Molmo2-8B Minimax H3

Complete community INT8 ConvRot quantization of Ai2's Molmo2-8B, with experimental Molmo-to-MiniMax-H3 projectors. This is not an official Ai2 or MiniMax release.

The single weight file molmo2_8b_int8_convrot.safetensors totals 9.29 GB (decimal); 302 eligible linear layers are quantized. The complete package including the projector is about 9.65 GB.

The full model is included: language and vision weights, multimodal adapter, tokenizer, processor and original model code. Eligible linear layers use symmetric per-output-channel INT8 weights with regular block-Hadamard ConvRot rotations (blocks 256, 64 or 16) and dynamic per-token INT8 activations. Embeddings, normalization, biases and unsupported shapes retain BF16. The separate H3 projector retains its original precision. No original BF16 weight download is required.

Load the complete quantized model

Use Python with PyTorch supporting your NVIDIA CUDA GPU, transformers 4.57.1 or later (checked with 4.57.1, 5.17 and 5.22.1), accelerate, safetensors, pillow, numpy, einops and molmo_utils. Video processing may require the upstream video dependencies.

from huggingface_hub import snapshot_download
import importlib.util
from transformers import AutoProcessor

folder = snapshot_download("Alissonerdx/Molmo2-8B-Minimax-H3")
spec = importlib.util.spec_from_file_location("convrot_runtime", folder + "/convrot_runtime.py")
runtime = importlib.util.module_from_spec(spec)
spec.loader.exec_module(runtime)
model = runtime.load_model(folder, device="cuda")
processor = AutoProcessor.from_pretrained(folder, trust_remote_code=True)

This package uses its included loader; ordinary Transformers from_pretrained does not interpret this INT8 weight file.

Use in ComfyUI

Install the node from GitHub: alisson-anjos/ComfyUI-Molmo2-H3. Its Molmo2 as H3 encoder (projection, experimental) loader has no inputs: on first use it downloads this package into ComfyUI/models/text_encoders/Molmo2-8B-Minimax-H3/ (keeping files already present), finds the projector under projections/, and returns a CLIP. Connect that CLIP where an H3 workflow uses its text-encoder loader. Molmo runs inside the ComfyUI process, with ComfyUI's own Python, and is placed and offloaded by ComfyUI's model manager like other text encoders; no separate environment or worker process is needed. MiniMax H3 and task LoRAs are separate models.

Experimental H3 projection

molmo2-8b-h3-tap24-phaseb-pilot.safetensors maps layer-24 Molmo hidden states into H3 conditioning. The projector was calibrated with the original floating-point Molmo; quantization compatibility requires evaluation. It does not establish reliable photo-controlled selection of an arbitrary person in a multi-person video. No claim of preserved tracking accuracy or successful identity replacement is made.

Reproducibility and validation

Native tracking comparison: the short target description failed selection, while explicit shorts/gloves descriptions selected each requested performer in both BF16 and INT8 in this clip. This small check does not establish general tracking equivalence. See the explicit-prompt follow-up alongside the original results.

See provenance.json for original shard SHA256 verification, convrot_config.json for the exact module map, and validation.json for completed inference checks. Basic inference checks do not substitute for a full tracking or downstream video benchmark. Export and validation scripts are provided in quantization/.

The runtime is a compact eager PyTorch implementation, not a fused performance-optimized engine. Hardware INT8 GEMM is used on CUDA; no throughput improvement is promised. Source files are from allenai/Molmo2-8B under Apache 2.0. Modified distribution: quantized weights, standalone loader and experimental H3 integration. See LICENSE and NOTICE.