ValueError: Unrecognized model in OrdalieTech/Solon-embeddings-large-0.1. Should have a `model_type` key in its config.json
Author: vador31
Pull request: no
Labels: name: bug

### System Info

- Python 3.13.7
- Transformers 4.57.2
- Sentence-transformers 5.2.0.dev0
- Windows 11



### Who can help?

@ArthurZucker @Cyrilvallez

### Information

- [ ] The official example scripts
- [x] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

Just run the following :
```
from sentence_transformers import SentenceTransformer
model=SentenceTransformer("OrdalieTech/Solon-embeddings-large-0.1")
```

Output : 
```
Traceback (most recent call last):
  File "<python-input-1>", line 1, in <module>
    model=SentenceTransformer("OrdalieTech/Solon-embeddings-large-0.1")
  File "D:\Nextcloud\IA\UtopIA_posit\env_UtopIA\Lib\site-packages\sentence_transformers\SentenceTransformer.py", line 327, in __init__
    modules, self.module_kwargs = self._load_sbert_model(
                                  ~~~~~~~~~~~~~~~~~~~~~~^
        model_name_or_path,
        ^^^^^^^^^^^^^^^^^^^
    ...<7 lines>...
        config_kwargs=config_kwargs,
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    )
    ^
  File "D:\Nextcloud\IA\UtopIA_posit\env_UtopIA\Lib\site-packages\sentence_transformers\SentenceTransformer.py", line 2305, in _load_sbert_model
    module = module_class.load(
        model_name_or_path,
    ...<11 lines>...
        backend=self.backend,
    )
  File "D:\Nextcloud\IA\UtopIA_posit\env_UtopIA\Lib\site-packages\sentence_transformers\models\Transformer.py", line 366, in load
    return cls(model_name_or_path=model_name_or_path, **init_kwargs)
  File "D:\Nextcloud\IA\UtopIA_posit\env_UtopIA\Lib\site-packages\sentence_transformers\models\Transformer.py", line 88, in __init__
    config, is_peft_model = self._load_config(model_name_or_path, cache_dir, backend, config_args)
                            ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\Nextcloud\IA\UtopIA_posit\env_UtopIA\Lib\site-packages\sentence_transformers\models\Transformer.py", line 163, in _load_config
    return AutoConfig.from_pretrained(model_name_or_path, **config_args, cache_dir=cache_dir), False
           ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\Nextcloud\IA\UtopIA_posit\env_UtopIA\Lib\site-packages\transformers\models\auto\configuration_auto.py", line 1380, in from_pretrained
    raise ValueError(
    ...<3 lines>...
    )
ValueError: Unrecognized model in OrdalieTech/Solon-embeddings-large-0.1. Should have a `model_type` key in its config.json, or contain one of the following strings in its name: aimv2, aimv2_vision_model, albert, align, altclip, apertus, arcee, aria, aria_text, audio-spectrogram-transformer, autoformer, aya_vision, bamba, bark, bart, beit, bert, bert-generation, big_bird, bigbird_pegasus, biogpt, bit, bitnet, blenderbot, blenderbot-small, blip, blip-2, blip_2_qformer, bloom, blt, bridgetower, bros, camembert, canine, chameleon, chinese_clip, chinese_clip_vision_model, clap, clip, clip_text_model, clip_vision_model, clipseg, clvp, code_llama, codegen, cohere, cohere2, cohere2_vision, colpali, colqwen2, conditional_detr, convbert, convnext, convnextv2, cpmant, csm, ctrl, cvt, d_fine, dab-detr, dac, data2vec-audio, data2vec-text, data2vec-vision, dbrx, deberta, deberta-v2, decision_transformer, deepseek_v2, deepseek_v3, deepseek_vl, deepseek_vl_hybrid, deformable_detr, deit, depth_anything, depth_pro, deta, detr, dia, diffllama, dinat, dinov2, dinov2_with_registers, dinov3_convnext, dinov3_vit, distilbert, doge, donut-swin, dots1, dpr, dpt, edgetam, edgetam_video, edgetam_vision_model, efficientformer, efficientloftr, efficientnet, electra, emu3, encodec, encoder-decoder, eomt, ernie, ernie4_5, ernie4_5_moe, ernie_m, esm, evolla, exaone4, falcon, falcon_h1, falcon_mamba, fastspeech2_conformer, fastspeech2_conformer_with_hifigan, flaubert, flava, flex_olmo, florence2, fnet, focalnet, fsmt, funnel, fuyu, gemma, gemma2, gemma3, gemma3_text, gemma3n, gemma3n_audio, gemma3n_text, gemma3n_vision, git, glm, glm4, glm4_moe, glm4v, glm4v_moe, glm4v_moe_text, glm4v_text, glpn, got_ocr2, gpt-sw3, gpt2, gpt_bigcode, gpt_neo, gpt_neox, gpt_neox_japanese, gpt_oss, gptj, gptsan-japanese, granite, granite_speech, granitemoe, granitemoehybrid, granitemoeshared, granitevision, graphormer, grounding-dino, groupvit, helium, hgnet_v2, hiera, hubert, hunyuan_v1_dense, hunyuan_v1_moe, ibert, idefics, idefics2, idefics3, idefics3_vision, ijepa, imagegpt, informer, instructblip, instructblipvideo, internvl, internvl_vision, jamba, janus, jetmoe, jukebox, kosmos-2, kosmos-2.5, kyutai_speech_to_text, layoutlm, layoutlmv2, layoutlmv3, led, levit, lfm2, lfm2_vl, lightglue, lilt, llama, llama4, llama4_text, llava, llava_next, llava_next_video, llava_onevision, longcat_flash, longformer, longt5, luke, lxmert, m2m_100, mamba, mamba2, marian, markuplm, mask2former, maskformer, maskformer-swin, mbart, mctct, mega, megatron-bert, metaclip_2, mgp-str, mimi, minimax, ministral, mistral, mistral3, mixtral, mlcd, mllama, mm-grounding-dino, mobilebert, mobilenet_v1, mobilenet_v2, mobilevit, mobilevitv2, modernbert, modernbert-decoder, moonshine, moshi, mpnet, mpt, mra, mt5, musicgen, musicgen_melody, mvp, nat, nemotron, nezha, nllb-moe, nougat, nystromformer, olmo, olmo2, olmo3, olmoe, omdet-turbo, oneformer, open-llama, openai-gpt, opt, ovis2, owlv2, owlvit, paligemma, parakeet_ctc, parakeet_encoder, patchtsmixer, patchtst, pegasus, pegasus_x, perceiver, perception_encoder, perception_lm, persimmon, phi, phi3, phi4_multimodal, phimoe, pix2struct, pixtral, plbart, poolformer, pop2piano, prompt_depth_anything, prophetnet, pvt, pvt_v2, qdqbert, qwen2, qwen2_5_omni, qwen2_5_vl, qwen2_5_vl_text, qwen2_audio, qwen2_audio_encoder, qwen2_moe, qwen2_vl, qwen2_vl_text, qwen3, qwen3_moe, qwen3_next, qwen3_omni_moe, qwen3_vl, qwen3_vl_moe, qwen3_vl_moe_text, qwen3_vl_text, rag, realm, recurrent_gemma, reformer, regnet, rembert, resnet, retribert, roberta, roberta-prelayernorm, roc_bert, roformer, rt_detr, rt_detr_resnet, rt_detr_v2, rwkv, sam, sam2, sam2_hiera_det_model, sam2_video, sam2_vision_model, sam_hq, sam_hq_vision_model, sam_vision_model, seamless_m4t, seamless_m4t_v2, seed_oss, segformer, seggpt, sew, sew-d, shieldgemma2, siglip, siglip2, siglip2_vision_model, siglip_vision_model, smollm3, smolvlm, smolvlm_vision, speech-encoder-decoder, speech_to_text, speech_to_text_2, speecht5, splinter, squeezebert, stablelm, starcoder2, superglue, superpoint, swiftformer, swin, swin2sr, swinv2, switch_transformers, t5, t5gemma, table-transformer, tapas, textnet, time_series_transformer, timesfm, timesformer, timm_backbone, timm_wrapper, trajectory_transformer, transfo-xl, trocr, tvlt, tvp, udop, umt5, unispeech, unispeech-sat, univnet, upernet, van, vaultgemma, video_llava, videomae, vilt, vipllava, vision-encoder-decoder, vision-text-dual-encoder, visual_bert, vit, vit_hybrid, vit_mae, vit_msn, vitdet, vitmatte, vitpose, vitpose_backbone, vits, vivit, vjepa2, voxtral, voxtral_encoder, wav2vec2, wav2vec2-bert, wav2vec2-conformer, wavlm, whisper, xclip, xcodec, xglm, xlm, xlm-prophetnet, xlm-roberta, xlm-roberta-xl, xlnet, xlstm, xmod, yolos, yoso, zamba, zamba2, zoedepth
```

