manfiles and new scripts

Dockerfile

@@ -1,11 +1,37 @@
-FROM runpod/pytorch:1.5.0-cu1320-torch2130-ubuntu2404
+FROM nvidia/cuda:13.4.3-cudnn-devel-ubuntu26.04
-WORKDIR /root
+ENV DEBIAN_FRONTEND=noninteractive
+RUN rm -f /etc/dpkg/dpkg.cfg.d/excludes && \
+    rm -f /etc/apt/apt.conf.d/docker-clean && \
+    rm -f /etc/apt/apt.conf.d/01-nodoc && \
+    rm -f /usr/bin/man && \
+    dpkg-divert --quiet --remove /usr/bin/man
 
+RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
+    --mount=type=cache,target=/var/lib/apt,sharing=locked \
+    apt update && apt-get dist-upgrade --autoremove -y && apt-get install -y \
+    python3-pip \
+    python3-dev \
+    python3-venv \
+    build-essential \
+    man \
+    man-db \
+    wget \
+    bzip2 \
+    curl \
+    git \
+    cmake
+
+RUN python3 -m venv /root/venv
+ENV PATH="/root/venv/bin:${PATH}"
+RUN --mount=type=cache,target=/root/.cache/pip,sharing=locked pip3 install torch torchvision torchaudio --upgrade --index-url \
+    https://download.pytorch.org/whl/cu134/
+
+WORKDIR /root
 RUN mkdir llama.cpp
 COPY --exclude=build/ llama.cpp ./llama.cpp/
 WORKDIR /root/llama.cpp
-ENV LD_LIBRARY_PATH=/usr/local/cuda-13.2/compat:${LD_LIBRARY_PATH}
+ENV LD_LIBRARY_PATH=/usr/local/cuda-13.4/compat:${LD_LIBRARY_PATH}
-ENV LIBRARY_PATH=/usr/local/cuda-13.2/compat:${LIBRARY_PATH}
+ENV LIBRARY_PATH=/usr/local/cuda-13.4/compat:${LIBRARY_PATH}
 
 RUN cmake -B build -S . \
      -DCMAKE_BUILD_TYPE=Release \
@@ -18,18 +44,22 @@ RUN cmake -B build -S . \
     && rm -rf build
 
 WORKDIR /root
-RUN git clone https://github.com/silveroxides/convert_to_quant && cd convert_to_quant && pip install -r requirements.txt && pip install .
+RUN git clone https://github.com/silveroxides/convert_to_quant && cd convert_to_quant && pip3 install -r requirements.txt && pip3 install .
 
 RUN mkdir nvfp4_gguf_quant
 WORKDIR /root/nvfp4_gguf_quant
 COPY --exclude=__pycache__ nvfp4gguf ./nvfp4gguf
-COPY --chmod=0644 gguf_to_nvfp4_gguf.py hf_to_nvfp4_gguf.py hf_to_nvfp4_safetensors.py requirements.txt safetensors_to_nvfp4_gguf.py .
+COPY --chmod=0644 gguf_to_nvfp4_gguf.py hf_to_nvfp4_gguf.py hf_to_nvfp4_safetensors.py requirements.txt safetensors_to_nvfp4_gguf.py hf_info.py gguf_info.py .
 
-RUN pip install -r requirements.txt
+RUN --mount=type=cache,target=/root/.cache/pip,sharing=locked pip3 install -r requirements.txt
 COPY --chmod=0644 docker/README.txt /workspace/README.txt
-COPY --chmod=0755 docker/hf_to_nvfp4_gguf docker/hf_to_nvfp4_safetensors docker/gguf_to_nvfp4_gguf docker/safetensors_to_nvfp4_gguf /usr/local/bin/
+COPY --chmod=0755 docker/hf_info docker/gguf_info docker/hf_to_nvfp4_gguf docker/hf_to_nvfp4_safetensors docker/gguf_to_nvfp4_gguf docker/safetensors_to_nvfp4_gguf /usr/local/bin/
 
-RUN apt remove -y python3-cryptography && pip install heretic-llm
+RUN mkdir -p /usr/local/share/man/man1
+COPY --chmod=0644 man/man1/* /usr/local/share/man/man1/
+
+RUN --mount=type=cache,target=/root/.cache/pip,sharing=locked pip3 install heretic-llm
+RUN rm -f /opt/nvidia/entrypoint.d/*banner*.sh /opt/nvidia/entrypoint.d/*license*.sh 2>/dev/null
 
 WORKDIR /workspace
 

docker/gguf_info

@@ -0,0 +1,2 @@
+#!/bin/bash
+python /root/nvfp4_gguf_quant/gguf_info.py "${@}"

docker/hf_info

@@ -0,0 +1,2 @@
+#!/bin/bash
+python /root/nvfp4_gguf_quant/hf_info.py "${@}"

hf_to_nvfp4_safetensors.py

@@ -309,7 +309,7 @@ def parse_args(argv: list[str] | None = None) -> argparse.Namespace:
     parser.add_argument("--early-stop-loss", type=float, default=5e-9)
     parser.add_argument(
         "--early-stop-stall", type=int, default=DEFAULT_EARLY_STOP_STALL,
-        help="Stop after this many steps without progress. At the learning-rate floor, progress means a 1% loss drop (default: 2000)",
+        help="Stop after this many steps without progress. At the learning-rate floor, progress means a 1%% loss drop (default: 2000)",
     )
     parser.add_argument("--calib-samples", type=int, default=3072)
     parser.add_argument("--seed", type=int, default=42)

man/man1/ctq.1

@@ -0,0 +1,170 @@
+.TH CTQ 1 "November 2026" "convert_to_quant" "User Commands"
+.SH NAME
+ctq \- convert safetensors model weights to scaled FP8, NVFP4, INT8, and MXFP8 formats
+.SH SYNOPSIS
+.B ctq
+\fB\-i\fR \fIINPUT\fR
+[\fB\-o\fR \fIOUTPUT\fR]
+[\fB\-\-comfy_quant\fR]
+[\fB\-\-simple\fR]
+[\fB\-\-device\fR \fIDEVICE\fR]
+[\fB\-\-low\-memory\fR]
+[\fB\-\-nvfp4\fR|\fB\-\-int8\fR|\fB\-\-mxfp8\fR]
+[\fIoptions...\fR]
+.SH DESCRIPTION
+.B ctq
+(\fBconvert_to_quant\fR) quantizes neural network weights stored in \fI.safetensors\fR
+format into scaled lower-precision representations, primarily targeting
+\fBComfyUI\fR and hardware-accelerated inference runtimes.
+.PP
+By default, \fBctq\fR performs Scaled FP8 quantization using SVD-guided learned rounding
+(Prodigy optimizer) and per-tensor scaling. It supports multiple quantization formats:
+.IP \(bu 2
+\fBFP8\fR (Float8 E4M3 / E5M2) with tensor-wise, row-wise, or block-wise scaling.
+.IP \(bu 2
+\fBNVFP4\fR (NVIDIA 4-bit floating point, E2M1 with dual-level block scaling).
+.IP \(bu 2
+\fBINT8\fR block-wise quantization with optional group-wise Hadamard rotation (\fIConvRot\fR).
+.IP \(bu 2
+\fBMXFP8\fR (Microscaling FP8) and Hybrid MXFP8 formats.
+.PP
+Quantization error can optionally be extracted into a companion Error Correction LoRA
+adapter (\fB\-\-extract\-lora\fR) to restore high-precision fidelity.
+.SH OPTIONS
+.SS Standard Options
+.TP
+.BI \-i " " INPUT ", " \-\-input " " INPUT
+Path to the input \fI.safetensors\fR weight file (required).
+.TP
+.BI \-o " " OUTPUT ", " \-\-output " " OUTPUT
+Path to the output \fI.safetensors\fR destination file. Auto-generated if omitted.
+.TP
+.BR \-\-comfy_quant ", " \-\-comfy\-quant
+Write ComfyUI-compatible quantization tensors and metadata (\fI.comfy_quant\fR).
+.TP
+.B \-\-simple
+Skip SVD learned rounding and apply fast, direct grid quantization.
+.TP
+.BR \-fpmm ", " \-\-full_precision_matrix_mult
+Add \fIfull_precision_matrix_mult=True\fR to \fI.comfy_quant\fR metadata.
+.TP
+.B \-\-heur
+Skip layers with poor quantization characteristics (aspect ratio, size).
+.TP
+.BI \-\-device " " DEVICE
+Execution device (e.g. \fBcuda\fR, \fBcuda:0\fR, \fBcpu\fR). Overrides auto-detection.
+.TP
+.BR \-lm ", " \-\-low\-memory
+Stream tensor loading to minimize RAM consumption (recommended for models exceeding 50% available memory).
+.TP
+.BI \-\-verbose " " {DEBUG,VERBOSE,NORMAL,MINIMAL}
+Logging verbosity level (default: \fBNORMAL\fR).
+.TP
+.BI \-\-manual_seed " " SEED
+Random seed for reproducibility. Set to \-1 for non-deterministic execution.
+.TP
+.B \-\-save\-quant\-metadata
+Write quantization metadata directly into safetensors file header (\fI_quantization_metadata\fR).
+.SS Quantization Formats & Modes
+.TP
+.B \-\-nvfp4
+Quantize to NVIDIA FP4 (E2M1) with dual-level block scaling (Blackwell architecture).
+.TP
+.B \-\-int8
+Quantize to INT8 block-wise format instead of FP8.
+.TP
+.B \-\-convrot
+Enable group-wise Hadamard rotation (ConvRot) for INT8 row-wise quantization to mitigate activation outliers.
+.TP
+.BI \-\-convrot\-group\-size " " SIZE
+Group size for ConvRot (must be a power of 4, e.g. 64, 256, 1024; default: 256).
+.TP
+.B \-\-mxfp8
+Quantize to MXFP8 (Microscaling FP8) format.
+.TP
+.BI \-\-scaling_mode " " {tensor,row,block}
+FP8 scaling granularity: \fBtensor\fR (single scale), \fBrow\fR (per-row), or \fBblock\fR (2D tile). Default: \fBtensor\fR.
+.TP
+.BI \-\-block_size " " SIZE
+Block size for block-wise scaling (default: 128).
+.SS Learned Rounding & Optimization
+.TP
+.BI \-\-optimizer " " {prodigy,adamw,radam,original}
+Optimization algorithm for learned rounding (default: \fBprodigy\fR).
+.TP
+.BI \-\-num_iter " " N
+Optimization iterations per tensor (default: 4000).
+.TP
+.BI \-\-lr " " LR
+Initial learning rate for AdamW/RAdam/Original (default: 1.0).
+.TP
+.BI \-\-lr_schedule " " {plateau,exponential,adaptive}
+Learning rate decay schedule (default: \fBplateau\fR).
+.TP
+.B \-\-use_speed
+Enable Prodigy's \fIuse_speed\fR acceleration parameter.
+.TP
+.BI \-\-top_p " " P
+Cumulative energy proportion of principal components for SVD (default: 0.2).
+.TP
+.BI \-\-min_k " " K ", " \-\-max_k " " K
+Minimum and maximum SVD rank bounds (default: 256 and 1280).
+.TP
+.B \-\-full_matrix
+Compute full SVD instead of low-rank SVD.
+.TP
+.BI \-\-scale\-optimization " " {fixed,iterative,joint,dualround}
+Block scale tuning strategy (default: \fBfixed\fR).
+.SS Error Correction LoRA
+.TP
+.B \-\-extract\-lora
+Extract quantization residual error into a separate LoRA adapter file.
+.TP
+.BI \-\-lora\-rank " " RANK
+Rank for extracted LoRA layers (default: 32).
+.TP
+.BI \-\-lora\-target " " REGEX
+Regex matching layers to target for LoRA extraction.
+.TP
+.BI \-\-lora\-output " " PATH
+Output file path for the extracted LoRA adapter (\fI.safetensors\fR).
+.SS Model Filters
+.B ctq
+includes pre-tuned layer exclusion filters for common architecture families:
+.TP
+.BR \-\-gemma4 ", " \-\-qwen_vlm ", " \-\-t5xxl ", " \-\-wan ", " \-\-hunyuan ", " \-\-ltxv2 ", " \-\-qwen ", " \-\-zimage
+Keep architecture-critical layers (embeddings, layernorms, vision towers, cross-attention) in native precision.
+.SH EXAMPLES
+.TP
+.B Standard FP8 quantization for ComfyUI:
+.RS
+.nf
+ctq -i model.safetensors -o model-fp8.safetensors --comfy_quant
+.fi
+.RE
+.TP
+.B NVFP4 quantization using learned rounding:
+.RS
+.nf
+ctq -i model.safetensors -o model-nvfp4.safetensors --nvfp4 --scale-optimization joint
+.fi
+.RE
+.TP
+.B Fast INT8 conversion with ConvRot outlier mitigation:
+.RS
+.nf
+ctq -i model.safetensors --int8 --convrot --simple -o model-int8.safetensors
+.fi
+.RE
+.TP
+.B Quantize with Error Correction LoRA extraction:
+.RS
+.nf
+ctq -i model.safetensors --nvfp4 --extract-lora --lora-rank 32 --lora-output model-ec-lora.safetensors
+.fi
+.RE
+.SH SEE ALSO
+.BR hf_to_nvfp4_safetensors (1),
+.BR safetensors_to_nvfp4_gguf (1),
+.BR heretic (1)
+

