diff --git a/science/linux-ai-ml-env/Makefile b/science/linux-ai-ml-env/Makefile
index 6080a6c0b183..46b9945b2f0f 100644
--- a/science/linux-ai-ml-env/Makefile
+++ b/science/linux-ai-ml-env/Makefile
@@ -1,110 +1,109 @@
 PORTNAME=	ai-ml-env
-PORTVERSION=	1.0.0
+PORTVERSION=	1.0.1
 CATEGORIES=	science linux
 MASTER_SITES=	https://arrowd.name/
 PKGNAMEPREFIX=	linux-
 DISTFILES=	miniconda3.tar.gz
-DIST_SUBDIR=	${PORTNAME}
+DIST_SUBDIR=	${PORTNAME}-${PORTVERSION}
 
-MAINTAINER=	voxnod@gmail.com
+MAINTAINER=	arrowd@FreeBSD.org
 COMMENT=	Linux Python environment for running Stable Diffusion models and PyTorch CUDA examples
 
 ONLY_FOR_ARCHS=	amd64
 
 LICENSE=	MULTI
 LICENSE_NAME=	Multiple licenses
 LICENSE_TEXT=	The package contains multiple packages with different licenses
 LICENSE_PERMS=	dist-mirror pkg-mirror auto-accept
 
-FETCH_DEPENDS=	linux_base-rl9>=9.2:emulators/linux_base-rl9
 BUILD_DEPENDS=	linux-rl9-devtools>=0:devel/linux-rl9-devtools
 RUN_DEPENDS=	linux-nvidia-libs>=0:x11/linux-nvidia-libs \
 		linux-rl9-python${PYTHON_SUFFIX}>=0:lang/linux-rl9-python3 \
 		nvidia-driver>=0:x11/nvidia-driver
 
 USES=		linux:rl9
 USE_LINUX_PREFIX=	yes
 USE_GITHUB=	nodefault
 GH_ACCOUNT=	pytorch:examples
 GH_PROJECT=	examples:examples
 GH_TAGNAME=	5dfeb46902baf444010f2f54bcf4dfbea109ae4d:examples
 
 WRKSRC=		${WRKSRC_examples}
 MAKEDIST_WRKSRC=${WRKDIR}/makedist
 
 SUB_FILES=	ai-ml-env-bash \
 		ai-ml-env-python
 SUB_LIST=	LOCALBASE=${LOCALBASE} \
 		LINUXBASE=${LINUXBASE}
 
 PLIST_SUB+=     PYTHON_SUFFIX=${PYTHON_SUFFIX} \
 		PYTHON_VER=${PYTHON_VER}
 
-CONDA_VERSION=	py39_25.1.1-2
+CONDA_VERSION=	py${PYTHON_SUFFIX}_25.9.1-1
 CONDA_SCRIPT=	Miniconda3-${CONDA_VERSION}-Linux-x86_64.sh
 PYTHON_SUFFIX=	39
 PYTHON_VER=	3.9
 DATA_DIR=	${LOCALBASE}/share/${PORTNAME}
 
 makedist:
 	@${MKDIR} ${MAKEDIST_WRKSRC}
 	@${ECHO_MSG} "Setting up Miniconda 'pytorch' environment"
 	cd ${MAKEDIST_WRKSRC} && \
 		${FETCH_CMD} -a -v https://repo.anaconda.com/miniconda/${CONDA_SCRIPT}
 
 	${SETENV} HOME=${MAKEDIST_WRKSRC} CONDA_OVERRIDE_GLIBC=2.28 \
 		${SH} ${MAKEDIST_WRKSRC}/${CONDA_SCRIPT} -b -s
 
 	${SETENV} HOME=${MAKEDIST_WRKSRC} ${SH} -c \
 		"${MAKEDIST_WRKSRC}/miniconda3/bin/conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/main && \
 		${MAKEDIST_WRKSRC}/miniconda3/bin/conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/r && \
 		${MAKEDIST_WRKSRC}/miniconda3/bin/conda create --name pytorch python=${PYTHON_VER} -y"
 