Model's config.json:
```
{
  "_name_or_path": "solon-large-06-BIG",
  "architectures": [
    "XLMRobertaModel"
  ],
  "attention_probs_dropout_prob": 0.1,
  "bos_token_id": 0,
  "classifier_dropout": null,
  "eos_token_id": 2,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 1024,
  "initializer_range": 0.02,
  "intermediate_size": 4096,
  "layer_norm_eps": 1e-05,
  "max_position_embeddings": 514,
  "model_type": "xlm-roberta",
  "num_attention_heads": 16,
  "num_hidden_layers": 24,
  "output_past": true,
  "pad_token_id": 1,
  "position_embedding_type": "absolute",
  "torch_dtype": "float32",
  "transformers_version": "4.35.2",
  "type_vocab_size": 1,
  "use_cache": true,
  "vocab_size": 250002
}
```


### Expected behavior

I just want to load the model :/

Comments:
I confirm the same bug for `v4.57.2` regarding `Qwen3-Omni-30B-A3B-Thinking`
 
Reproduce:
```shell
NPROC_PER_NODE=2 \
CUDA_VISIBLE_DEVICES=2,5 \
USE_AUDIO_IN_VIDEO=True \
ENABLE_AUDIO_OUTPUT=False \
swift infer \
    --model "/mnt/Qwen3-Omni-30B-A3B-Thinking" \
    --infer_backend pt \
    --val_dataset query.jsonl \
    --max_new_tokens 128 \
    --attn_impl flash_attention_2  
```

It was fixed when downgraded to `4.57.1`
 

@3manifold @vador31 
The same error `vllm serve Qwen3-VL-8B-Instruct ...` exists in `v4.57.2`, but not in `v4.57.1`.

> I confirm the same bug for `v4.57.2` regarding `Qwen3-Omni-30B-A3B-Thinking`
> 
> It was fixed when downgraded to `4.57.1`

Not for me :/

If `4.57.1` does not fix it let us have alook, sorry everyone again

Ok, had trouble to repro but can you share the complete error log?

~Weirdly enough, I can get an error when not having `hf_transfer` installed but when I install it, it works again.~ Was may local envs, checking with a fresh install.