man/man1/gguf_info.1

@@ -0,0 +1,95 @@
+.TH GGUF_INFO 1 "November 2026" "nvfp4_gguf_quant" "User Commands"
+.SH NAME
+gguf_info \- print architecture, quantization, and tensor metadata from a GGUF header
+.SH SYNOPSIS
+.B gguf_info
+[\fB\-\-metadata\fR]
+[\fB\-\-tensors\fR]
+[\fB\-h\fR|\fB\-\-help\fR]
+\fIgguf\fR [\fIgguf\fR ...]
+.SH DESCRIPTION
+.B gguf_info
+inspects one or more GGUF files (versions 2 and 3) by reading only their header
+and tensor directories. The raw weight bytes are never loaded into memory,
+making the inspection fast even for multi-gigabyte models.
+.PP
+By default,
+.B gguf_info
+displays a concise summary of the model, including:
+.IP \(bu 2
+GGUF version, file size, and total tensor and key-value counts.
+.IP \(bu 2
+Model architecture identifier (e.g., \fIllama\fR, \fIqwen2\fR, \fIgemma2\fR).
+.IP \(bu 2
+General metadata: model name, author, license, context length, embedding length,
+attention head counts, layer counts, feed-forward dimension, and vocabulary size.
+.IP \(bu 2
+Quantization summary: file type classification (e.g., \fIMOSTLY_NVFP4\fR,
+\fIMOSTLY_Q4_K_M\fR, \fIMOSTLY_BF16\fR) and an exact count and breakdown of tensor
+quantization types found across the model.
+.SH OPTIONS
+.TP
+.BR \-h ", " \-\-help
+Show command-line usage help and exit.
+.TP
+.B \-\-metadata
+Print every key-value metadata pair found in the GGUF header. Long string
+values, large numeric arrays, and vocabulary/merges arrays are truncated or
+summarized for readability.
+.TP
+.B \-\-tensors
+Print every tensor present in the file with its full name, GGML quantization
+type (e.g., \fINVFP4\fR, \fIBF16\fR, \fIF16\fR, \fIQ4_K\fR), shape dimensions,
+and byte offset within the file.
+.SH SUPPORTED QUANTIZATION TYPES
+.B gguf_info
+recognizes all standard GGML types including:
+.PP
+.BR F32 ", " F16 ", " BF16 ", " F64 ", "
+.BR Q4_0 ", " Q4_1 ", " Q5_0 ", " Q5_1 ", " Q8_0 ", " Q8_1 ", "
+.BR Q2_K ", " Q3_K ", " Q4_K ", " Q5_K ", " Q6_K ", " Q8_K ", "
+.BR IQ1_S ", " IQ1_M ", " IQ2_XXS ", " IQ2_XS ", " IQ2_S ", "
+.BR IQ3_XXS ", " IQ3_S ", " IQ4_XS ", " IQ4_NL ", "
+.BR TQ1_0 ", " TQ2_0 ", " MXFP4 ", " NVFP4 ", " Q1_0 ", " Q2_0 .
+.SH EXAMPLES
+.TP
+.B Inspect a single GGUF model:
+.RS
+.nf
+gguf_info model-NVFP4.gguf
+.fi
+.RE
+.TP
+.B Dump all metadata keys and parameters:
+.RS
+.nf
+gguf_info \-\-metadata model-NVFP4.gguf
+.fi
+.RE
+.TP
+.B List all tensors and their formats:
+.RS
+.nf
+gguf_info \-\-tensors model-NVFP4.gguf
+.fi
+.RE
+.TP
+.B Inspect multiple models in batch:
+.RS
+.nf
+gguf_info model-f16.gguf model-NVFP4.gguf
+.fi
+.RE
+.SH EXIT STATUS
+.TP
+.B 0
+Success.
+.TP
+.B 1
+An error occurred (e.g. file not found, invalid magic bytes, or unsupported GGUF version).
+.SH SEE ALSO
+.BR hf_info (1),
+.BR gguf_to_nvfp4_gguf (1),
+.BR hf_to_nvfp4_gguf (1),
+.BR ollama_nvfp4 (1)
+

