AWQ requires approximately 3.1 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 1.1 2B AWQ and how large is it?

Codegemma 1.1 2B AWQ is developed by TechxGenus, a model with 2b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate Codegemma 1.1 2B AWQ?

Codegemma 1.1 2B AWQ is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Codegemma 1.1 2B AWQ by TechxGenus — VRAM 3.1GB, 8K context

Name: Codegemma 1.1 2B AWQ
Author: TechxGenus

Codegemma 1.1 2B AWQ is an open-source language model by TechxGenus. Features: 2b LLM, VRAM: 3.1GB, Context: 8K, License: gemma, Quantized, LLM Explorer Score: 0.12.

4-bit Awq Endpoints compatible Gemma Quantized Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/TechxGenus/codegemma-1.1-2b-AWQ

Codegemma 1.1 2B AWQ Parameters and Internals

Model Type

text-to-text, text-to-code decoder-only

Use Cases

Primary Use Cases:

Code Completion, Code Generation, Code Conversation, Code Education

Limitations:

Large Language Models (LLMs) have limitations based on their training data.

Additional Notes

Models focus on responsible AI development and high-performance open code-focused implementations.

Supported Languages

English (High)

Training Details

Data Sources:

publicly available code repositories, open source mathematics datasets, synthetically generated code

Data Volume:

500 to 1000 billion tokens

Methodology:

FIM Pretrained with fill-in-the-middle (FIM) objective

Hardware Used:

TPUv5e

Safety Evaluation

Methodologies:

structured evaluations, internal red-teaming testing

Risk Categories:

content safety, representational harms, cyber-offence capabilities

Input Output

Input Format:

code prefix and/or suffix for code completion and generation scenarios, or natural language text or prompt

Accepted Modalities:

code, text

Output Format:

fill-in-the-middle code completion, code and natural language

LLM Name	Codegemma 1.1 2B AWQ
Repository 🤗	https://huggingface.co/TechxGenus/codegemma-1.1-2b-AWQ
Base Model(s)	google/codegemma-1.1-2b google/codegemma-1.1-2b
Model Size	2b
Required VRAM	3.1 GB
Updated	2026-04-27
Maintainer	TechxGenus
Model Type	gemma
Model Files	3.1 GB
AWQ Quantization	Yes
Quantization Type	awq
Model Architecture	GemmaForCausalLM
License	gemma
Context Length	8192
Model Max Length	8192
Transformers Version	4.40.0
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>
Vocabulary Size	256000
Torch Data Type	float16

Rank the Codegemma 1.1 2B AWQ 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

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... 2.8 Gemma 2B HQQ 1bit Smashed	8K / 1.3 GB	5	0

Note: green Score (e.g. "73.2") means that the model is better than TechxGenus/codegemma-1.1-2b-AWQ.