Edit: Really can't reproduce and I'm cleaning my cache from the model at each retry
```text
sentence-transformers==5.1.0  # also tried dev version at commit 6aaa53b
transformers==4.57.2
```
Running (env disabled, shouldn't matter):
`HF_HUB_ENABLE_HF_TRANSFER=0 python -c "from sentence_transformers import SentenceTransformer; model = SentenceTransformer('OrdalieTech/Solon-embeddings-large-0.1'); print(model)"`

I tried with a new venv :

Powershell :
```
python -m venv env_train
.\env_train\Scripts\activate
python -m pip install --upgrade pip
pip install transformers sentence-transformers torch==2.6
python
```

Python :
```
>>> from sentence_transformers import SentenceTransformer
>>> model=SentenceTransformer("OrdalieTech/Solon-embeddings-large-0.1")
Traceback (most recent call last):
  File "<python-input-1>", line 1, in <module>
    model=SentenceTransformer("OrdalieTech/Solon-embeddings-large-0.1")
  File "D:\Nextcloud\IA\UtopIA_posit\env_train\Lib\site-packages\sentence_transformers\SentenceTransformer.py", line 327, in __init__
    modules, self.module_kwargs = self._load_sbert_model(
                                  ~~~~~~~~~~~~~~~~~~~~~~^
        model_name_or_path,
        ^^^^^^^^^^^^^^^^^^^
    ...<7 lines>...
        config_kwargs=config_kwargs,
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    )
    ^
  File "D:\Nextcloud\IA\UtopIA_posit\env_train\Lib\site-packages\sentence_transformers\SentenceTransformer.py", line 2305, in _load_sbert_model
    module = module_class.load(
        model_name_or_path,
    ...<11 lines>...
        backend=self.backend,
    )
  File "D:\Nextcloud\IA\UtopIA_posit\env_train\Lib\site-packages\sentence_transformers\models\Transformer.py", line 365, in load
    return cls(model_name_or_path=model_name_or_path, **init_kwargs)
  File "D:\Nextcloud\IA\UtopIA_posit\env_train\Lib\site-packages\sentence_transformers\models\Transformer.py", line 87, in __init__
    config, is_peft_model = self._load_config(model_name_or_path, cache_dir, backend, config_args)
                            ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\Nextcloud\IA\UtopIA_posit\env_train\Lib\site-packages\sentence_transformers\models\Transformer.py", line 162, in _load_config
    return AutoConfig.from_pretrained(model_name_or_path, **config_args, cache_dir=cache_dir), False
           ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\Nextcloud\IA\UtopIA_posit\env_train\Lib\site-packages\transformers\models\auto\configuration_auto.py", line 1380, in from_pretrained
    raise ValueError(
    ...<3 lines>...
    )
ValueError: Unrecognized model in OrdalieTech/Solon-embeddings-large-0.1. Should have a `model_type` key in its config.json, or contain one of the following strings in its name: aimv2, aimv2_vision_model, albert, align, altclip, apertus, arcee, aria, aria_text, audio-spectrogram-transformer, autoformer, aya_vision, bamba, bark, bart, beit, bert, bert-generation, big_bird, bigbird_pegasus, biogpt, bit, bitnet, blenderbot, blenderbot-small, blip, blip-2, blip_2_qformer, bloom, blt, bridgetower, bros, camembert, canine, chameleon, chinese_clip, chinese_clip_vision_model, clap, clip, clip_text_model, clip_vision_model, clipseg, clvp, code_llama, codegen, cohere, cohere2, cohere2_vision, colpali, colqwen2, conditional_detr, convbert, convnext, convnextv2, cpmant, csm, ctrl, cvt, d_fine, dab-detr, dac, data2vec-audio, data2vec-text, data2vec-vision, dbrx, deberta, deberta-v2, decision_transformer, deepseek_v2, deepseek_v3, deepseek_vl, deepseek_vl_hybrid, deformable_detr, deit, depth_anything, depth_pro, deta, detr, dia, diffllama, dinat, dinov2, dinov2_with_registers, dinov3_convnext, dinov3_vit, distilbert, doge, donut-swin, dots1, dpr, dpt, edgetam, edgetam_video, edgetam_vision_model, efficientformer, efficientloftr, efficientnet, electra, emu3, encodec, encoder-decoder, eomt, ernie, ernie4_5, ernie4_5_moe, ernie_m, esm, evolla, exaone4, falcon, falcon_h1, falcon_mamba, fastspeech2_conformer, fastspeech2_conformer_with_hifigan, flaubert, flava, flex_olmo, florence2, fnet, focalnet, fsmt, funnel, fuyu, gemma, gemma2, gemma3, gemma3_text, gemma3n, gemma3n_audio, gemma3n_text, gemma3n_vision, git, glm, glm4, glm4_moe, glm4v, glm4v_moe, glm4v_moe_text, glm4v_text, glpn, got_ocr2, gpt-sw3, gpt2, gpt_bigcode, gpt_neo, gpt_neox, gpt_neox_japanese, gpt_oss, gptj, gptsan-japanese, granite, granite_speech, granitemoe, granitemoehybrid, granitemoeshared, granitevision, graphormer, grounding-dino, groupvit, helium, hgnet_v2, hiera, hubert, hunyuan_v1_dense, hunyuan_v1_moe, ibert, idefics, idefics2, idefics3, idefics3_vision, ijepa, imagegpt, informer, instructblip, instructblipvideo, internvl, internvl_vision, jamba, janus, jetmoe, jukebox, kosmos-2, kosmos-2.5, kyutai_speech_to_text, layoutlm, layoutlmv2, layoutlmv3, led, levit, lfm2, lfm2_vl, lightglue, lilt, llama, llama4, llama4_text, llava, llava_next, llava_next_video, llava_onevision, longcat_flash, longformer, longt5, luke, lxmert, m2m_100, mamba, mamba2, marian, markuplm, mask2former, maskformer, maskformer-swin, mbart, mctct, mega, megatron-bert, metaclip_2, mgp-str, mimi, minimax, ministral, mistral, mistral3, mixtral, mlcd, mllama, mm-grounding-dino, mobilebert, mobilenet_v1, mobilenet_v2, mobilevit, mobilevitv2, modernbert, modernbert-decoder, moonshine, moshi, mpnet, mpt, mra, mt5, musicgen, musicgen_melody, mvp, nat, nemotron, nezha, nllb-moe, nougat, nystromformer, olmo, olmo2, olmo3, olmoe, omdet-turbo, oneformer, open-llama, openai-gpt, opt, ovis2, owlv2, owlvit, paligemma, parakeet_ctc, parakeet_encoder, patchtsmixer, patchtst, pegasus, pegasus_x, perceiver, perception_encoder, perception_lm, persimmon, phi, phi3, phi4_multimodal, phimoe, pix2struct, pixtral, plbart, poolformer, pop2piano, prompt_depth_anything, prophetnet, pvt, pvt_v2, qdqbert, qwen2, qwen2_5_omni, qwen2_5_vl, qwen2_5_vl_text, qwen2_audio, qwen2_audio_encoder, qwen2_moe, qwen2_vl, qwen2_vl_text, qwen3, qwen3_moe, qwen3_next, qwen3_omni_moe, qwen3_vl, qwen3_vl_moe, qwen3_vl_moe_text, qwen3_vl_text, rag, realm, recurrent_gemma, reformer, regnet, rembert, resnet, retribert, roberta, roberta-prelayernorm, roc_bert, roformer, rt_detr, rt_detr_resnet, rt_detr_v2, rwkv, sam, sam2, sam2_hiera_det_model, sam2_video, sam2_vision_model, sam_hq, sam_hq_vision_model, sam_vision_model, seamless_m4t, seamless_m4t_v2, seed_oss, segformer, seggpt, sew, sew-d, shieldgemma2, siglip, siglip2, siglip2_vision_model, siglip_vision_model, smollm3, smolvlm, smolvlm_vision, speech-encoder-decoder, speech_to_text, speech_to_text_2, speecht5, splinter, squeezebert, stablelm, starcoder2, superglue, superpoint, swiftformer, swin, swin2sr, swinv2, switch_transformers, t5, t5gemma, table-transformer, tapas, textnet, time_series_transformer, timesfm, timesformer, timm_backbone, timm_wrapper, trajectory_transformer, transfo-xl, trocr, tvlt, tvp, udop, umt5, unispeech, unispeech-sat, univnet, upernet, van, vaultgemma, video_llava, videomae, vilt, vipllava, vision-encoder-decoder, vision-text-dual-encoder, visual_bert, vit, vit_hybrid, vit_mae, vit_msn, vitdet, vitmatte, vitpose, vitpose_backbone, vits, vivit, vjepa2, voxtral, voxtral_encoder, wav2vec2, wav2vec2-bert, wav2vec2-conformer, wavlm, whisper, xclip, xcodec, xglm, xlm, xlm-prophetnet, xlm-roberta, xlm-roberta-xl, xlnet, xlstm, xmod, yolos, yoso, zamba, zamba2, zoedepth
```