man/man1/gguf_info.py.1

@@ -0,0 +1 @@
+gguf_info.1

man/man1/gguf_to_nvfp4_gguf.1

@@ -0,0 +1,216 @@
+.TH GGUF_TO_NVFP4_GGUF 1 "November 2026" "nvfp4_gguf_quant" "User Commands"
+.SH NAME
+gguf_to_nvfp4_gguf \- quantize or repack an existing GGUF model to NVFP4 GGUF
+.SH SYNOPSIS
+.B gguf_to_nvfp4_gguf
+[\fB\-o\fR \fIOUTPUT\fR]
+[\fB\-\-modelfile\-only\fR]
+[\fB\-\-overwrite\fR]
+[\fB\-\-mmproj\fR \fIMMPROJ\fR]
+[\fB\-\-context\-length\fR \fILEN\fR]
+[\fB\-\-no\-quant\fR]
+[\fB\-\-outtype\fR \fITYPE\fR]
+[\fB\-\-llama\-cpp\fR \fIPATH\fR]
+[\fB\-\-convert\-to\-quant\fR \fIPATH\fR]
+[\fB\-\-model\-name\fR \fINAME\fR]
+[\fB\-\-device\fR \fIDEVICE\fR]
+[\fB\-\-simple\fR]
+[\fB\-\-optimizer\fR \fIOPT\fR]
+[\fB\-\-num\-iter\fR \fIN\fR]
+[\fB\-\-lr\fR \fILR\fR]
+[\fB\-\-lr\-patience\fR \fIN\fR]
+[\fB\-\-lr\-min\fR \fIMIN\fR]
+[\fB\-\-lr\-schedule\fR \fISCHED\fR]
+[\fB\-\-scale\-optimization\fR \fIMODE\fR]
+[\fB\-\-scale\-refinement\-rounds\fR \fIN\fR]
+[\fB\-\-full\-matrix\fR]
+[\fB\-\-top\-p\fR \fIP\fR]
+[\fB\-\-min\-k\fR \fIK\fR]
+[\fB\-\-max\-k\fR \fIK\fR]
+[\fB\-\-early\-stop\-loss\fR \fILOSS\fR]
+[\fB\-\-early\-stop\-stall\fR \fISTALL\fR]
+[\fB\-\-target\-loss\fR \fITARGET\fR]
+[\fB\-\-early\-stop\-floor\-stall\fR \fIFLOOR\fR]
+[\fB\-\-calib\-samples\fR \fISAMPLES\fR]
+[\fB\-\-seed\fR \fISEED\fR]
+[\fB\-\-no\-bias\-correction\fR]
+[\fB\-\-quantize\-output\fR]
+[\fB\-\-exclude\fR \fIREGEX\fR]
+[\fB\-\-checkpoint\-dir\fR \fIDIR\fR]
+[\fB\-\-fresh\fR]
+[\fB\-\-keep\-checkpoints\fR]
+[\fB\-\-eager\fR]
+[\fB\-\-no\-temp\-file\fR]
+[\fB\-\-verbose\fR]
+[\fB\-\-self\-test\fR]
+[\fB\-h\fR|\fB\-\-help\fR]
+\fIgguf\fR
+.SH DESCRIPTION
+.B gguf_to_nvfp4_gguf
+reads an existing GGUF model (such as an F16, BF16, or existing quantized checkpoint)
+and converts it to NVIDIA FP4 (NVFP4) format, or repacks an existing NVFP4 GGUF file.
+.PP
+Vocabulary, tokenizer settings, architecture metadata, and tensor names are copied directly
+from the source GGUF header. When quantizing unquantized (float) tensors, learned rounding
+or simple grid rounding is applied using the same optimization flags as \fBhf_to_nvfp4_gguf\fR.
+.PP
+An accompanying Ollama \fBModelfile\fR is written alongside the output GGUF, configured with
+the source model's parameters and templates.
+.SH POSITIONAL ARGUMENTS
+.TP
+.I gguf
+Input GGUF file path.
+.SH OPTIONS
+.SS Output & File Control
+.TP
+.BI \-o " " OUTPUT ", " \-\-output " " OUTPUT
+Output directory (default: \fI./<name>-NVFP4\fR, or \fI./<name>\fR with \fB\-\-no\-quant\fR and \fBauto\fR).
+If ending in \fI.gguf\fR, specifies the destination file directly.
+.TP
+.B \-\-overwrite
+Overwrite existing output GGUF, Modelfile, or copied projector without prompting.
+.TP
+.B \-\-modelfile\-only
+Write the Ollama Modelfile and skip quantization and GGUF generation.
+.TP
+.BI \-\-mmproj " " MMPROJ
+Path to a vision projector GGUF to bundle into the output directory and add as another \fBFROM\fR layer.
+May be repeated for multi-modal models.
+.TP
+.BI \-\-context\-length " " LEN
+Override maximum context length stored in the GGUF metadata.
+.TP
+.BI \-\-model\-name " " NAME
+Override the model name stored in the GGUF metadata.
+.SS Quantization Mode
+.TP
+.B \-\-no\-quant
+Skip learned rounding. Writes a float GGUF, or copies/repacks an existing NVFP4 GGUF when combined with \fB\-\-outtype nvfp4\fR.
+.TP
+.BI \-\-outtype " " {auto,f32,f16,bf16,nvfp4}
+Target data type when learned rounding is skipped. \fBnvfp4\fR copies an already NVFP4 GGUF without re-optimizing;
+\fBauto\fR leaves tensors as stored in the source file (default: \fBauto\fR).
+.TP
+.B \-\-simple
+Round each value directly onto the E2M1 grid without learned rounding (fast, non-iterative).
+.TP
+.B \-\-quantize\-output
+Also quantize the output (\fIlm_head\fR) matrix. By default, output projection layers are left unquantized.
+.TP
+.BI \-\-exclude " " REGEX
+Regular expression matching GGUF tensor names that should remain in their source precision.
+.SS Optimization & Learned Rounding
+.TP
+.BI \-\-optimizer " " {auto,prodigy,adamw,radam,original}
+Learned-rounding optimization algorithm (default: \fBauto\fR; selects Prodigy if available, else AdamW).
+.TP
+.BI \-\-num\-iter " " N
+Learned-rounding optimization iterations per matrix (default: 4000).
+.TP
+.BI \-\-lr " " LR
+Initial learning rate (default: 1.0).
+.TP
+.BI \-\-lr\-patience " " N
+Patience steps before decaying the learning rate (default: 5).
+.TP
+.BI \-\-lr\-min " " MIN
+Lowest learning rate floor (default: 1e-8).
+.TP
+.BI \-\-lr\-schedule " " {plateau,exponential,adaptive}
+Learning rate decay schedule (default: \fBplateau\fR).
+.TP
+.BI \-\-scale\-optimization " " {fixed,iterative,joint}
+Block scale tuning strategy: \fBfixed\fR, \fBiterative\fR, or \fBjoint\fR (default: \fBjoint\fR).
+.TP
+.BI \-\-scale\-refinement\-rounds " " N
+Scale refresh iterations when using \fBiterative\fR scale optimization (default: 2).
+.TP
+.B \-\-full\-matrix
+Use full SVD instead of low-rank SVD for reconstruction objective.
+.TP
+.BI \-\-top\-p " " P ", " \-\-min\-k " " K ", " \-\-max\-k " " K
+Singular value decomposition rank selection bounds.
+.TP
+.BI \-\-target\-loss " " TARGET
+Target reconstruction loss threshold for early exit (default: 1e-5; 0 disables).
+.TP
+.BI \-\-early\-stop\-stall " " STALL
+Early stop threshold in steps without loss reduction (default: 2000).
+.TP
+.BI \-\-early\-stop\-floor\-stall " " FLOOR
+Steps without improvement after reaching \fB\-\-lr\-min\fR before terminating matrix search (default: 64; 0 disables).
+.TP
+.BI \-\-calib\-samples " " SAMPLES
+Random calibration vectors used for sibling bias correction (default: 3072).
+.TP
+.BI \-\-seed " " SEED
+Random seed (default: 42).
+.TP
+.B \-\-no\-bias\-correction
+Disable sibling bias correction.
+.SS System & Execution
+.TP
+.BI \-\-device " " DEVICE
+Compute device: \fBauto\fR, \fBcpu\fR, \fBcuda\fR, or \fBcuda:N\fR (default: \fBauto\fR).
+.TP
+.BI \-\-llama\-cpp " " PATH
+Path to local llama.cpp repository checkout (default: \fI~/llama.cpp\fR).
+.TP
+.BI \-\-convert\-to\-quant " " PATH
+Path to local convert_to_quant repository checkout.
+.TP
+.BI \-\-checkpoint\-dir " " DIR
+Directory for per-tensor resume checkpoints.
+.TP
+.B \-\-fresh
+Delete checkpoint directory before starting.
+.TP
+.B \-\-keep\-checkpoints
+Keep per-tensor checkpoints on disk after completion.
+.TP
+.B \-\-eager
+Map and load tensors up front.
+.TP
+.B \-\-no\-temp\-file
+Keep finished GGUF tensors in RAM rather than a temporary file.
+.TP
+.B \-\-verbose
+Enable verbose logging.
+.TP
+.B \-\-self\-test
+Verify NVFP4 packing against llama.cpp dequantizer and exit.
+.SH EXAMPLES
+.TP
+.B Quantize an F16 GGUF to NVFP4:
+.RS
+.nf
+gguf_to_nvfp4_gguf -o ./llama3-nvfp4 ./llama-3-8b-f16.gguf
+.fi
+.RE
+.TP
+.B Fast conversion with simple grid rounding:
+.RS
+.nf
+gguf_to_nvfp4_gguf --simple -o ./qwen-nvfp4 ./qwen2.5-7b-bf16.gguf
+.fi
+.RE
+.TP
+.B Repack existing NVFP4 GGUF into a new directory with Modelfile:
+.RS
+.nf
+gguf_to_nvfp4_gguf --no-quant --outtype nvfp4 -o ./repacked ./model-nvfp4.gguf
+.fi
+.RE
+.SH EXIT STATUS
+.TP
+.B 0
+Success.
+.TP
+.B 1
+An error occurred during quantization or repacking.
+.SH SEE ALSO
+.BR hf_to_nvfp4_gguf (1),
+.BR safetensors_to_nvfp4_gguf (1),
+.BR gguf_info (1),
+.BR ollama_nvfp4 (1)
+

