ValueError: Unrecognized model in Qwen/Qwen2.5-7B-Instruct. Should have a `model_type` key in its config.json,
Hello, I keep getting this error with some versions of Qwen.
my transformers._version is 4.46.3
Appreciate any support to fix this
ValueError Traceback (most recent call last)
Cell In[1], line 5
1 from transformers import AutoModelForCausalLM, AutoTokenizer
3 model_name = "Qwen/Qwen2.5-7B-Instruct"
----> 5 model = AutoModelForCausalLM.from_pretrained(
6 model_name,
7 torch_dtype="auto",
8 device_map="auto",
9 trust_remote_code=True
10 )
11 tokenizer = AutoTokenizer.from_pretrained(model_name)
13 prompt = "Give me a short introduction to large language model."
File ~/.local/lib/python3.8/site-packages/transformers/models/auto/auto_factory.py:526, in _BaseAutoModelClass.from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs)
523 if kwargs.get("quantization_config", None) is not None:
524 _ = kwargs.pop("quantization_config")
--> 526 config, kwargs = AutoConfig.from_pretrained(
527 pretrained_model_name_or_path,
528 return_unused_kwargs=True,
529 trust_remote_code=trust_remote_code,
530 code_revision=code_revision,
531 _commit_hash=commit_hash,
532 **hub_kwargs,
533 **kwargs,
534 )
536 # if torch_dtype=auto was passed here, ensure to pass it on
537 if kwargs_orig.get("torch_dtype", None) == "auto":
File ~/.local/lib/python3.8/site-packages/transformers/models/auto/configuration_auto.py:1049, in AutoConfig.from_pretrained(cls, pretrained_model_name_or_path, **kwargs)
1046 if pattern in str(pretrained_model_name_or_path):
1047 return CONFIG_MAPPING[pattern].from_dict(config_dict, **unused_kwargs)
-> 1049 raise ValueError(
1050 f"Unrecognized model in {pretrained_model_name_or_path}. "
1051 f"Should have a model_type key in its {CONFIG_NAME}, or contain one of the following strings "
1052 f"in its name: {', '.join(CONFIG_MAPPING.keys())}"
1053 )
ValueError: Unrecognized model in Qwen/Qwen2.5-7B-Instruct. Should have a model_type key in its config.json, or contain one of the following strings in its name: albert, align, altclip, audio-spectrogram-transformer, autoformer, bark, bart, beit, bert, bert-generation, big_bird, bigbird_pegasus, biogpt, bit, blenderbot, blenderbot-small, blip, blip-2, bloom, 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, conditional_detr, convbert, convnext, convnextv2, cpmant, ctrl, cvt, dac, data2vec-audio, data2vec-text, data2vec-vision, dbrx, deberta, deberta-v2, decision_transformer, deformable_detr, deit, depth_anything, deta, detr, dinat, dinov2, distilbert, donut-swin, dpr, dpt, efficientformer, efficientnet, electra, encodec, encoder-decoder, ernie, ernie_m, esm, falcon, falcon_mamba, fastspeech2_conformer, flaubert, flava, fnet, focalnet, fsmt, funnel, fuyu, gemma, gemma2, git, glm, glpn, gpt-sw3, gpt2, gpt_bigcode, gpt_neo, gpt_neox, gpt_neox_japanese, gptj, gptsan-japanese, granite, granitemoe, graphormer, grounding-dino, groupvit, hiera, hubert, ibert, idefics, idefics2, idefics3, imagegpt, informer, instructblip, instructblipvideo, jamba, jetmoe, jukebox, kosmos-2, layoutlm, layoutlmv2, layoutlmv3, led, levit, lilt, llama, llava, llava_next, llava_next_video, llava_onevision, longformer, longt5, luke, lxmert, m2m_100, mamba, mamba2, marian, markuplm, mask2former, maskformer, maskformer-swin, mbart, mctct, mega, megatron-bert, mgp-str, mimi, mistral, mixtral, mllama, mobilebert, mobilenet_v1, mobilenet_v2, mobilevit, mobilevitv2, moshi, mpnet, mpt, mra, mt5, musicgen, musicgen_melody, mvp, nat, nemotron, nezha, nllb-moe, nougat, nystromformer, olmo, olmoe, omdet-turbo, oneformer, open-llama, openai-gpt, opt, owlv2, owlvit, paligemma, patchtsmixer, patchtst, pegasus, pegasus_x, perceiver, persimmon, phi, phi3, phimoe, pix2struct, pixtral, plbart, poolformer, pop2piano, prophetnet, pvt, pvt_v2, qdqbert, qwen2, qwen2_audio, qwen2_audio_encoder, qwen2_moe, qwen2_vl, rag, realm, recurrent_gemma, reformer, regnet, rembert, resnet, retribert, roberta, roberta-prelayernorm, roc_bert, roformer, rt_detr, rt_detr_resnet, rwkv, sam, seamless_m4t, seamless_m4t_v2, segformer, seggpt, sew, sew-d, siglip, siglip_vision_model, speech-encoder-decoder, speech_to_text, speech_to_text_2, speecht5, splinter, squeezebert, stablelm, starcoder2, superpoint, swiftformer, swin, swin2sr, swinv2, switch_transformers, t5, table-transformer, tapas, time_series_transformer, timesformer, timm_backbone, trajectory_transformer, transfo-xl, trocr, tvlt, tvp, udop, umt5, unispeech, unispeech-sat, univnet, upernet, van, video_llava, videomae, vilt, vipllava, vision-encoder-decoder, vision-text-dual-encoder, visual_bert, vit, vit_hybrid, vit_mae, vit_msn, vitdet, vitmatte, vits, vivit, wav2vec2, wav2vec2-bert, wav2vec2-conformer, wavlm, whisper, xclip, xglm, xlm, xlm-prophetnet, xlm-roberta, xlm-roberta-xl, xlnet, xmod, yolos, yoso, zamba, zoedepth
any solution on this? im running into the same issue
solution: I'm manually inserting the requested type:
python -c '
import json
path = "config.json"
with open(path) as f:
cfg = json.load(f)
cfg["model_type"] = "qwen2"
with open(path, "w") as f:
json.dump(cfg, f, indent=2)
'