 	${LINUXBASE}/bin/bash -c \
 		"source ${MAKEDIST_WRKSRC}/miniconda3/etc/profile.d/conda.sh && \
 		conda activate pytorch && \
 		pip install -r ${FILESDIR}/requirements.txt"
 
 	cd ${MAKEDIST_WRKSRC} && \
 		tar -czf /tmp/miniconda3.tar.gz miniconda3
 	@cd /tmp \
 		&& sha256 miniconda3.tar.gz \
 		&& ${ECHO_CMD} -n "SIZE (${DIST_SUBDIR}/miniconda3.tar.gz) = " \
 		&& ${STAT} -f %z miniconda3.tar.gz
 
 do-build:
 	/compat/linux/bin/cc --sysroot=/compat/linux -m64 -std=c99 -Wall -ldl -fPIC -shared -o ${WRKDIR}/dummy-uvm.so ${FILESDIR}/uvm_ioctl_override.c
 
 do-install:
 	@${FIND} ${WRKSRC} -name '*.orig' -delete
 	# Install Linux Python packages
 	${MKDIR} ${STAGEDIR}${PREFIX}/usr/bin
 	${FIND} ${WRKDIR}/miniconda3/envs/pytorch/bin -type f -exec ${CP} {} ${STAGEDIR}${PREFIX}/usr/bin \;
 	${MKDIR} ${STAGEDIR}${PREFIX}/usr/lib/python${PYTHON_VER}
 	${CP} -r ${WRKDIR}/miniconda3/envs/pytorch/lib/python${PYTHON_VER}/site-packages ${STAGEDIR}${PREFIX}/usr/lib/python${PYTHON_VER}
 	for item in `cat ${FILESDIR}/redundant-items.txt`; do \
 		${RM} -r ${STAGEDIR}${PREFIX}/usr/$$item; \
 	done
 	${FIND} ${STAGEDIR}${PREFIX}/usr/bin -type f -exec ${REINPLACE_CMD} -i '' 's|^#!.*/miniconda3/envs/pytorch/bin/python|#!/bin/python3|' {} +
 	# Install Shkhln's lib
 	${MKDIR} ${STAGEDIR}${DATADIR}
 	${INSTALL_LIB} ${WRKDIR}/dummy-uvm.so ${STAGEDIR}${DATADIR}
 	# Install Pytorch examples
 	${MKDIR} ${STAGEDIR}${DATA_DIR}/pytorch-examples
 	cd ${WRKSRC} && \
 		${COPYTREE_SHARE} . ${STAGEDIR}${DATA_DIR}/pytorch-examples
 	${RM} -r ${STAGEDIR}${DATA_DIR}/pytorch-examples/.github
 	${INSTALL_SCRIPT} ${WRKSRC}/run_python_examples.sh ${STAGEDIR}${DATA_DIR}/pytorch-examples
 	# Install Stable Diffusion sample
 	${INSTALL_DATA} ${FILESDIR}/stable-diffusion-sample.py ${STAGEDIR}${DATA_DIR}
 	# Install demonstration scripts
 	${MKDIR} ${STAGEDIR}${LOCALBASE}/bin
 	${INSTALL_SCRIPT} ${WRKDIR}/ai-ml-env-bash ${STAGEDIR}${LOCALBASE}/bin/ai-ml-env-bash
 	${INSTALL_SCRIPT} ${WRKDIR}/ai-ml-env-python ${STAGEDIR}${LOCALBASE}/bin/ai-ml-env-python
 
 post-install:
 	@${FIND} ${STAGEDIR}${PREFIX}/usr/lib/python${PYTHON_VER} -type f | sed -e 's|${STAGEDIR}${PREFIX}/||' >> ${TMPPLIST}
 	@${FIND} ${STAGEDIR}${DATA_DIR}/pytorch-examples -type f | sed -e 's|${STAGEDIR}||' >> ${TMPPLIST}
 	@${FIND} ${STAGEDIR}${DATA_DIR} -type d | sed -e 's|${STAGEDIR}|@dir |' >> ${TMPPLIST}
 