I checked uv vs pip and I also get the same versions in essence:
```text
certifi==2025.11.12
charset-normalizer==3.4.4
filelock==3.20.0
fsspec==2025.10.0
hf-xet==1.2.0
huggingface-hub==0.36.0
idna==3.11
jinja2==3.1.6
joblib==1.5.2
markupsafe==3.0.3
mpmath==1.3.0
networkx==3.6
numpy==2.3.5
nvidia-cublas-cu12==12.4.5.8
nvidia-cuda-cupti-cu12==12.4.127
nvidia-cuda-nvrtc-cu12==12.4.127
nvidia-cuda-runtime-cu12==12.4.127
nvidia-cudnn-cu12==9.1.0.70
nvidia-cufft-cu12==11.2.1.3
nvidia-curand-cu12==10.3.5.147
nvidia-cusolver-cu12==11.6.1.9
nvidia-cusparse-cu12==12.3.1.170
nvidia-cusparselt-cu12==0.6.2
nvidia-nccl-cu12==2.21.5
nvidia-nvjitlink-cu12==12.4.127
nvidia-nvtx-cu12==12.4.127
packaging==25.0
pillow==12.0.0
pyyaml==6.0.3
regex==2025.11.3
requests==2.32.5
safetensors==0.7.0
scikit-learn==1.7.2
scipy==1.16.3
sentence-transformers==5.1.2
sympy==1.13.1
threadpoolctl==3.6.0
tokenizers==0.22.1
torch==2.6.0
tqdm==4.67.1
transformers==4.57.2
triton==3.2.0
typing-extensions==4.15.0
urllib3==2.5.0
```

I do not get into python directly, just calling `HF_HUB_ENABLE_HF_TRANSFER=0 python -c "from sentence_transformers import SentenceTransformer; model = SentenceTransformer('OrdalieTech/Solon-embeddings-large-0.1'); print(model)"`. Can you check this with `python -c`?

