more updates

README.md

@@ -57,7 +57,7 @@ This app is developed using React + Fastapi. You can run this app locally with t
 
 4. **(Optional) Install GPU-related packages** 
 
-   If you are running on a GPU-enabled device, you can install additional packages (e.g., for faster inference and support for more models):
+   If you are running on a GPU-enabled device, you can install additional packages (support for more models):
    
    ```commandline
    python install_gpu_packages.py

backend/hf_model_utils.py

@@ -3,7 +3,7 @@ import torch.nn as nn
 import json
 import hashlib
 import gc
-from transformers import AutoConfig, AutoModel, AutoModelForCausalLM, AutoModelForMaskedLM
+from transformers import AutoConfig, AutoModel, AutoModelForCausalLM, AutoModelForMaskedLM, AutoModelForSequenceClassification, AutoModelForTokenClassification, AutoModelForQuestionAnswering, AutoModelForSeq2SeqLM, AutoModelForImageClassification
 from accelerate import init_empty_weights
 
 
@@ -80,10 +80,30 @@ def get_model_structure(model_name: str, model_type: str | None):
         config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
         with init_empty_weights():
             model = AutoModelForCausalLM.from_config(config, trust_remote_code=True)
-    if model_type == "masked":
-        config = AutoConfig.from_pretrained("model_name")
+    elif model_type == "masked":
+        config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
         with init_empty_weights():
             model = AutoModelForMaskedLM.from_config(config)
+    elif model_type == "sequence":
+        config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
+        with init_empty_weights():
+            model = AutoModelForSequenceClassification.from_config(config)
+    elif model_type == "token":
+        config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
+        with init_empty_weights():
+            model = AutoModelForTokenClassification.from_config(config)
+    elif model_type == "qa":
+        config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
+        with init_empty_weights():
+            model = AutoModelForQuestionAnswering.from_config(config)
+    elif model_type == "s2s":
+        config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
+        with init_empty_weights():
+            model = AutoModelForSeq2SeqLM.from_config(config)
+    elif model_type == "vision":
+        config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
+        with init_empty_weights():
+            model = AutoModelForImageClassification.from_config(config)
     else:
         config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
         with torch.device("meta"):

frontend/src/components/ModelInputBar.jsx

@@ -5,7 +5,7 @@ export default function ModelInputBar({ loading, fetchModelStructure }) {
     const options = [
         { label: "Not Sure", value: "none", default: "deepseek-ai/DeepSeek-V3.1" },
         { label: "Causal Language Models (e.g. GPT, LLaMA, Phi, Mistral)", value: "causal", default: "gpt2"},
-        { label: "Masked Language Models (BERT, RoBERTa, DistilBERT)", value: "masked", default: "distilbert-base-uncased" },
+        { label: "Masked Language Models (BERT, RoBERTa, DistilBERT)", value: "masked", default: "google-bert/bert-base-uncased" },
         { label: "Sequence Classification (text classification, sentiment analysis)", value: "sequence", default: "distilbert-base-uncased" },
         { label: "Token Classification (NER, POS tagging)", value: "token",  default: "dbmdz/bert-large-cased-finetuned-conll03-english" },
         { label: "Question Answering Models (e.g. BERT QA, RoBERTa QA)", value: "qa", default: "distilbert-base-uncased-distilled-squad" },