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ComfyUI-format conversion of the PDMD 2-NFE LoRA — the 2-step (2 NFE) student distilled from MiniMax-H3-33B with Projected Distribution Matching Distillation, rank 128, covering attention projections and both feed-forward layers of all 50 transformer blocks + 2 token-refiner blocks. (Companion repo for the 4-NFE variant: `Iwannapose/minimax_h3_pdmd_4nfe_comfyui`.) The two are numerically identical (the source fp32 file is an exact cast of the bf16 one); the bf16 file is the one to use. Both are larger than the source because the fused `qkv_proj` stores the block-diagonal zero padding explicitly. Source keys are Diffusers PEFT names (`transformer.transformer_blocks.N.attn.to_q.lora_A.weight` …); target keys follow the ComfyUI H3 layout (same scheme as the proven `minimax_h3_fl2v_turbo_8step_v1.0_768p_comfyui_bf16.safetensors`):

Video generation
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ComfyUI-format conversion of the PDMD 4-NFE LoRA (`lora_model_0.safetensors`) — the 4-step (4 NFE) student distilled from MiniMax-H3-33B with Projected Distribution Matching Distillation, rank 128, covering attention projections and both feed-forward layers of all 50 transformer blocks + 2 token-refiner blocks. Earlier uploads (base / keys_v2 / v3 / v4 / v5) were broken conversions and have been removed: most of their keys matched nothing in the ComfyUI loader (silent no-op), and v4 missed the SwiGLU gate/value remap below. Source keys are Diffusers PEFT names (`transformer.transformer_blocks.N.attn.to_q.lora_A.weight` …); target keys follow the ComfyUI H3 layout (same scheme as the proven `minimax_h3_fl2v_turbo_8step_v1.0_768p_comfyui_bf16.safetensors`):

Video generation
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Image generation
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Fill-mask
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With this model, you can classify emotions in English text data. The model was trained on 6 diverse datasets (see Appendix below) and predicts Ekman's 6 basic emotions, plus a neutral class: 1) anger 🤬 2) disgust 🤢 3) fear 😨 4) joy 😀 5) neutral 😐 6) sadness 😭 7) surprise 😲 The model is a fine-tuned checkpoint of DistilRoBERTa-base. For a 'non-distilled' emotion model, please refer to the model card of the RoBERTa-large version.

Text analysis
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This RoBERTa-based model can classify the sentiment of English language text in 3 classes: The model was fine-tuned on 5,304 manually annotated social media posts. The hold-out accuracy is 86.1%. For details on the training approach see Web Appendix F in Hartmann et al. (2021). Please cite this paper when you use our model. Feel free to reach out to jochen.hartmann@tum.de with any questions or feedback you may have.

Text analysis
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