Ig `.\env_train\Scripts\activate` is the way to activate under win.

```
PS D:\Nextcloud\IA\UtopIA_posit> $env:HF_HUB_ENABLE_HF_TRANSFER=0
PS D:\Nextcloud\IA\UtopIA_posit> python -c "from sentence_transformers import SentenceTransformer; model = SentenceTransformer('OrdalieTech/Solon-embeddings-large-0.1'); print(model)"
Traceback (most recent call last):
  File "<string>", line 1, in <module>
    from sentence_transformers import SentenceTransformer; model = SentenceTransformer('OrdalieTech/Solon-embeddings-large-0.1'); print(model)
                                                                   ~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\User\AppData\Roaming\Python\Python313\site-packages\sentence_transformers\SentenceTransformer.py", line 327, in __init__
    modules, self.module_kwargs = self._load_sbert_model(
                                  ~~~~~~~~~~~~~~~~~~~~~~^
        model_name_or_path,
        ^^^^^^^^^^^^^^^^^^^
    ...<7 lines>...
        config_kwargs=config_kwargs,
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    )
    ^
  File "C:\Users\User\AppData\Roaming\Python\Python313\site-packages\sentence_transformers\SentenceTransformer.py", line 2305, in _load_sbert_model
    module = module_class.load(
        model_name_or_path,
    ...<11 lines>...
        backend=self.backend,
    )
  File "C:\Users\User\AppData\Roaming\Python\Python313\site-packages\sentence_transformers\models\Transformer.py", line 365, in load
    return cls(model_name_or_path=model_name_or_path, **init_kwargs)
  File "C:\Users\User\AppData\Roaming\Python\Python313\site-packages\sentence_transformers\models\Transformer.py", line 87, in __init__
    config, is_peft_model = self._load_config(model_name_or_path, cache_dir, backend, config_args)
                            ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\User\AppData\Roaming\Python\Python313\site-packages\sentence_transformers\models\Transformer.py", line 162, in _load_config
    return AutoConfig.from_pretrained(model_name_or_path, **config_args, cache_dir=cache_dir), False
           ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\User\AppData\Roaming\Python\Python313\site-packages\transformers\models\auto\configuration_auto.py", line 1380, in from_pretrained
    raise ValueError(
    ...<3 lines>...
    )
ValueError: Unrecognized model in OrdalieTech/Solon-embeddings-large-0.1. Should have a `model_type` key in its config.json, or contain one of the following strings in its name: aimv2, aimv2_vision_model, albert, align, altclip, apertus, arcee, aria, aria_text, audio-spectrogram-transformer, autoformer, aya_vision, bamba, bark, bart, beit, bert, bert-generation, big_bird, bigbird_pegasus, biogpt, bit, bitnet, blenderbot, blenderbot-small, blip, blip-2, blip_2_qformer, bloom, blt, bridgetower, bros, camembert, canine, chameleon, chinese_clip, chinese_clip_vision_model, clap, clip, clip_text_model, clip_vision_model, clipseg, clvp, code_llama, codegen, cohere, cohere2, cohere2_vision, colpali, colqwen2, conditional_detr, convbert, convnext, convnextv2, cpmant, csm, ctrl, cvt, d_fine, dab-detr, dac, data2vec-audio, data2vec-text, data2vec-vision, dbrx, deberta, deberta-v2, decision_transformer, deepseek_v2, deepseek_v3, deepseek_vl, deepseek_vl_hybrid, deformable_detr, deit, depth_anything, depth_pro, deta, detr, dia, diffllama, dinat, dinov2, dinov2_with_registers, dinov3_convnext, dinov3_vit, distilbert, doge, donut-swin, dots1, dpr, dpt, edgetam, edgetam_video, edgetam_vision_model, efficientformer, efficientloftr, efficientnet, electra, emu3, encodec, encoder-decoder, eomt, ernie, ernie4_5, ernie4_5_moe, ernie_m, esm, evolla, exaone4, falcon, falcon_h1, falcon_mamba, fastspeech2_conformer, fastspeech2_conformer_with_hifigan, flaubert, flava, flex_olmo, florence2, fnet, focalnet, fsmt, funnel, fuyu, gemma, gemma2, gemma3, gemma3_text, gemma3n, gemma3n_audio, gemma3n_text, gemma3n_vision, git, glm, glm4, glm4_moe, glm4v, glm4v_moe, glm4v_moe_text, glm4v_text, glpn, got_ocr2, gpt-sw3, gpt2, gpt_bigcode, gpt_neo, gpt_neox, gpt_neox_japanese, gpt_oss, gptj, gptsan-japanese, granite, granite_speech, granitemoe, granitemoehybrid, granitemoeshared, granitevision, graphormer, grounding-dino, groupvit, helium, hgnet_v2, hiera, hubert, hunyuan_v1_dense, hunyuan_v1_moe, ibert, idefics, idefics2, idefics3, idefics3_vision, ijepa, imagegpt, informer, instructblip, instructblipvideo, internvl, internvl_vision, jamba, janus, jetmoe, jukebox, kosmos-2, kosmos-2.5, kyutai_speech_to_text, layoutlm, layoutlmv2, layoutlmv3, led, levit, lfm2, lfm2_vl, lightglue, lilt, llama, llama4, llama4_text, llava, llava_next, llava_next_video, llava_onevision, longcat_flash, longformer, longt5, luke, lxmert, m2m_100, mamba, mamba2, marian, markuplm, mask2former, maskformer, maskformer-swin, mbart, mctct, mega, megatron-bert, metaclip_2, mgp-str, mimi, minimax, ministral, mistral, mistral3, mixtral, mlcd, mllama, mm-grounding-dino, mobilebert, mobilenet_v1, mobilenet_v2, mobilevit, mobilevitv2, modernbert, modernbert-decoder, moonshine, moshi, mpnet, mpt, mra, mt5, musicgen, musicgen_melody, mvp, nat, nemotron, nezha, nllb-moe, nougat, nystromformer, olmo, olmo2, olmo3, olmoe, omdet-turbo, oneformer, open-llama, openai-gpt, opt, ovis2, owlv2, owlvit, paligemma, parakeet_ctc, parakeet_encoder, patchtsmixer, patchtst, pegasus, pegasus_x, perceiver, perception_encoder, perception_lm, persimmon, phi, phi3, phi4_multimodal, phimoe, pix2struct, pixtral, plbart, poolformer, pop2piano, prompt_depth_anything, prophetnet, pvt, pvt_v2, qdqbert, qwen2, qwen2_5_omni, qwen2_5_vl, qwen2_5_vl_text, qwen2_audio, qwen2_audio_encoder, qwen2_moe, qwen2_vl, qwen2_vl_text, qwen3, qwen3_moe, qwen3_next, qwen3_omni_moe, qwen3_vl, qwen3_vl_moe, qwen3_vl_moe_text, qwen3_vl_text, rag, realm, recurrent_gemma, reformer, regnet, rembert, resnet, retribert, roberta, roberta-prelayernorm, roc_bert, roformer, rt_detr, rt_detr_resnet, rt_detr_v2, rwkv, sam, sam2, sam2_hiera_det_model, sam2_video, sam2_vision_model, sam_hq, sam_hq_vision_model, sam_vision_model, seamless_m4t, seamless_m4t_v2, seed_oss, segformer, seggpt, sew, sew-d, shieldgemma2, siglip, siglip2, siglip2_vision_model, siglip_vision_model, smollm3, smolvlm, smolvlm_vision, speech-encoder-decoder, speech_to_text, speech_to_text_2, speecht5, splinter, squeezebert, stablelm, starcoder2, superglue, superpoint, swiftformer, swin, swin2sr, swinv2, switch_transformers, t5, t5gemma, table-transformer, tapas, textnet, time_series_transformer, timesfm, timesformer, timm_backbone, timm_wrapper, trajectory_transformer, transfo-xl, trocr, tvlt, tvp, udop, umt5, unispeech, unispeech-sat, univnet, upernet, van, vaultgemma, video_llava, videomae, vilt, vipllava, vision-encoder-decoder, vision-text-dual-encoder, visual_bert, vit, vit_hybrid, vit_mae, vit_msn, vitdet, vitmatte, vitpose, vitpose_backbone, vits, vivit, vjepa2, voxtral, voxtral_encoder, wav2vec2, wav2vec2-bert, wav2vec2-conformer, wavlm, whisper, xclip, xcodec, xglm, xlm, xlm-prophetnet, xlm-roberta, xlm-roberta-xl, xlnet, xlstm, xmod, yolos, yoso, zamba, zamba2, zoedepth
```

```
PS D:\Nextcloud\IA\UtopIA_posit> pip freeze
certifi==2025.11.12
charset-normalizer==3.4.4
colorama==0.4.6
filelock==3.20.0
fsspec==2025.10.0
huggingface-hub==0.36.0
idna==3.11
Jinja2==3.1.6
joblib==1.5.2
MarkupSafe==3.0.3
mpmath==1.3.0
networkx==3.6
numpy==2.3.5
packaging==25.0
pillow==12.0.0
PyYAML==6.0.3
regex==2025.11.3
requests==2.32.5
safetensors==0.7.0
scikit-learn==1.7.2
scipy==1.16.3
sentence-transformers==5.1.2
setuptools==80.9.0
sympy==1.14.0
threadpoolctl==3.6.0
tokenizers==0.22.1
torch==2.9.1
tqdm==4.67.1
transformers==4.57.3
typing_extensions==4.15.0
urllib3==2.5.0
```

