Duplicate from ldhldh/polyglot_ko_1.3B_PEFT_demo

.gitattributes

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README.md

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+---
+title: 🤗 KoRWKV-1.5B 🔥Streaming🔥
+emoji: 💻
+colorFrom: yellow
+colorTo: purple
+sdk: gradio
+sdk_version: 3.29.0
+app_file: app.py
+pinned: false
+license: mit
+duplicated_from: ldhldh/polyglot_ko_1.3B_PEFT_demo
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+from threading import Thread
+
+import torch
+import gradio as gr
+from transformers import pipeline,AutoTokenizer, AutoModelForCausalLM, BertTokenizer, BertForSequenceClassification, StoppingCriteria, StoppingCriteriaList
+from peft import PeftModel, PeftConfig
+import re
+from kobert_transformers import get_tokenizer
+
+torch_device = "cuda" if torch.cuda.is_available() else "cpu"
+print("Running on device:", torch_device)
+print("CPU threads:", torch.get_num_threads())
+
+peft_model_id = "ldhldh/polyglot-ko-1.3b_lora_big_8kstep" 
+#18k > 상대의 말까지 하는 이슈가 있음 
+#8k > 약간 아쉬운가? 
+config = PeftConfig.from_pretrained(peft_model_id)
+
+base_model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path)
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+
+#base_model = AutoModelForCausalLM.from_pretrained("EleutherAI/polyglot-ko-3.8b")
+#tokenizer = AutoTokenizer.from_pretrained("EleutherAI/polyglot-ko-3.8b")
+base_model.eval()
+#base_model.config.use_cache = True
+
+
+model = PeftModel.from_pretrained(base_model, peft_model_id, device_map="auto")
+model.eval()
+#model.config.use_cache = True
+
+
+mbti_bert_model_name = "Lanvizu/fine-tuned-klue-bert-base_model_11"
+mbti_bert_model = BertForSequenceClassification.from_pretrained(mbti_bert_model_name)
+mbti_bert_model.eval()
+mbti_bert_tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+
+bert_model_name = "ldhldh/bert_YN_small"
+bert_model = BertForSequenceClassification.from_pretrained(bert_model_name)
+bert_model.eval()
+bert_tokenizer = get_tokenizer()
+
+
+def mbti_classify(x):
+    classifier = pipeline("text-classification", model=mbti_bert_model, tokenizer=mbti_bert_tokenizer, return_all_scores=True)
+    result = classifier([x])
+    return result[0]
+
+
+def classify(x):
+    input_list =  bert_tokenizer.batch_encode_plus([x], truncation=True, padding=True, return_tensors='pt')
+    input_ids = input_list['input_ids'].to(bert_model.device)
+    attention_masks = input_list['attention_mask'].to(bert_model.device)
+    outputs =  bert_model(input_ids, attention_mask=attention_masks, return_dict=True)
+    return outputs.logits.argmax(dim=1).cpu().tolist()[0]
+
+def gen(x, top_p, top_k, temperature, max_new_tokens, repetition_penalty):
+    gened = model.generate(
+        **tokenizer(
+            f"{x}",
+            return_tensors='pt',
+            return_token_type_ids=False
+        ),
+        #bad_words_ids = bad_words_ids ,
+        max_new_tokens=max_new_tokens,
+        min_new_tokens = 5,
+        exponential_decay_length_penalty = (max_new_tokens/2, 1.1),
+        top_p=top_p,
+        top_k=top_k,
+        temperature = temperature,
+        early_stopping=True,
+        do_sample=True,
+        eos_token_id=2,
+        pad_token_id=2,
+        #stopping_criteria = stopping_criteria,
+        repetition_penalty=repetition_penalty,
+        no_repeat_ngram_size = 2
+    )
+
+    model_output = tokenizer.decode(gened[0])
+    return model_output
+
+def reset_textbox():
+    return gr.update(value='')
+
+
+with gr.Blocks() as demo:
+    duplicate_link = "https://huggingface.co/spaces/beomi/KoRWKV-1.5B?duplicate=true"
+    gr.Markdown(
+       "duplicated from beomi/KoRWKV-1.5B, baseModel:EleutherAI/polyglot-ko-1.3b"
+    )
+    
+    with gr.Row():
+        with gr.Column(scale=4):
+            user_text = gr.Textbox(
+                placeholder='\\nfriend: 우리 여행 갈래? \\nyou:',
+                label="User input"
+            )
+            model_output = gr.Textbox(label="Model output", lines=10, interactive=False)
+            button_submit = gr.Button(value="Submit")
+            button_bert = gr.Button(value="bert_Sumit")
+            button_mbti_bert = gr.Button(value="mbti_bert_Sumit")
+        with gr.Column(scale=1):
+            max_new_tokens = gr.Slider(
+                minimum=1, maximum=200, value=20, step=1, interactive=True, label="Max New Tokens",
+            )
+            top_p = gr.Slider(
+                minimum=0.05, maximum=1.0, value=0.8, step=0.05, interactive=True, label="Top-p (nucleus sampling)",
+            )
+            top_k = gr.Slider(
+                minimum=5, maximum=100, value=30, step=5, interactive=True, label="Top-k (nucleus sampling)",
+            )
+            temperature = gr.Slider(
+                minimum=0.1, maximum=2.0, value=0.5, step=0.1, interactive=True, label="Temperature",
+            )
+            repetition_penalty = gr.Slider(
+                minimum=1.0, maximum=3.0, value=1.2, step=0.1, interactive=True, label="repetition_penalty",
+            )
+    
+    button_submit.click(gen, [user_text, top_p, top_k, temperature, max_new_tokens, repetition_penalty], model_output)
+    button_bert.click(classify, [user_text], model_output)
+    button_mbti_bert.click(mbti_classify, [user_text], model_output)
+    demo.queue(max_size=32).launch(enable_queue=True)

requirements.txt

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+git+https://github.com/huggingface/transformers
+git+https://github.com/huggingface/peft
+torch
+accelerate
+xformers
+kobert-transformers
+gradio