 .include <bsd.port.mk>
diff --git a/science/linux-ai-ml-env/distinfo b/science/linux-ai-ml-env/distinfo
index eba6ad95a915..ff6dd1918a87 100644
--- a/science/linux-ai-ml-env/distinfo
+++ b/science/linux-ai-ml-env/distinfo
@@ -1,5 +1,5 @@
-TIMESTAMP = 1742925172
-SHA256 (ai-ml-env/miniconda3.tar.gz) = 1d9eb42dd753f462f4ccd82ab7716561b90274eeaa45254fd50b879bb09537b7
-SIZE (ai-ml-env/miniconda3.tar.gz) = 3511877484
-SHA256 (ai-ml-env/pytorch-examples-5dfeb46902baf444010f2f54bcf4dfbea109ae4d_GH0.tar.gz) = a024b134dfd1edba649289e551a0cd85bd22424dd76df4303b280e20757a602c
-SIZE (ai-ml-env/pytorch-examples-5dfeb46902baf444010f2f54bcf4dfbea109ae4d_GH0.tar.gz) = 7298483
+TIMESTAMP = 1742925173
+SHA256 (ai-ml-env-1.0.1/miniconda3.tar.gz) = caabf9a12acd61912a10a32e690283137e15dff2617fdd701ff9f695dc7a4d5c
+SIZE (ai-ml-env-1.0.1/miniconda3.tar.gz) = 3584426356
+SHA256 (ai-ml-env-1.0.1/pytorch-examples-5dfeb46902baf444010f2f54bcf4dfbea109ae4d_GH0.tar.gz) = a024b134dfd1edba649289e551a0cd85bd22424dd76df4303b280e20757a602c
+SIZE (ai-ml-env-1.0.1/pytorch-examples-5dfeb46902baf444010f2f54bcf4dfbea109ae4d_GH0.tar.gz) = 7298483
diff --git a/science/linux-ai-ml-env/files/redundant-items.txt b/science/linux-ai-ml-env/files/redundant-items.txt
index 54795ebaf9d0..3563b304e27b 100644
--- a/science/linux-ai-ml-env/files/redundant-items.txt
+++ b/science/linux-ai-ml-env/files/redundant-items.txt
@@ -1,41 +1,49 @@
 bin/2to3-3.9
+bin/bunzip2
+bin/bzcat
+bin/bzdiff
+bin/bzgrep
+bin/bzip2
+bin/bzip2recover
+bin/bzmore
 bin/c_rehash
 bin/clear
 bin/idle3.9
 bin/infocmp
 bin/lzmadec
 bin/lzmainfo
 bin/ncursesw6-config
 bin/openssl
 bin/pip
 bin/pip3
 bin/pydoc3.9
 bin/python3.9
 bin/python3.9-config
 bin/sqlite3
 bin/sqlite3_analyzer
 bin/tabs
 bin/tclsh8.6
 bin/tic
 bin/toe
 bin/tput
 bin/tset
 bin/wheel
 bin/wish8.6
 bin/x86_64-conda-linux-gnu-ld
+bin/xmlwf
 bin/xz
 bin/xzdec
 bin/xzdiff
 bin/xzgrep
 bin/xzless
 bin/xzmore
 lib/python3.9/site-packages/_distutils_hack/
 lib/python3.9/site-packages/distutils-precedence.pth
 lib/python3.9/site-packages/pip-25.0.dist-info/
 lib/python3.9/site-packages/pip/
 lib/python3.9/site-packages/pkg_resources/
 lib/python3.9/site-packages/README.txt
 lib/python3.9/site-packages/setuptools-75.8.0-py3.9.egg-info/
 lib/python3.9/site-packages/setuptools/
 lib/python3.9/site-packages/wheel-0.45.1.dist-info/
 lib/python3.9/site-packages/wheel/
diff --git a/science/linux-ai-ml-env/pkg-message b/science/linux-ai-ml-env/pkg-message
index 4c744748c5c5..e8f01bd37e44 100644
--- a/science/linux-ai-ml-env/pkg-message
+++ b/science/linux-ai-ml-env/pkg-message
@@ -1,32 +1,32 @@
 [
 { type: install
   message: <<EOM
 To utilize CUDA support for PyTorch, both https://github.com/shkhln 's
 compatibility library preloading and Linux compatibility layer usage are
 required, as demonstrated in the provided demonstration scripts.
 
 To play with Stable Diffusion copy it to a user-writable directory, edit at will
 and launch it via ai-ml-env-python:
 
 	cp /usr/local/share/ai-ml-env/stable-diffusion-sample.py ~/
-	ai-ml-env-python stable-diffusion-sample.py
+	ai-ml-env-python ~/stable-diffusion-sample.py
 
 This will download data packages into ~/.cache/huggingface/hub/ during the
 first-time run.
 
 The port also provides Pytorch examples from the official GitHub repository.
 To run and modify these examples, copy them to a user-writable directory
 and then launch the entry point script via ai-ml-env-bash:
 
 	cp -r /usr/local/share/ai-ml-env/pytorch-examples ~/pytorch-examples
 	cd ~/pytorch-examples && ai-ml-env-bash ./run_python_examples.sh
 
 You can then run
 
 ai-ml-env-bash ./run_python_examples.sh clean
 
 to remove downloaded data or just remove the whole directory to clean up
 everything.
 EOM
 }
 ]