This is super weird, still can't repro 😓 also tried the vllm example. Is it maybe specific to windows? Are the other users also experiencing this under different OS?

My last guess/suggestion would be to clean the cache (completely) so that you definitely cannot have a corrupted state

Hello!

I'm also unable to reproduce this using the same environment (Python, Transformers, Sentence Transformers, Windows). Could you try and run:
```python
>>> from transformers import PreTrainedConfig
>>> PreTrainedConfig.get_config_dict("OrdalieTech/Solon-embeddings-large-0.1")
({'_name_or_path': 'solon-large-06-BIG', 'architectures': ['XLMRobertaModel'], 'attention_probs_dropout_prob': 0.1, 'bos_token_id': 0, 'classifier_dropout': None, 'eos_token_id': 2, 'hidden_act': 'gelu', 'hidden_dropout_prob': 0.1, 'hidden_size': 1024, 'initializer_range': 0.02, 'intermediate_size': 4096, 'layer_norm_eps': 1e-05, 'max_position_embeddings': 514, 'model_type': 'xlm-roberta', 'num_attention_heads': 16, 'num_hidden_layers': 24, 'output_past': True, 'pad_token_id': 1, 'position_embedding_type': 'absolute', 'torch_dtype': 'float32', 'transformers_version': '4.35.2', 'type_vocab_size': 1, 'use_cache': True, 'vocab_size': 250002, '_commit_hash': '9f6465f6ea2f6d10c6294bc15d84edf87d47cdef'}, {})
```

My suspicion is that you're getting an empty dict from that, although I can't really figure out how that would happen unless you have an incorrectly cached version of the `config.json` with just `{}`, as there's lots of different error paths that you're somehow avoiding.

My guess is a broken cache or `transformers` install. Clearing the former and reinstalling the latter are my recommendations.

- Tom Aarsen

Hello, another weird message :

```
>>> from transformers import PreTrainedConfig
Traceback (most recent call last):
  File "<python-input-0>", line 1, in <module>
    from transformers import PreTrainedConfig
ImportError: cannot import name 'PreTrainedConfig' from 'transformers' (D:\Nextcloud\IA\UtopIA_posit\env_train\Lib\site-packages\transformers\__init__.py)
```

Same with Ubuntu 24 (WSL)

```
>>> from transformers import PreTrainedConfig
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ImportError: cannot import name 'PreTrainedConfig' from 'transformers' (/home/david/env_test/lib/python3.12/site-packages/transformers/__init__.py)
```

### Pointing to the same HF_HOME folder  :

- **Ubuntu 24 (WSL) python 3.12.3 :**
```
>>> from sentence_transformers import SentenceTransformer
>>> model=SentenceTransformer("OrdalieTech/Solon-embeddings-large-0.1")
>>> print(model)
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'XLMRobertaModel'})
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
```
```
certifi==2025.11.12
charset-normalizer==3.4.4
filelock==3.20.0
fsspec==2025.10.0
hf-xet==1.2.0
huggingface-hub==0.36.0
idna==3.11
Jinja2==3.1.6
joblib==1.5.2
MarkupSafe==3.0.3
mpmath==1.3.0
networkx==3.6
numpy==2.3.5
nvidia-cublas-cu12==12.4.5.8
nvidia-cuda-cupti-cu12==12.4.127
nvidia-cuda-nvrtc-cu12==12.4.127
nvidia-cuda-runtime-cu12==12.4.127
nvidia-cudnn-cu12==9.1.0.70
nvidia-cufft-cu12==11.2.1.3
nvidia-curand-cu12==10.3.5.147
nvidia-cusolver-cu12==11.6.1.9
nvidia-cusparse-cu12==12.3.1.170
nvidia-cusparselt-cu12==0.6.2
nvidia-nccl-cu12==2.21.5
nvidia-nvjitlink-cu12==12.4.127
nvidia-nvtx-cu12==12.4.127
packaging==25.0
pillow==12.0.0
PyYAML==6.0.3
regex==2025.11.3
requests==2.32.5
safetensors==0.7.0
scikit-learn==1.7.2
scipy==1.16.3
sentence-transformers==5.1.2
setuptools==80.9.0
sympy==1.13.1
threadpoolctl==3.6.0
tokenizers==0.22.1
torch==2.6.0
tqdm==4.67.1
transformers==4.57.3
triton==3.2.0
typing_extensions==4.15.0
urllib3==2.5.0
```

