What are the hardware requirements for Medgemma 4B It Abliterated?

Medgemma 4B It Abliterated requires approximately 6.3 GB of VRAM and supports a context window of 128K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Medgemma 4B It Abliterated and how large is it?

Medgemma 4B It Abliterated is developed by drwlf, a model with 4b 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 Medgemma 4B It Abliterated?

Medgemma 4B It Abliterated is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Medgemma 4B It Abliterated by drwlf — VRAM 6.3GB, 128K context

Name: Medgemma 4B It Abliterated
Author: drwlf

Medgemma 4B It Abliterated is an open-source language model by drwlf. Features: 4b LLM, VRAM: 6.3GB, Context: 128K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.18.

8-bit Abliteration Axolotl Base model:adapter:google/medg... Base model:google/medgemma-4b-... Bitsandbytes Conversational En Gemma3 Instruct Instruction-following Lora Medgemma Medical Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/drwlf/medgemma-4b-it-abliterated

Medgemma 4B It Abliterated Parameters and Internals

LLM Name	Medgemma 4B It Abliterated
Repository 🤗	https://huggingface.co/drwlf/medgemma-4b-it-abliterated
Base Model(s)	google/medgemma-4b-it google/medgemma-4b-it
Model Size	4b
Required VRAM	6.3 GB
Updated	2026-07-14
Maintainer	drwlf
Model Type	gemma3
Instruction-Based	Yes
Model Files	5.0 GB: 1-of-2 1.3 GB: 2-of-2
Supported Languages	en
Model Architecture	Gemma3ForConditionalGeneration
License	apache-2.0
Context Length	131072
Model Max Length	131072
Transformers Version	4.53.2
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>
Vocabulary Size	262208
Torch Data Type	bfloat16

Rank the Medgemma 4B It Abliterated capabilities

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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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