Model Catalog¶
This user-facing catalog groups registered HuggingFace model types by task and lists their module classes and example model IDs. Diffusers pipeline components are described separately because they are not entries in the model registry. Neither list implies GGUF import or runtime support; see the generated GGUF capability and evidence catalog for those capability-specific verdicts and statistics.
Auto-fallback: Models not explicitly registered but architecturally compatible with Llama (standard CausalLM transformers) are automatically supported via fallback detection. MoE variants are also auto-detected.
Decoder-Only LLMs (RoPE)¶
Standard autoregressive language models using rotary position embeddings.
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Module Class |
Example HuggingFace Model |
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Also registered with CausalLMModel: apertus, arcee, baichuan,
code_llama, codegen, codegen2, command_r, csm, diffllama,
doge, dots1, evolla, exaone, exaone4, glm, glm4,
gpt_neox, gpt_neox_japanese, gptj, helium, hunyuan_v1_dense,
llama4_text, longcat_flash, minicpm, minicpm3, ministral,
ministral3, mistral3, nanochat, open-llama, openelm,
persimmon, seed_oss, solar_open, stablelm, starcoder2, yi,
youtu, zamba, zamba2.
Decoder-Only LLMs (Absolute Position Embeddings)¶
GPT-2 style models using learned absolute positional embeddings.
Model Type |
Module Class |
Example HuggingFace Model |
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Also registered with GPT2CausalLMModel: biogpt, ctrl, gpt-sw3,
gpt_bigcode, gpt_neo, imagegpt, openai-gpt, xglm, xlm.
Mixture-of-Experts (MoE)¶
Models that route tokens to a subset of expert MLPs.
Model Type |
Module Class |
Example HuggingFace Model |
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Also registered with MoECausalLMModel: arctic, dbrx,
ernie4_5_moe, flex_olmo, glm4_moe, granitemoe,
granitemoehybrid, granitemoeshared, hunyuan_v1_moe, jetmoe,
minimax, olmoe, qwen3_omni_moe, qwen3_vl_moe.
SSM / State-Space Models¶
Mamba and Mamba2 architectures using selective state-space layers.
Model Type |
Module Class |
Task |
Example HuggingFace Model |
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Hybrid SSM+Attention¶
Models combining Mamba/SSM layers with transformer attention layers.
Model Type |
Module Class |
Task |
Example HuggingFace Model |
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Vision-Language (Multimodal)¶
Models that process both images and text.
Model Type |
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Task |
Example HuggingFace Model |
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Also registered with LLaVAModel (task: vision-language):
aya_vision, chameleon, cohere2_vision, deepseek_vl,
deepseek_vl_hybrid, florence2, fuyu, glm4v, glm4v_moe,
got_ocr2, idefics2, idefics3, instructblip,
instructblipvideo, janus, llava_next_video, llava_onevision,
molmo, ovis2, paligemma, pixtral, smolvlm, video_llava,
vipllava.
Encoder-Only (BERT Family)¶
Models for text embeddings, classification, and feature extraction.
Model Type |
Module Class |
Task |
Example HuggingFace Model |
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Also registered with BertModel (task: feature-extraction):
albert, camembert, data2vec-text, deberta, deberta-v2,
electra, ernie, ernie_m, esm, flaubert, ibert,
megatron-bert, mobilebert, mpnet, nezha, qdqbert, rembert,
roberta-prelayernorm, roc_bert, roformer, splinter,
squeezebert, xlm-roberta, xlm-roberta-xl, xlnet, xmod,
bros, layoutlm, layoutlmv2, lilt, markuplm, mega, mra,
nystromformer, yoso.
Encoder-Decoder (Seq2Seq)¶
Sequence-to-sequence models for translation, summarization, etc.
Model Type |
Module Class |
Task |
Example HuggingFace Model |
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Also registered with BartForConditionalGeneration (task: seq2seq):
bigbird_pegasus, blenderbot, blenderbot-small, fsmt, led,
m2m_100, marian, mvp, nllb-moe, nllb_moe, pegasus,
pegasus_x, plbart, prophetnet, xlm-prophetnet.
Also registered with T5ForConditionalGeneration (task: seq2seq):
longt5, switch_transformers, umt5.
Speech & Audio¶
Speech-to-Text¶
Model Type |
Module Class |
Task |
Example HuggingFace Model |
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Text-to-Speech¶
Model Type |
Module Class |
Task |
Example HuggingFace Model |
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Audio Feature Extraction¶
Model Type |
Module Class |
Task |
Example HuggingFace Model |
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Also registered with Wav2Vec2Model: data2vec-audio, mctct,
musicgen, seamless_m4t, seamless_m4t_v2, sew, sew-d,
speecht5, unispeech, unispeech-sat, voxtral_encoder,
wav2vec2-bert, wav2vec2-conformer.
Vision¶
Image Classification & Feature Extraction¶
Model Type |
Module Class |
Task |
Example HuggingFace Model |
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Also registered with ViTModel: beit, cvt, data2vec-vision,
deit, dinov2, dinov2_with_registers, dinov3_vit, hiera,
ijepa, mobilevit, mobilevitv2, pvt, pvt_v2, swin,
swin2sr, swinv2, vit_hybrid, vit_mae, vit_msn.
Diffusion (via Diffusers)¶
Diffusion models are built through the build_diffusers_pipeline() API
which auto-detects the pipeline type from HuggingFace. These are not
registered in the model registry but are supported as diffusers pipeline
components.
Supported component classes include:
UNet2DConditionModel— Stable Diffusion, SDXL UNetAutoencoderKLModel— VAE encoder/decoderFluxTransformer2DModel— Flux transformerSD3Transformer2DModel— Stable Diffusion 3 transformerDiTTransformer2DModel— DiT (Diffusion Transformer)HunyuanDiT2DModel— Hunyuan-DiTControlNetModel— ControlNet conditioningCogVideoXTransformer3DModel— CogVideoXVideoAutoencoderModel— Video VAEQwenImageTransformer2DModel— Qwen image generation and packed-token Qwen-Image-Edit-2509 denoising with source-image conditioning, masks, and 3D RoPEAutoencoderKLQwenImageModel— Qwen image VAE, including edit-pipeline latent normalizationQwen2_5_VLForConditionalGeneration— image-aware prompt encoder used by Qwen-Image-Edit-2509
from mobius import build
# Auto-detect diffusers pipeline
pkg = build("stabilityai/stable-diffusion-xl-base-1.0")
Quantization Support¶
All decoder-only LLMs and MoE models support quantized weight loading:
Format |
Source |
How |
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GPTQ |
HuggingFace (e.g. |
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AWQ |
HuggingFace (e.g. |
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GGUF |
Local |
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GGUF import uses quantized target storage by default where the selected graph
supports it. Source blocks may be byte-preserved, losslessly repacked, or
lossily dequantized/requantized to a target such as INT4 affine block-32.
Lossy conversion emits one aggregate warning, and every saved package records
source fidelity, storage, and compute semantics in quantization_report.json.
Use --dequantize for an explicitly reported float model.
Encoder GGUF imports for bert and modern-bert select
feature-extraction and expose token-level last_hidden_state only. Pooling,
classifier/reranker heads, generative task overrides, and cache options are
rejected unless their exact output contracts are implemented.