- **W11 python 3.13.7 :**
```
>>> from sentence_transformers import SentenceTransformer
>>> model=SentenceTransformer("OrdalieTech/Solon-embeddings-large-0.1")
modules.json: 349B [00:00, 822kB/s]
D:\Nextcloud\IA\UtopIA_posit\env_train\Lib\site-packages\huggingface_hub\file_download.py:143: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in D:\HF_HOME\hub\models--OrdalieTech--Solon-embeddings-large-0.1. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.
To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development
  warnings.warn(message)
config_sentence_transformers.json: 123B [00:00, 619kB/s]
README.md: 20.4kB [00:00, 940kB/s]
sentence_bert_config.json: 53.0B [00:00, 5.39kB/s]
Traceback (most recent call last):
  File "<python-input-1>", line 1, in <module>
    model=SentenceTransformer("OrdalieTech/Solon-embeddings-large-0.1")
  File "D:\Nextcloud\IA\UtopIA_posit\env_train\Lib\site-packages\sentence_transformers\SentenceTransformer.py", line 327, in __init__
    modules, self.module_kwargs = self._load_sbert_model(
                                  ~~~~~~~~~~~~~~~~~~~~~~^
        model_name_or_path,
        ^^^^^^^^^^^^^^^^^^^
    ...<7 lines>...
        config_kwargs=config_kwargs,
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    )
    ^
  File "D:\Nextcloud\IA\UtopIA_posit\env_train\Lib\site-packages\sentence_transformers\SentenceTransformer.py", line 2305, in _load_sbert_model
    module = module_class.load(
        model_name_or_path,
    ...<11 lines>...
        backend=self.backend,
    )
  File "D:\Nextcloud\IA\UtopIA_posit\env_train\Lib\site-packages\sentence_transformers\models\Transformer.py", line 365, in load
    return cls(model_name_or_path=model_name_or_path, **init_kwargs)
  File "D:\Nextcloud\IA\UtopIA_posit\env_train\Lib\site-packages\sentence_transformers\models\Transformer.py", line 87, in __init__
    config, is_peft_model = self._load_config(model_name_or_path, cache_dir, backend, config_args)
                            ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\Nextcloud\IA\UtopIA_posit\env_train\Lib\site-packages\sentence_transformers\models\Transformer.py", line 162, in _load_config
    return AutoConfig.from_pretrained(model_name_or_path, **config_args, cache_dir=cache_dir), False
           ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\Nextcloud\IA\UtopIA_posit\env_train\Lib\site-packages\transformers\models\auto\configuration_auto.py", line 1380, in from_pretrained
    raise ValueError(
    ...<3 lines>...
    )
ValueError: Unrecognized model in OrdalieTech/Solon-embeddings-large-0.1. Should have a `model_type` key in its config.json, or contain one of the following strings in its name: aimv2, aimv2_vision_model, albert, align, altclip, apertus, arcee, aria, aria_text, audio-spectrogram-transformer, autoformer, aya_vision, bamba, bark, bart, beit, bert, bert-generation, big_bird, bigbird_pegasus, biogpt, bit, bitnet, blenderbot, blenderbot-small, blip, blip-2, blip_2_qformer, bloom, blt, bridgetower, bros, camembert, canine, chameleon, chinese_clip, chinese_clip_vision_model, clap, clip, clip_text_model, clip_vision_model, clipseg, clvp, code_llama, codegen, cohere, cohere2, cohere2_vision, colpali, colqwen2, conditional_detr, convbert, convnext, convnextv2, cpmant, csm, ctrl, cvt, d_fine, dab-detr, dac, data2vec-audio, data2vec-text, data2vec-vision, dbrx, deberta, deberta-v2, decision_transformer, deepseek_v2, deepseek_v3, deepseek_vl, deepseek_vl_hybrid, deformable_detr, deit, depth_anything, depth_pro, deta, detr, dia, diffllama, dinat, dinov2, dinov2_with_registers, dinov3_convnext, dinov3_vit, distilbert, doge, donut-swin, dots1, dpr, dpt, edgetam, edgetam_video, edgetam_vision_model, efficientformer, efficientloftr, efficientnet, electra, emu3, encodec, encoder-decoder, eomt, ernie, ernie4_5, ernie4_5_moe, ernie_m, esm, evolla, exaone4, falcon, falcon_h1, falcon_mamba, fastspeech2_conformer, fastspeech2_conformer_with_hifigan, flaubert, flava, flex_olmo, florence2, fnet, focalnet, fsmt, funnel, fuyu, gemma, gemma2, gemma3, gemma3_text, gemma3n, gemma3n_audio, gemma3n_text, gemma3n_vision, git, glm, glm4, glm4_moe, glm4v, glm4v_moe, glm4v_moe_text, glm4v_text, glpn, got_ocr2, gpt-sw3, gpt2, gpt_bigcode, gpt_neo, gpt_neox, gpt_neox_japanese, gpt_oss, gptj, gptsan-japanese, granite, granite_speech, granitemoe, granitemoehybrid, granitemoeshared, granitevision, graphormer, grounding-dino, groupvit, helium, hgnet_v2, hiera, hubert, hunyuan_v1_dense, hunyuan_v