man/man1/gguf_to_nvfp4_gguf.py.1

@@ -0,0 +1 @@
+gguf_to_nvfp4_gguf.1

man/man1/heretic.1

@@ -0,0 +1,142 @@
+.TH HERETIC 1 "November 2026" "heretic-llm" "User Commands"
+.SH NAME
+heretic \- automated directional ablation and refusal suppression for large language models
+.SH SYNOPSIS
+.B heretic
+\fB\-\-model\fR \fIMODEL\fR
+[\fB\-\-quantization\fR \fIMETHOD\fR]
+[\fB\-\-n\-trials\fR \fIN\fR]
+[\fB\-\-export\-strategy\fR \fISTRATEGY\fR]
+[\fB\-\-save\-directory\fR \fIDIR\fR]
+[\fB\-\-upload\-repo\-id\fR \fIREPO\fR]
+[\fB\-\-reproduce\fR \fIFILE\fR]
+[\fIoptions...\fR]
+.SH DESCRIPTION
+.B heretic
+(\fBheretic-llm\fR) is an automated optimization and directional ablation tool for
+modifying and un-aligning Large Language Models (LLMs). It isolates refusal vectors
+and behavioral directions across internal residual streams and attention/MLP layers,
+optimizing modifications via genetic multi-objective search (Optuna Pareto front analysis).
+.PP
+The tool balances refusal suppression ("abliteration" / "decensoring") against model capability
+preservation by evaluating candidate modifications against built-in language benchmarks
+(MMLU, GSM8K, IFEval, AGIEval, BBH, etc.).
+.PP
+Modified models can be saved to disk as merged safetensors checkpoints or adapter weights,
+or uploaded directly to Hugging Face with complete reproducibility logs (\fIreproduce.json\fR).
+.SH OPTIONS
+.SS Model & Input
+.TP
+.BI \-\-model " " MODEL
+Hugging Face model repository ID (e.g. \fIgoogle/gemma-2-9b-it\fR) or local filesystem path (required).
+.TP
+.BI \-\-model\-commit " " HASH
+Specific Git commit hash of the Hugging Face model repository.
+.TP
+.BI \-\-quantization " " {NONE,BNB_4BIT}
+Quantization mode used when loading model weights into VRAM: \fBNONE\fR (native precision) or \fBBNB_4BIT\fR (4-bit bitsandbytes). Default: \fBNONE\fR.
+.TP
+.BI \-\-dtypes " " LIST
+List of PyTorch data types to attempt when loading tensors (default: \fI['auto', 'float16', 'bfloat16', 'float32']\fR).
+.TP
+.BI \-\-system\-prompt " " STR
+System prompt passed to the model during evaluation (default: \fI"You are a helpful assistant."\fR).
+.SS Execution & Memory
+.TP
+.BI \-\-device\-map " " MAP
+Device allocation strategy passed to Hugging Face Accelerate (default: \fBauto\fR).
+.TP
+.BI \-\-max\-memory " " DICT
+Maximum VRAM/RAM limits per device (e.g. \fI'{"0": "22GB", "cpu": "64GB"}'\fR).
+.TP
+.BR \-\-offload\-outputs\-to\-cpu ", " \-\-no\-offload\-outputs\-to\-cpu
+Offload intermediate residual tensors and logits to CPU RAM immediately to reduce peak VRAM usage (default: enabled).
+.TP
+.BI \-\-batch\-size " " N
+Batch size for evaluation sequences. Set to 0 for automatic detection (default: 0).
+.TP
+.BI \-\-max\-batch\-size " " N
+Upper bound during automatic batch size discovery (default: 128).
+.TP
+.BI \-\-max\-response\-length " " N
+Maximum token generation length during prompt scoring.
+.SS Optimization & Genetic Search
+.TP
+.BI \-\-n\-trials " " N
+Total number of optimization trials to explore across the Pareto front.
+.TP
+.BI \-\-n\-startup\-trials " " N
+Number of initial random trials before heuristic exploration begins.
+.TP
+.BI \-\-seed " " SEED
+Random seed for reproducibility.
+.TP
+.BI \-\-study\-checkpoint\-dir " " DIR
+Directory where Optuna study progression and intermediate trials are saved.
+.TP
+.BI \-\-scorers " " JSON
+Custom or overriding scoring configurations (loss metrics, refusal detectors, perplexity monitors).
+.TP
+.BI \-\-modifiers " " JSON
+Custom modification operators (directional projection, orthogonalization, rank cuts).
+.SS Benchmarks & Evaluation
+.TP
+.BI \-\-benchmarks " " LIST
+List of evaluation benchmarks to verify model integrity. Supported benchmarks include:
+\fIagieval\fR, \fIbbh\fR, \fIcommonsense_qa\fR, \fIeq_bench\fR, \fIgsm8k\fR, \fIhellaswag\fR, \fIifeval\fR, \fImmlu\fR, \fImmlu_pro\fR, \fIpiqa\fR, \fIwinogrande\fR.
+.TP
+.BI \-\-evaluate\-model " " MODEL
+Evaluate an existing model relative to the base model instead of running search trials.
+.SS Export & Publishing
+.TP
+.BI \-\-export\-strategy " " {MERGE,ADAPTER}
+Model export strategy: \fBMERGE\fR (bake modifications directly into output safetensors weights)
+or \fBADAPTER\fR (export as differential weight delta).
+.TP
+.BI \-\-save\-directory " " DIR
+Local directory path where modified safetensors weights and tokenizer files are written.
+.TP
+.BI \-\-max\-shard\-size " " SIZE
+Maximum file size for exported safetensors shards (default: \fI5GB\fR).
+.TP
+.BI \-\-upload\-repo\-id " " REPO
+Hugging Face repository ID to upload the final modified model to.
+.TP
+.B \-\-upload\-repo\-private
+Make the destination Hugging Face repository private.
+.TP
+.BI \-\-upload\-tags " " LIST
+Tags added to the Hugging Face model card (default: \fI['uncensored', 'decensored', 'abliterated']\fR).
+.SS Reproduction
+.TP
+.BI \-\-reproduce " " PATH_OR_URL
+Load parameters from an existing \fIreproduce.json\fR file or URL and reproduce the exact model modification.
+.TP
+.BI \-\-collect\-reproducibles " " DIR
+Download all \fIreproduce.json\fR files from public Heretic model repositories on Hugging Face into \fIDIR\fR for archival.
+.SH EXAMPLES
+.TP
+.B Run automated abliteration search on a local model:
+.RS
+.nf
+heretic --model ./meta-llama-3-8b-instruct --n-trials 50 --save-directory ./llama3-abliterated
+.fi
+.RE
+.TP
+.B Optimize with 4-bit base weights to reduce VRAM:
+.RS
+.nf
+heretic --model mistralai/Mistral-7B-Instruct-v0.3 --quantization BNB_4BIT --save-directory ./mistral-heretic
+.fi
+.RE
+.TP
+.B Reproduce an existing published Heretic model:
+.RS
+.nf
+heretic --model google/gemma-2-9b-it --reproduce https://huggingface.co/owner/repo/raw/main/reproduce.json --save-directory ./gemma2-reproduced
+.fi
+.RE
+.SH SEE ALSO
+.BR ctq (1),
+.BR hf_to_nvfp4_gguf (1)
+

man/man1/hf_info.1

@@ -0,0 +1,86 @@
+.TH HF_INFO 1 "November 2026" "nvfp4_gguf_quant" "User Commands"
+.SH NAME
+hf_info \- print architecture, quantization, and tensor layout from Hugging Face checkpoints
+.SH SYNOPSIS
+.B hf_info
+[\fB\-\-metadata\fR]
+[\fB\-\-tensors\fR]
+[\fB\-h\fR|\fB\-\-help\fR]
+\fImodel\fR [\fImodel\fR ...]
+.SH DESCRIPTION
+.B hf_info
+inspects one or more Hugging Face model directories or standalone \fI.safetensors\fR files.
+It reads configuration files (\fIconfig.json\fR, \fIgeneration_config.json\fR,
+\fItokenizer_config.json\fR) and safetensors file headers without loading weight bytes
+into RAM, allowing rapid inspection of multi-gigabyte or sharded checkpoints.
+.PP
+By default,
+.B hf_info
+displays a comprehensive summary, including:
+.IP \(bu 2
+Model architecture class, text/vision config hierarchy, and model type.
+.IP \(bu 2
+Model dimensions: context length, hidden size, intermediate size, layer counts, attention heads, and key-value heads.
+.IP \(bu 2
+Parameter counts: total parameters, active parameters, and size on disk.
+.IP \(bu 2
+Chat template configuration and capability detection (such as tool/function calling support).
+.IP \(bu 2
+Quantization format detection: Hugging Face quantization configs (bitsandbytes, AWQ, GPTQ, FP8),
+embedded NVFP4 scales, and ComfyUI NVFP4 metadata (\fI_quantization_metadata\fR, \fIweight_scale\fR, \fI.comfy_quant\fR).
+.IP \(bu 2
+Sharding structure: list of shard files, header sizes, and index verification against \fImodel.safetensors.index.json\fR.
+.SH OPTIONS
+.TP
+.BR \-h ", " \-\-help
+Show command-line usage help and exit.
+.TP
+.B \-\-metadata
+Print complete config, generation configuration, tokenizer configuration, and safetensors header metadata.
+Long strings and large arrays are shortened for readability.
+.TP
+.B \-\-tensors
+Print every tensor name, data type (e.g. \fIBF16\fR, \fIF16\fR, \fIU8\fR), shape, byte size,
+and the shard file in which it resides.
+.SH EXAMPLES
+.TP
+.B Inspect a Hugging Face model directory:
+.RS
+.nf
+hf_info ./gemma-4-12B-it
+.fi
+.RE
+.TP
+.B Inspect a standalone safetensors file:
+.RS
+.nf
+hf_info ./model-00001-of-00004.safetensors
+.fi
+.RE
+.TP
+.B Dump full config and chat template metadata:
+.RS
+.nf
+hf_info \-\-metadata ./meta-llama-3-8b-instruct
+.fi
+.RE
+.TP
+.B List all tensors across all checkpoint shards:
+.RS
+.nf
+hf_info \-\-tensors ./mistral-7b-v0.3
+.fi
+.RE
+.SH EXIT STATUS
+.TP
+.B 0
+Success.
+.TP
+.B 1
+An error occurred (e.g. model directory not found or missing configuration).
+.SH SEE ALSO
+.BR gguf_info (1),
+.BR hf_to_nvfp4_gguf (1),
+.BR hf_to_nvfp4_safetensors (1),
+.BR safetensors_to_nvfp4_gguf (1)
+

