سلام. من در حال اجرای مدلم هستم. خروجی: ==((====))==  Unsloth 2025.2.5: Fast Phi3 vision patching. Transformers: 4.48.2.
   \\   /|    GPU: Tesla T4. Max memory: 14.741 GB. Platform: Linux.
O^O/ \_/ \    Torch: 2.5.1+cu124. CUDA: 7.5. CUDA Toolkit: 12.4. Triton: 3.1.0
\        /    Bfloat16 = FALSE. FA [Xformers = 0.0.29. FA2 = False]
 "-____-"     Free Apache license: http://github.com/unslothai/unsloth
Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-6-d05b54edb724> in <cell line: 0>()
     18 ]  # More models at https://docs.unsloth.ai/get-started/all-our-models
     19 
---> 20 model, tokenizer = FastVisionModel .from_pretrained(
     21     model_name = "arshiaafshani/Arsh-V1",
     22     max_seq_length = max_seq_length,

2 frames
/usr/local/lib/python3.11/dist-packages/transformers/models/auto/auto_factory.py in from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs)
    565                 pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs
    566             )
--> 567         raise ValueError(
    568             f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
    569             f"Model type should be one of {', '.join(c.__name__ for c in cls._model_mapping.keys())}."

ValueError: Unrecognized configuration class <class 'transformers.models.phi3.configuration_phi3.Phi3Config'> for this kind of AutoModel: AutoModelForVision2Seq.
Model type should be one of BlipConfig, Blip2Config, ChameleonConfig, GitConfig, Idefics2Config, Idefics3Config, InstructBlipConfig, InstructBlipVideoConfig, Kosmos2Config, LlavaConfig, LlavaNextConfig, LlavaNextVideoConfig, LlavaOnevisionConfig, MllamaConfig, PaliGemmaConfig, Pix2StructConfig, Qwen2VLConfig, VideoLlavaConfig, VipLlavaConfig, VisionEncoderDecoderConfig.


کدم: from unsloth import FastVisionModel   # FastVisionModel for LLMs
import torch
max_seq_length = 2048  # Choose any! We auto support RoPE Scaling internally!
load_in_4bit = True  # Use 4bit quantization to reduce memory usage. Can be False.

# 4bit pre quantized models we support for 4x faster downloading + no OOMs.
fourbit_models = [
    "unsloth/Meta-Llama-3.1-8B-bnb-4bit",  # Llama-3.1 2x faster
    "unsloth/Mistral-Small-Instruct-2409",  # Mistral 22b 2x faster!
    "unsloth/Phi-4",  # Phi-4 2x faster!
    "unsloth/Phi-4-unsloth-bnb-4bit",  # Phi-4 Unsloth Dynamic 4-bit Quant
    "unsloth/gemma-2-9b-bnb-4bit",  # Gemma 2x faster!
    "unsloth/Qwen2.5-7B-Instruct-bnb-4bit"  # Qwen 2.5 2x faster!
    "unsloth/Llama-3.2-1B-bnb-4bit",  # NEW! Llama 3.2 models
    "unsloth/Llama-3.2-1B-Instruct-bnb-4bit",
    "unsloth/Llama-3.2-3B-bnb-4bit",
    "unsloth/Llama-3.2-3B-Instruct-bnb-4bit",
]  # More models at https://docs.unsloth.ai/get-started/all-our-models

model, tokenizer = FastVisionModel .from_pretrained(
    model_name = "arshiaafshani/Arsh-V1",
    max_seq_length = max_seq_length,
    load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

از معماری phi3 استفاده می کنم