1_moe, ibert, idefics, idefics2, idefics3, idefics3_vision, ijepa, imagegpt, informer, instructblip, instructblipvideo, internvl, internvl_vision, jamba, janus, jetmoe, jukebox, kosmos-2, kosmos-2.5, kyutai_speech_to_text, layoutlm, layoutlmv2, layoutlmv3, led, levit, lfm2, lfm2_vl, lightglue, lilt, llama, llama4, llama4_text, llava, llava_next, llava_next_video, llava_onevision, longcat_flash, longformer, longt5, luke, lxmert, m2m_100, mamba, mamba2, marian, markuplm, mask2former, maskformer, maskformer-swin, mbart, mctct, mega, megatron-bert, metaclip_2, mgp-str, mimi, minimax, ministral, mistral, mistral3, mixtral, mlcd, mllama, mm-grounding-dino, mobilebert, mobilenet_v1, mobilenet_v2, mobilevit, mobilevitv2, modernbert, modernbert-decoder, moonshine, moshi, mpnet, mpt, mra, mt5, musicgen, musicgen_melody, mvp, nat, nemotron, nezha, nllb-moe, nougat, nystromformer, olmo, olmo2, olmo3, olmoe, omdet-turbo, oneformer, open-llama, openai-gpt, opt, ovis2, owlv2, owlvit, paligemma, parakeet_ctc, parakeet_encoder, patchtsmixer, patchtst, pegasus, pegasus_x, perceiver, perception_encoder, perception_lm, persimmon, phi, phi3, phi4_multimodal, phimoe, pix2struct, pixtral, plbart, poolformer, pop2piano, prompt_depth_anything, prophetnet, pvt, pvt_v2, qdqbert, qwen2, qwen2_5_omni, qwen2_5_vl, qwen2_5_vl_text, qwen2_audio, qwen2_audio_encoder, qwen2_moe, qwen2_vl, qwen2_vl_text, qwen3, qwen3_moe, qwen3_next, qwen3_omni_moe, qwen3_vl, qwen3_vl_moe, qwen3_vl_moe_text, qwen3_vl_text, rag, realm, recurrent_gemma, reformer, regnet, rembert, resnet, retribert, roberta, roberta-prelayernorm, roc_bert, roformer, rt_detr, rt_detr_resnet, rt_detr_v2, rwkv, sam, sam2, sam2_hiera_det_model, sam2_video, sam2_vision_model, sam_hq, sam_hq_vision_model, sam_vision_model, seamless_m4t, seamless_m4t_v2, seed_oss, segformer, seggpt, sew, sew-d, shieldgemma2, siglip, siglip2, siglip2_vision_model, siglip_vision_model, smollm3, smolvlm, smolvlm_vision, speech-encoder-decoder, speech_to_text, speech_to_text_2, speecht5, splinter, squeezebert, stablelm, starcoder2, superglue, superpoint, swiftformer, swin, swin2sr, swinv2, switch_transformers, t5, t5gemma, table-transformer, tapas, textnet, time_series_transformer, timesfm, timesformer, timm_backbone, timm_wrapper, trajectory_transformer, transfo-xl, trocr, tvlt, tvp, udop, umt5, unispeech, unispeech-sat, univnet, upernet, van, vaultgemma, video_llava, videomae, vilt, vipllava, vision-encoder-decoder, vision-text-dual-encoder, visual_bert, vit, vit_hybrid, vit_mae, vit_msn, vitdet, vitmatte, vitpose, vitpose_backbone, vits, vivit, vjepa2, voxtral, voxtral_encoder, wav2vec2, wav2vec2-bert, wav2vec2-conformer, wavlm, whisper, xclip, xcodec, xglm, xlm, xlm-prophetnet, xlm-roberta, xlm-roberta-xl, xlnet, xlstm, xmod, yolos, yoso, zamba, zamba2, zoedepth
```
```
certifi==2025.11.12
charset-normalizer==3.4.4
colorama==0.4.6
filelock==3.20.0
fsspec==2025.10.0
huggingface-hub==0.36.0
idna==3.11
Jinja2==3.1.6
joblib==1.5.2
MarkupSafe==3.0.3
mpmath==1.3.0
networkx==3.6
numpy==2.3.5
packaging==25.0
pillow==12.0.0
PyYAML==6.0.3
regex==2025.11.3
requests==2.32.5
safetensors==0.7.0
scikit-learn==1.7.2
scipy==1.16.3
sentence-transformers==5.1.2
setuptools==80.9.0
sympy==1.13.1
threadpoolctl==3.6.0
tokenizers==0.22.1
torch==2.6.0
tqdm==4.67.1
transformers==4.57.2
typing_extensions==4.15.0
urllib3==2.5.0
```

I noticed that on linux i was with transformers 4.57.3 so I upgraded W11 but same issue

@tomaarsen @vasqu I cleared __pycache__ folder, is that enough to clear the cache ? The issue is still there after that :/

Apologies, I should have been a bit clearer.
To remove your cache for this model, delete the folder in `~/.cache/huggingface/hub/models--OrdalieTech--Solon-embeddings-large-0.1`, where `~` is your home folder, e.g. `C:/Users/vador`.

- Tom Aarsen

The cache location can also be found via envs, e.g. something along `hf env` --> `HF_HUB_CACHE`

OK, thank you !

Deleted again (at least for the 5th time) my [HF_HOME]/hub/models--OrdalieTech--Solon-embeddings-large-0.1 folder and the issue is still there :/

Re-re-re-re-tried also "hf download OrdalieTech/Solon-embeddings-large-0.1", no difference :/

With another model it works... :(
```
>>> from sentence_transformers import SentenceTransformer
>>> model=SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
>>> print(model)
SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)
```

This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) are likely to be ignored.