man/man1/hf_info.py.1

@@ -0,0 +1 @@
+hf_info.1

man/man1/hf_to_nvfp4_gguf.1

@@ -0,0 +1,235 @@
+.TH HF_TO_NVFP4_GGUF 1 "November 2026" "nvfp4_gguf_quant" "User Commands"
+.SH NAME
+hf_to_nvfp4_gguf \- quantize Hugging Face models to NVFP4 GGUF for llama.cpp and Ollama
+.SH SYNOPSIS
+.B hf_to_nvfp4_gguf
+[\fB\-o\fR \fIOUTPUT\fR]
+[\fB\-\-modelfile\-only\fR]
+[\fB\-\-overwrite\fR]
+[\fB\-\-mmproj\fR \fIMMPROJ\fR]
+[\fB\-\-context\-length\fR \fILEN\fR]
+[\fB\-\-no\-quant\fR]
+[\fB\-\-outtype\fR \fITYPE\fR]
+[\fB\-\-llama\-cpp\fR \fIPATH\fR]
+[\fB\-\-convert\-to\-quant\fR \fIPATH\fR]
+[\fB\-\-model\-name\fR \fINAME\fR]
+[\fB\-\-device\fR \fIDEVICE\fR]
+[\fB\-\-simple\fR]
+[\fB\-\-optimizer\fR \fIOPT\fR]
+[\fB\-\-num\-iter\fR \fIN\fR]
+[\fB\-\-lr\fR \fILR\fR]
+[\fB\-\-lr\-patience\fR \fIN\fR]
+[\fB\-\-lr\-min\fR \fIMIN\fR]
+[\fB\-\-lr\-schedule\fR \fISCHED\fR]
+[\fB\-\-scale\-optimization\fR \fIMODE\fR]
+[\fB\-\-scale\-refinement\-rounds\fR \fIN\fR]
+[\fB\-\-full\-matrix\fR]
+[\fB\-\-top\-p\fR \fIP\fR]
+[\fB\-\-min\-k\fR \fIK\fR]
+[\fB\-\-max\-k\fR \fIK\fR]
+[\fB\-\-early\-stop\-loss\fR \fILOSS\fR]
+[\fB\-\-early\-stop\-stall\fR \fISTALL\fR]
+[\fB\-\-target\-loss\fR \fITARGET\fR]
+[\fB\-\-early\-stop\-floor\-stall\fR \fIFLOOR\fR]
+[\fB\-\-calib\-samples\fR \fISAMPLES\fR]
+[\fB\-\-seed\fR \fISEED\fR]
+[\fB\-\-no\-bias\-correction\fR]
+[\fB\-\-quantize\-output\fR]
+[\fB\-\-exclude\fR \fIREGEX\fR]
+[\fB\-\-checkpoint\-dir\fR \fIDIR\fR]
+[\fB\-\-fresh\fR]
+[\fB\-\-keep\-checkpoints\fR]
+[\fB\-\-eager\fR]
+[\fB\-\-no\-temp\-file\fR]
+[\fB\-\-verbose\fR]
+[\fB\-\-self\-test\fR]
+[\fB\-h\fR|\fB\-\-help\fR]
+\fImodel\fR
+.SH DESCRIPTION
+.B hf_to_nvfp4_gguf
+quantizes a Hugging Face model directory (\fIconfig.json\fR + safetensors) to
+NVIDIA FP4 (NVFP4) format packaged into a GGUF file suitable for \fBllama.cpp\fR
+and \fBOllama\fR.
+.PP
+The model is loaded through \fBllama.cpp\fR's architecture conversion logic so that
+all vocabulary, tensor naming conventions, tokenization metadata, and architectural
+parameters match native GGUF standards. Weight matrices are quantized using learned
+rounding (via \fBconvert_to_quant\fR) or simple nearest-grid projection onto the E2M1
+floating point grid with two-level scaling (per-block E4M3/E8M0 scales and per-tensor scales).
+.PP
+Along with the output GGUF, an Ollama \fBModelfile\fR is generated automatically,
+preserving the model's chat template, stop sequences, parameter defaults, and vision projectors.
+.SH POSITIONAL ARGUMENTS
+.TP
+.I model
+Path to a Hugging Face model directory containing \fIconfig.json\fR and one or more \fI.safetensors\fR files.
+.SH OPTIONS
+.SS Output & File Control
+.TP
+.BI \-o " " OUTPUT ", " \-\-output " " OUTPUT
+Output destination directory (default: \fI./<model-name>-NVFP4\fR, or \fI./<model-name>\fR with
+\fB\-\-no\-quant\fR and \fBauto\fR). If the path ends in \fI.gguf\fR, it specifies the destination
+GGUF file itself.
+.TP
+.B \-\-overwrite
+Overwrite an existing GGUF, Modelfile, or copied projector without prompting.
+.TP
+.B \-\-modelfile\-only
+Generate only the Ollama \fBModelfile\fR and exit without quantizing or writing GGUF tensors.
+.TP
+.BI \-\-mmproj " " MMPROJ
+Path to a vision projector GGUF. The file is copied into the output directory and added as an
+additional \fBFROM\fR layer in the Modelfile. May be specified multiple times for multimodal models.
+.TP
+.BI \-\-context\-length " " LEN
+Override the maximum context length recorded in the GGUF metadata. Replaces checkpoint value
+or defines one if missing.
+.TP
+.BI \-\-model\-name " " NAME
+Override the model name recorded in the GGUF metadata.
+.SS Quantization Mode
+.TP
+.B \-\-no\-quant
+Skip learned rounding. Writes a float GGUF, or repacks an already NVFP4 checkpoint.
+.TP
+.BI \-\-outtype " " {auto,f32,f16,bf16,nvfp4}
+Data type for non-quantized weights when learned rounding is skipped. \fBnvfp4\fR repacks a
+checkpoint that already has packed 4-bit weights. \fBauto\fR preserves the source float types (default: \fBauto\fR).
+.TP
+.B \-\-simple
+Round weights directly onto the E2M1 grid without iterative learned rounding (fast, non-iterative).
+.TP
+.B \-\-quantize\-output
+Also quantize the final output projection (\fIlm_head\fR) matrix. By default, output layers are
+kept in BF16/float for generation quality.
+.TP
+.BI \-\-exclude " " REGEX
+Regular expression matching GGUF tensor names that should remain unquantized in BF16.
+.SS Optimization & Learned Rounding
+.TP
+.BI \-\-optimizer " " {auto,prodigy,adamw,radam,original}
+Learned-rounding optimization algorithm. \fBauto\fR selects Prodigy if installed, otherwise AdamW (default: \fBauto\fR).
+.TP
+.BI \-\-num\-iter " " N
+Learned-rounding optimization iterations per matrix (default: 4000).
+.TP
+.BI \-\-lr " " LR
+Initial learning rate (default: 1.0).
+.TP
+.BI \-\-lr\-patience " " N
+Number of steps without improvement before decaying the learning rate (default: 5).
+.TP
+.BI \-\-lr\-min " " MIN
+Minimum learning rate floor. The flat-loss termination check begins once this floor is reached (default: 1e-8).
+.TP
+.BI \-\-lr\-schedule " " {plateau,exponential,adaptive}
+Learning rate decay schedule (default: \fBplateau\fR).
+.TP
+.BI \-\-scale\-optimization " " {fixed,iterative,joint}
+Block scale tuning strategy. \fBfixed\fR keeps initial scales; \fBiterative\fR refreshes scales periodically
+using the original optimizer; \fBjoint\fR trains block scales concurrently with the codes (default: \fBjoint\fR).
+.TP
+.BI \-\-scale\-refinement\-rounds " " N
+Number of scale refreshes when \fB\-\-scale\-optimization\fR is set to \fBiterative\fR (default: 2).
+.TP
+.B \-\-full\-matrix
+Use full singular value decomposition (SVD) rather than a low-rank approximation for the loss objective.
+.TP
+.BI \-\-top\-p " " P
+Cumulative energy cutoff for low-rank SVD (default: 0.2).
+.TP
+.BI \-\-min\-k " " K ", " \-\-max\-k " " K
+Minimum and maximum rank limits for low-rank SVD (default: 128 and 1280).
+.TP
+.BI \-\-target\-loss " " TARGET
+Stop optimization when best reconstruction loss reaches this threshold (default: 1e-5; 0 disables).
+.TP
+.BI \-\-early\-stop\-stall " " STALL
+Stop optimization after this many steps without reaching a new best loss (default: 2000).
+.TP
+.BI \-\-early\-stop\-floor\-stall " " FLOOR
+Stop optimization after this many steps without improvement once the learning rate has reached \fB\-\-lr\-min\fR (default: 64; 0 disables).
+.TP
+.BI \-\-calib\-samples " " SAMPLES
+Number of random Gaussian calibration inputs used to calculate sibling bias correction (default: 3072).
+.TP
+.BI \-\-seed " " SEED
+Random seed for calibration vectors (default: 42).
+.TP
+.B \-\-no\-bias\-correction
+Disable sibling bias correction on downstream biases.
+.SS System & Execution
+.TP
+.BI \-\-device " " DEVICE
+Device to perform optimization on: \fBauto\fR, \fBcpu\fR, \fBcuda\fR, or \fBcuda:N\fR (default: \fBauto\fR).
+.TP
+.BI \-\-llama\-cpp " " PATH
+Path to local \fBllama.cpp\fR repository checkout (default: \fI~/llama.cpp\fR).
+.TP
+.BI \-\-convert\-to\-quant " " PATH
+Path to local \fBconvert_to_quant\fR repository checkout, if not installed as a Python package.
+.TP
+.BI \-\-checkpoint\-dir " " DIR
+Directory used to store and resume per-tensor quantization checkpoints.
+.TP
+.B \-\-fresh
+Remove existing checkpoint directory before starting.
+.TP
+.B \-\-keep\-checkpoints
+Preserve per-tensor checkpoints after successful conversion.
+.TP
+.B \-\-eager
+Load all model tensors into memory up front rather than streaming (requires more system RAM).
+.TP
+.B \-\-no\-temp\-file
+Keep finished GGUF tensors in memory rather than buffering through a temporary scratch file.
+.TP
+.B \-\-verbose
+Enable detailed verbose logging.
+.TP
+.B \-\-self\-test
+Run internal verification of NVFP4 packing against llama.cpp's reference dequantizer and exit immediately.
+.SH EXAMPLES
+.TP
+.B Full learned-rounding conversion to GGUF:
+.RS
+.nf
+hf_to_nvfp4_gguf -o ./llama3-8b-NVFP4 ./meta-llama-3-8b
+.fi
+.RE
+.TP
+.B Fast quantization with simple grid rounding:
+.RS
+.nf
+hf_to_nvfp4_gguf --simple -o ./qwen-nvfp4 ./Qwen2.5-7B-Instruct
+.fi
+.RE
+.TP
+.B Multi-modal model with vision projector:
+.RS
+.nf
+hf_to_nvfp4_gguf --mmproj mmproj-model-f16.gguf -o ./vision-nvfp4 ./Qwen2-VL-7B
+.fi
+.RE
+.TP
+.B Specify CUDA GPU and custom llama.cpp directory:
+.RS
+.nf
+hf_to_nvfp4_gguf --llama-cpp /opt/llama.cpp --device cuda:0 -o ./output ./model
+.fi
+.RE
+.SH EXIT STATUS
+.TP
+.B 0
+Success.
+.TP
+.B 1
+An error occurred during conversion or validation.
+.SH SEE ALSO
+.BR gguf_to_nvfp4_gguf (1),
+.BR safetensors_to_nvfp4_gguf (1),
+.BR hf_to_nvfp4_safetensors (1),
+.BR hf_info (1),
+.BR gguf_info (1),
+.BR ollama_nvfp4 (1)
+

man/man1/hf_to_nvfp4_gguf.py.1

@@ -0,0 +1 @@
+hf_to_nvfp4_gguf.1

man/man1/hf_to_nvfp4_safetensors.1

@@ -0,0 +1,169 @@
+.TH HF_TO_NVFP4_SAFETENSORS 1 "November 2026" "nvfp4_gguf_quant" "User Commands"
+.SH NAME
+hf_to_nvfp4_safetensors \- quantize a Hugging Face directory to a ComfyUI NVFP4 safetensors file
+.SH SYNOPSIS
+.B hf_to_nvfp4_safetensors
+[\fB\-o\fR \fIOUTPUT\fR]
+[\fB\-\-overwrite\fR]
+[\fB\-\-output\-dtype\fR \fIDTYPE\fR]
+[\fB\-\-low\-memory\fR]
+[\fB\-\-heur\fR]
+[\fB\-\-exclude\-layers\fR \fIREGEX\fR]
+[\fB\-\-convert\-to\-quant\fR \fIPATH\fR]
+[\fB\-\-simple\fR]
+[\fB\-\-optimizer\fR \fIOPT\fR]
+[\fB\-\-num\-iter\fR \fIN\fR]
+[\fB\-\-lr\fR \fILR\fR]
+[\fB\-\-lr\-min\fR \fIMIN\fR]
+[\fB\-\-lr\-patience\fR \fIN\fR]
+[\fB\-\-lr\-schedule\fR \fISCHED\fR]
+[\fB\-\-scale\-optimization\fR \fIMODE\fR]
+[\fB\-\-scale\-refinement\-rounds\fR \fIN\fR]
+[\fB\-\-full\-matrix\fR]
+[\fB\-\-top\-p\fR \fIP\fR]
+[\fB\-\-min\-k\fR \fIK\fR]
+[\fB\-\-max\-k\fR \fIK\fR]
+[\fB\-\-early\-stop\-loss\fR \fILOSS\fR]
+[\fB\-\-early\-stop\-stall\fR \fISTALL\fR]
+[\fB\-\-calib\-samples\fR \fISAMPLES\fR]
+[\fB\-\-seed\fR \fISEED\fR]
+[\fB\-\-use\-speed\fR]
+[\fB\-\-verbose\fR]
+[\fB\-h\fR|\fB\-\-help\fR]
+\fImodel\fR
+.SH DESCRIPTION
+.B hf_to_nvfp4_safetensors
+quantizes a Hugging Face model directory (\fIconfig.json\fR and sharded \fI.safetensors\fR files)
+into a single consolidated \fI.safetensors\fR file formatted for ComfyUI's native NVFP4 loader.
+.PP
+For each quantized weight matrix, the script outputs packed NVFP4 bytes alongside
+companion scaling tensors:
+.IP \(bu 2
+\fI<name>.weight\fR (packed NVFP4 data, two 4-bit values per byte)
+.IP \(bu 2
+\fI<name>.weight_scale\fR (per-block E4M3 scale factors)
+.IP \(bu 2
+\fI<name>.weight_scale_2\fR (per-tensor scale factor)
+.IP \(bu 2
+\fI<name>.comfy_quant\fR (metadata tensor specifying quantization type)
+.PP
+A unified \fI_quantization_metadata\fR header is embedded in the output safetensors header.
+When the source model is split across multiple shards, each shard is quantized in a temporary
+workspace and then efficiently merged into the final single output file.
+.PP
+Normalization layers, embeddings, biases, and layers matching \fB\-\-exclude\-layers\fR
+remain in the target float format (controlled by \fB\-\-output\-dtype\fR).
+.SH POSITIONAL ARGUMENTS
+.TP
+.I model
+Hugging Face model directory containing \fIconfig.json\fR and \fI.safetensors\fR weights.
+.SH OPTIONS
+.SS Output & Memory Control
+.TP
+.BI \-o " " OUTPUT ", " \-\-output " " OUTPUT
+Output \fI.safetensors\fR file path (default: \fI./<model-name>-NVFP4.safetensors\fR).
+.TP
+.B \-\-overwrite
+Overwrite an existing output file without prompting.
+.TP
+.BI \-\-output\-dtype " " {bfloat16,float16}
+Data type recorded for quantized layer scales and used for unquantized 2D weights (default: \fBbfloat16\fR).
+.TP
+.B \-\-low\-memory
+Stream each shard sequentially through quantization rather than loading the entire model shard into RAM at once.
+.TP
+.B \-\-heur
+Skip layers that convert_to_quant's built-in size heuristic rejects.
+.TP
+.BI \-\-exclude\-layers " " REGEX
+Regular expression matching layer or tensor names to leave unquantized in float format.
+.SS Quantization & Optimization
+.TP
+.B \-\-simple
+Round onto the E2M1 grid directly without iterative learned rounding.
+.TP
+.BI \-\-optimizer " " {auto,prodigy,adamw,radam,original}
+Learned-rounding optimizer. \fBauto\fR uses Prodigy when installed, otherwise AdamW (default: \fBauto\fR).
+.TP
+.BI \-\-num\-iter " " N
+Learned-rounding iterations per matrix (default: 4000).
+.TP
+.BI \-\-lr " " LR
+Initial learning rate (default: 1.0).
+.TP
+.BI \-\-lr\-min " " MIN
+Lowest learning rate floor (default: 1e-8).
+.TP
+.BI \-\-lr\-patience " " N
+Patience steps before decaying learning rate (default: 5).
+.TP
+.BI \-\-lr\-schedule " " {plateau,exponential,adaptive}
+Learning rate decay schedule (default: \fBplateau\fR).
+.TP
+.BI \-\-scale\-optimization " " {fixed,iterative,joint}
+Scale tuning strategy: \fBfixed\fR, \fBiterative\fR, or \fBjoint\fR (default: \fBjoint\fR).
+.TP
+.BI \-\-scale\-refinement\-rounds " " N
+Scale refresh iterations when using iterative scale optimization (default: 2).
+.TP
+.B \-\-full\-matrix
+Use full SVD instead of low-rank SVD.
+.TP
+.BI \-\-top\-p " " P ", " \-\-min\-k " " K ", " \-\-max\-k " " K
+Singular value decomposition rank selection bounds.
+.TP
+.BI \-\-early\-stop\-loss " " LOSS
+Early stopping loss target (default: 5e-9).
+.TP
+.BI \-\-early\-stop\-stall " " STALL
+Stop after this many steps without loss improvement (default: 2000).
+.TP
+.BI \-\-calib\-samples " " SAMPLES
+Random Gaussian calibration inputs for bias correction (default: 3072).
+.TP
+.BI \-\-seed " " SEED
+Random seed (default: 42).
+.TP
+.B \-\-use\-speed
+Enable Prodigy optimizer's \fIuse_speed\fR mode.
+.SS System & Execution
+.TP
+.BI \-\-convert\-to\-quant " " PATH
+Path to local convert_to_quant repository checkout.
+.TP
+.B \-\-verbose
+Enable verbose output during shard quantization and merge.
+.SH EXAMPLES
+.TP
+.B Quantize a model directory into a ComfyUI NVFP4 safetensors file:
+.RS
+.nf
+hf_to_nvfp4_safetensors -o gemma-4-12B-it-NVFP4.safetensors ./gemma-4-12B-it
+.fi
+.RE
+.TP
+.B Low-memory streaming mode with float16 weights:
+.RS
+.nf
+hf_to_nvfp4_safetensors --low-memory --output-dtype float16 -o ./model-nvfp4.safetensors ./model
+.fi
+.RE
+.TP
+.B Exclude specific projection layers:
+.RS
+.nf
+hf_to_nvfp4_safetensors --exclude-layers ".*gate_proj.*" -o ./model-nvfp4.safetensors ./model
+.fi
+.RE
+.SH EXIT STATUS
+.TP
+.B 0
+Success.
+.TP
+.B 1
+An error occurred during quantization or shard merging.
+.SH SEE ALSO
+.BR safetensors_to_nvfp4_gguf (1),
+.BR hf_to_nvfp4_gguf (1),
+.BR hf_info (1)
+

man/man1/hf_to_nvfp4_safetensors.py.1

@@ -0,0 +1 @@
+hf_to_nvfp4_safetensors.1

man/man1/ollama_nvfp4.1

@@ -0,0 +1,181 @@
+.TH OLLAMA_NVFP4 1 "November 2026" "nvfp4_gguf_quant" "User Commands"
+.SH NAME
+ollama_nvfp4 \- inspect Ollama sha256 model stores and quantize models to NVFP4 GGUF
+.SH SYNOPSIS
+.B ollama_nvfp4
+[\fB\-\-models\fR \fIDIR\fR]
+.I subcommand
+[\fIoptions...\fR]
+.br
+.B ollama_nvfp4
+[\fB\-\-models\fR \fIDIR\fR]
+.B \-\-info
+\fImodel\fR
+[\fIgguf_info_args...\fR]
+.SH DESCRIPTION
+.B ollama_nvfp4
+is a management and quantization utility for local \fBOllama\fR model installations.
+Ollama stores model artifacts as content-addressed blobs (\fIblobs/sha256-<digest>\fR)
+referenced by JSON manifests (\fImanifests/\fR).
+.PP
+.B ollama_nvfp4
+resolves these hashes into human-readable model names, displays model capabilities and
+parameters, exports native GGUF weights alongside reconstructed \fBModelfile\fRs,
+and directly quantizes models to NVIDIA FP4 (NVFP4) format. Quantized models can
+optionally be registered straight back into Ollama's local library.
+.PP
+The generated \fBModelfile\fR preserves the original prompt template, stop sequences,
+system message, renderer, and parser settings so that thinking/reasoning modes and
+generation behavior remain intact.
+.SH GLOBAL OPTIONS
+.TP
+.BI \-\-models " " DIR
+Path to the Ollama models store directory containing \fIblobs/\fR and \fImanifests/\fR.
+If not provided, the location is resolved via the \fBOLLAMA_MODELS\fR environment variable,
+\fI/models/ollama\fR, or \fI~/.ollama/models\fR.
+.TP
+.BR \-h ", " \-\-help
+Show command help and exit.
+.SH SUBCOMMANDS
+.SS list
+.B ollama_nvfp4
+[\fB\-\-models\fR \fIDIR\fR]
+.B list
+[\fB\-\-full\-scan\fR]
+[\fB\-\-ctx\-scan\fR]
+.PP
+Display every manifest in the store as a readable model entry with family, quantization type,
+and capability badges (chat, tool call, thinking, vision).
+.TP
+.B \-\-full\-scan
+Read the underlying GGUF header of each model blob. This is slower on large stores but reveals
+capabilities and vision encoders not declared in manifest metadata.
+.TP
+.B \-\-ctx\-scan
+Scan parameter blobs and report models whose Modelfile explicitly configures \fInum_ctx\fR.
+.SS show
+.B ollama_nvfp4
+[\fB\-\-models\fR \fIDIR\fR]
+.B show
+\fImodel\fR
+.PP
+Display detailed information for a specified model, including manifest layers, sha256 blob
+hashes, component media types, GGUF architecture metadata, context length, and system settings.
+The \fImodel\fR argument can be a full name (e.g. \fIllama3.2:latest\fR) or a unique substring.
+.SS export
+.B ollama_nvfp4
+[\fB\-\-models\fR \fIDIR\fR]
+.B export
+[\fB\-o\fR \fIDIR\fR]
+[\fB\-\-modelfile\-only\fR]
+[\fB\-\-overwrite\fR|\fB\-\-force\fR]
+\fImodel\fR
+.PP
+Export the model's underlying GGUF weight blob and generate a fully configured \fBModelfile\fR
+into the destination directory (default: \fI./<model>\fR).
+.TP
+.BI \-o " " DIR ", " \-\-output " " DIR
+Output directory path.
+.TP
+.B \-\-modelfile\-only
+Write only the Modelfile without copying the GGUF or vision projector blobs.
+.TP
+.BR \-\-overwrite ", " \-\-force
+Replace existing files in the output directory.
+.SS quant
+.B ollama_nvfp4
+[\fB\-\-models\fR \fIDIR\fR]
+.B quant
+[\fB\-o\fR \fIDIR\fR]
+[\fB\-\-name\fR \fINAME\fR]
+[\fB\-\-install\fR]
+[\fB\-\-float\-only\fR]
+[\fB\-\-context\-length\fR \fILEN\fR]
+[\fIquant_opts...\fR]
+\fImodel\fR
+.PP
+Quantize an Ollama model to NVFP4 GGUF format and generate an accompanying Modelfile.
+.TP
+.BI \-o " " DIR ", " \-\-output " " DIR
+Output directory (default: \fI./<model>-NVFP4\fR).
+.TP
+.BI \-\-name " " NAME
+Model tag used when registering with \fB\-\-install\fR (default: \fI<model>:nvfp4\fR).
+.TP
+.B \-\-install
+Automatically register the quantized model back into Ollama using \fIollama create\fR.
+.TP
+.B \-\-float\-only
+Refuse to requantize a model if its source blob is already quantized (e.g. \fIQ4_K_M\fR).
+Quantizing from float16/BF16 produces superior NVFP4 fidelity.
+.TP
+.BI \-\-context\-length " " LEN
+Override maximum context length stored in the GGUF.
+.TP
+.I quant_opts
+All standard learned-rounding and execution options supported by \fBgguf_to_nvfp4_gguf\fR
+are supported: \fB\-\-simple\fR, \fB\-\-optimizer\fR, \fB\-\-lr\fR, \fB\-\-num\-iter\fR,
+\fB\-\-scale\-optimization\fR, \fB\-\-device\fR, \fB\-\-checkpoint\-dir\fR, etc.
+.SS \-\-info
+.B ollama_nvfp4
+[\fB\-\-models\fR \fIDIR\fR]
+.B \-\-info
+\fImodel\fR
+[\fIgguf_info_args...\fR]
+.PP
+Resolve \fImodel\fR to its sha256 GGUF weight blob in the store and invoke
+.BR gguf_info (1)
+directly on the file. Any subsequent flags (such as \fB\-\-metadata\fR or \fB\-\-tensors\fR)
+are forwarded directly to \fBgguf_info\fR.
+.SH ENVIRONMENT VARIABLES
+.TP
+.B OLLAMA_MODELS
+Path to the default Ollama models directory. Overridden by \fB\-\-models\fR.
+.SH EXAMPLES
+.TP
+.B List installed models with capability summary:
+.RS
+.nf
+ollama_nvfp4 list
+.fi
+.RE
+.TP
+.B Inspect detailed manifest and parameters of a model:
+.RS
+.nf
+ollama_nvfp4 show llama3.2:3b
+.fi
+.RE
+.TP
+.B Run gguf_info on an Ollama model's weight blob:
+.RS
+.nf
+ollama_nvfp4 --info llama3.2:3b --metadata
+.fi
+.RE
+.TP
+.B Export a model and its Modelfile:
+.RS
+.nf
+ollama_nvfp4 export llama3.2:3b -o ./llama3-export
+.fi
+.RE
+.TP
+.B Quantize an Ollama FP16 model to NVFP4 and install it:
+.RS
+.nf
+ollama_nvfp4 quant --install llama3.2:3b-instruct-fp16
+.fi
+.RE
+.SH EXIT STATUS
+.TP
+.B 0
+Success.
+.TP
+.B 1
+An error occurred (e.g. model not found, invalid store directory, or quantization failure).
+.SH SEE ALSO
+.BR gguf_info (1),
+.BR gguf_to_nvfp4_gguf (1),
+.BR hf_to_nvfp4_gguf (1)
+

man/man1/ollama_nvfp4.py.1

@@ -0,0 +1 @@
+ollama_nvfp4.1

man/man1/safetensors_to_nvfp4_gguf.1

@@ -0,0 +1,219 @@
+.TH SAFETENSORS_TO_NVFP4_GGUF 1 "November 2026" "nvfp4_gguf_quant" "User Commands"
+.SH NAME
+safetensors_to_nvfp4_gguf \- quantize a safetensors file to an NVFP4 GGUF weight container
+.SH SYNOPSIS
+.B safetensors_to_nvfp4_gguf
+[\fB\-o\fR \fIOUTPUT\fR]
+[\fB\-\-all\-weights\fR]
+[\fB\-\-no\-quant\fR]
+[\fB\-\-outtype\fR \fITYPE\fR]
+[\fB\-\-llama\-cpp\fR \fIPATH\fR]
+[\fB\-\-convert\-to\-quant\fR \fIPATH\fR]
+[\fB\-\-overwrite\fR|\fB\-\-force\fR]
+[\fB\-\-mmproj\fR \fIMMPROJ\fR]
+[\fB\-\-context\-length\fR \fILEN\fR]
+[\fB\-\-checkpoint\-dir\fR \fIDIR\fR]
+[\fB\-\-fresh\fR]
+[\fB\-\-keep\-checkpoints\fR]
+[\fB\-\-no\-temp\-file\fR]
+[\fB\-\-verbose\fR]
+[\fB\-\-self\-test\fR]
+[\fB\-\-device\fR \fIDEVICE\fR]
+[\fB\-\-simple\fR]
+[\fB\-\-optimizer\fR \fIOPT\fR]
+[\fB\-\-num\-iter\fR \fIN\fR]
+[\fB\-\-lr\fR \fILR\fR]
+[\fB\-\-lr\-schedule\fR \fISCHED\fR]
+[\fB\-\-scale\-optimization\fR \fIMODE\fR]
+[\fB\-\-scale\-refinement\-rounds\fR \fIN\fR]
+[\fB\-\-full\-matrix\fR]
+[\fB\-\-top\-p\fR \fIP\fR]
+[\fB\-\-min\-k\fR \fIK\fR]
+[\fB\-\-max\-k\fR \fIK\fR]
+[\fB\-\-early\-stop\-loss\fR \fILOSS\fR]
+[\fB\-\-early\-stop\-lr\fR \fILR\fR]
+[\fB\-\-early\-stop\-stall\fR \fISTALL\fR]
+[\fB\-\-target\-loss\fR \fITARGET\fR]
+[\fB\-\-early\-stop\-floor\-stall\fR \fIFLOOR\fR]
+[\fB\-\-lr\-min\fR \fIMIN\fR]
+[\fB\-\-lr\-patience\fR \fIN\fR]
+[\fB\-\-use\-speed\fR]
+[\fB\-\-calib\-samples\fR \fISAMPLES\fR]
+[\fB\-\-seed\fR \fISEED\fR]
+[\fB\-\-no\-bias\-correction\fR]
+[\fB\-\-quantize\-output\fR]
+[\fB\-\-exclude\fR \fIREGEX\fR]
+[\fB\-h\fR|\fB\-\-help\fR]
+\fIsafetensors\fR
+.SH DESCRIPTION
+.B safetensors_to_nvfp4_gguf
+quantizes the tensor weights stored in a single \fI.safetensors\fR file directly into
+an NVFP4 GGUF weight container, without requiring a Hugging Face model directory or
+tokenizer configuration.
+.PP
+This tool is particularly useful for standalone diffusion models (e.g. FLUX, SDXL),
+isolated LoRA weights, or intermediate checkpoints. All original tensor names are
+preserved.
+.PP
+By default, layers on \fBconvert_to_quant\fR's standard exclusion list (normalization
+layers, biases, embeddings, and output heads) remain unquantized in their original precision.
+Passing \fB\-\-all\-weights\fR overrides this heuristic and quantizes every eligible 2D
+matrix whose trailing dimension is a multiple of 64.
+.PP
+The tool also supports repacking ComfyUI-style NVFP4 \fI.safetensors\fR files (which contain
+\fIweight_scale\fR and \fIweight_scale_2\fR) into GGUF format without re-optimization using
+\fB\-\-no\-quant \-\-outtype nvfp4\fR.
+.SH POSITIONAL ARGUMENTS
+.TP
+.I safetensors
+Path to the input \fI.safetensors\fR file.
+.SH OPTIONS
+.SS Output & Packaging
+.TP
+.BI \-o " " OUTPUT ", " \-\-output " " OUTPUT
+Output GGUF file path (default: \fI<stem>-NVFP4.gguf\fR, or \fI<stem>.gguf\fR with \fB\-\-no\-quant\fR and \fBauto\fR).
+.TP
+.BR \-\-overwrite ", " \-\-force
+Replace an existing GGUF or auxiliary output file without prompting.
+.TP
+.B \-\-all\-weights
+Quantize every 2D weight matrix eligible for 4-bit block packing, ignoring convert_to_quant's
+default exclusion list.
+.TP
+.B \-\-no\-quant
+Skip learned rounding. Writes a float GGUF or repacks ComfyUI NVFP4 weights.
+.TP
+.BI \-\-outtype " " {auto,f32,f16,bf16,nvfp4}
+Data type when learned rounding is skipped: \fBnvfp4\fR repacks ComfyUI NVFP4 weights; \fBauto\fR preserves original dtypes (default: \fBauto\fR).
+.TP
+.BI \-\-mmproj " " MMPROJ
+Path to a vision projector GGUF to copy next to output. May be specified multiple times.
+.TP
+.BI \-\-context\-length " " LEN
+Maximum context length to store in the GGUF metadata.
+.SS Quantization & Optimization
+.TP
+.B \-\-simple
+Round each value directly onto the E2M1 grid without learned rounding.
+.TP
+.BI \-\-optimizer " " {auto,prodigy,adamw,radam,original}
+Learned-rounding optimizer (default: \fBauto\fR).
+.TP
+.BI \-\-num\-iter " " N
+Optimization iterations per matrix (default: 4000).
+.TP
+.BI \-\-lr " " LR
+Initial learning rate (default: 1.0).
+.TP
+.BI \-\-lr\-schedule " " {plateau,exponential,adaptive}
+Learning rate decay schedule (default: \fBplateau\fR).
+.TP
+.BI \-\-lr\-min " " MIN
+Lowest learning rate floor (default: 1e-8).
+.TP
+.BI \-\-lr\-patience " " N
+Patience steps before decaying the learning rate (default: 5).
+.TP
+.B \-\-use\-speed
+Enable Prodigy optimizer's \fIuse_speed\fR mode.
+.TP
+.BI \-\-scale\-optimization " " {fixed,iterative,joint}
+Block scale tuning method (default: \fBjoint\fR).
+.TP
+.BI \-\-scale\-refinement\-rounds " " N
+Scale refresh iterations for iterative mode (default: 2).
+.TP
+.B \-\-full\-matrix
+Use full SVD instead of low-rank SVD.
+.TP
+.BI \-\-top\-p " " P ", " \-\-min\-k " " K ", " \-\-max\-k " " K
+Singular value decomposition rank selection bounds.
+.TP
+.BI \-\-target\-loss " " TARGET
+Target reconstruction loss threshold (default: 1e-5; 0 disables).
+.TP
+.BI \-\-early\-stop\-stall " " STALL
+Early stop stall steps without reaching a new best loss (default: 2000).
+.TP
+.BI \-\-early\-stop\-floor\-stall " " FLOOR
+Stall steps at minimum learning rate before stopping (default: 64; 0 disables).
+.TP
+.BI \-\-early\-stop\-lr " " LR
+Learning rate threshold for early stop check.
+.TP
+.BI \-\-calib\-samples " " SAMPLES
+Random Gaussian calibration inputs for sibling bias correction (default: 3072).
+.TP
+.BI \-\-seed " " SEED
+Random seed (default: 42).
+.TP
+.B \-\-no\-bias\-correction
+Disable sibling bias correction.
+.TP
+.B \-\-quantize\-output
+Also quantize \fIoutput.weight\fR.
+.TP
+.BI \-\-exclude " " REGEX
+Regex matching tensor names to leave unquantized.
+.SS System & Execution
+.TP
+.BI \-\-device " " DEVICE
+Device for quantization computations: \fBauto\fR, \fBcpu\fR, \fBcuda\fR, or \fBcuda:N\fR (default: \fBauto\fR).
+.TP
+.BI \-\-llama\-cpp " " PATH
+Path to llama.cpp repository checkout (default: \fI~/llama.cpp\fR).
+.TP
+.BI \-\-convert\-to\-quant " " PATH
+Path to convert_to_quant checkout.
+.TP
+.BI \-\-checkpoint\-dir " " DIR
+Directory for per-tensor resume checkpoints.
+.TP
+.B \-\-fresh
+Delete checkpoint directory before starting.
+.TP
+.B \-\-keep\-checkpoints
+Keep per-tensor checkpoints after completion.
+.TP
+.B \-\-no\-temp\-file
+Keep finished GGUF tensors in memory rather than a temporary file.
+.TP
+.B \-\-verbose
+Enable verbose logging.
+.TP
+.B \-\-self\-test
+Verify NVFP4 packing against llama.cpp dequantizer and exit.
+.SH EXAMPLES
+.TP
+.B Quantize a standalone diffusion model:
+.RS
+.nf
+safetensors_to_nvfp4_gguf -o flux1-dev-nvfp4.gguf flux1-dev.safetensors
+.fi
+.RE
+.TP
+.B Repack ComfyUI NVFP4 safetensors to GGUF:
+.RS
+.nf
+safetensors_to_nvfp4_gguf --no-quant --outtype nvfp4 -o repacked.gguf comfy-nvfp4.safetensors
+.fi
+.RE
+.TP
+.B Quantize every eligible weight matrix with simple grid rounding:
+.RS
+.nf
+safetensors_to_nvfp4_gguf --simple --all-weights -o fast-nvfp4.gguf model.safetensors
+.fi
+.RE
+.SH EXIT STATUS
+.TP
+.B 0
+Success.
+.TP
+.B 1
+An error occurred during conversion or packaging.
+.SH SEE ALSO
+.BR hf_to_nvfp4_safetensors (1),
+.BR hf_to_nvfp4_gguf (1),
+.BR gguf_to_nvfp4_gguf (1)
+

man/man1/safetensors_to_nvfp4_gguf.py.1

@@ -0,0 +1 @@
+safetensors_to_nvfp4_gguf.1