| # import gradio as gr | |
| # from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer | |
| # from peft import PeftModel # Pastikan PEFT terinstal | |
| # # Tentukan model dasar dan LoRA weights | |
| # base_model_name = "unsloth/Llama-3.2-3B-Instruct" # Model dasar sesuai yang ada di gambar | |
| # lora_model_name = "nicolaakmal/llama32-lora-finetuned-v3" | |
| # # Load tokenizer dan base model | |
| # tokenizer = AutoTokenizer.from_pretrained(base_model_name) | |
| # base_model = AutoModelForCausalLM.from_pretrained(base_model_name) | |
| # # Load LoRA model sebagai PeftModel | |
| # model = PeftModel.from_pretrained(base_model, lora_model_name) | |
| # # Fungsi untuk menghasilkan respons | |
| # def generate_response(prompt): | |
| # inputs = tokenizer(prompt, return_tensors="pt").to("cuda") | |
| # text_streamer = TextStreamer(tokenizer, skip_prompt=True) | |
| # outputs = model.generate( | |
| # input_ids=inputs["input_ids"], | |
| # streamer=text_streamer, | |
| # max_new_tokens=128, | |
| # use_cache=True, | |
| # temperature=1.5, | |
| # top_p=0.1 | |
| # ) | |
| # response = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| # return response | |
| # # Interface Gradio | |
| # iface = gr.Interface( | |
| # fn=generate_response, | |
| # inputs="text", | |
| # outputs="text", | |
| # title="LLAMA-32 Fine-Tuned Model Demo", | |
| # description="Enter a prompt and the model will generate a response." | |
| # ) | |
| # iface.launch() | |
| # import gradio as gr | |
| # from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer | |
| # from peft import PeftModel # Pastikan PEFT terinstal | |
| # # Tentukan model dasar dan LoRA weights | |
| # base_model_name = "unsloth/Llama-3.2-3B-Instruct" # Base model dari unsloth | |
| # lora_model_name = "nicolaakmal/llama32-lora-finetuned-v3" | |
| # # Load tokenizer dan base model | |
| # tokenizer = AutoTokenizer.from_pretrained(base_model_name) | |
| # base_model = AutoModelForCausalLM.from_pretrained(base_model_name) | |
| # # Load LoRA model sebagai PeftModel | |
| # model = PeftModel.from_pretrained(base_model, lora_model_name) | |
| # # Set pad_token_id jika belum diatur | |
| # if model.config.pad_token_id is None: | |
| # model.config.pad_token_id = model.config.eos_token_id | |
| # # Fungsi untuk menghasilkan respons | |
| # def generate_response(prompt): | |
| # # Tokenisasi input dengan `attention_mask` | |
| # inputs = tokenizer(prompt, return_tensors="pt", padding=True) | |
| # input_ids = inputs["input_ids"].to("cpu") | |
| # attention_mask = inputs["attention_mask"].to("cpu") | |
| # # Streaming teks keluaran | |
| # text_streamer = TextStreamer(tokenizer, skip_prompt=True) | |
| # # Generasi teks dengan menggunakan `attention_mask` dan `pad_token_id` | |
| # outputs = model.generate( | |
| # input_ids=input_ids, | |
| # attention_mask=attention_mask, | |
| # streamer=text_streamer, | |
| # max_new_tokens=128, | |
| # use_cache=True, | |
| # temperature=1.5, | |
| # top_p=0.1 | |
| # ) | |
| # response = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| # return response | |
| # # Interface Gradio | |
| # iface = gr.Interface( | |
| # fn=generate_response, | |
| # inputs="text", | |
| # outputs="text", | |
| # title="LLAMA-32 Fine-Tuned Model Demo", | |
| # description="Enter a prompt and the model will generate a response." | |
| # ) | |
| # iface.launch() | |
| # import gradio as gr | |
| # from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer | |
| # from peft import PeftModel # Pastikan PEFT terinstal | |
| # # Tentukan model dasar dan LoRA weights | |
| # base_model_name = "unsloth/Llama-3.2-3B-Instruct" # Base model dari unsloth | |
| # lora_model_name = "nicolaakmal/llama32-lora-finetuned-v3" | |
| # # Load tokenizer dan base model | |
| # tokenizer = AutoTokenizer.from_pretrained(base_model_name) | |
| # base_model = AutoModelForCausalLM.from_pretrained(base_model_name) | |
| # # Load LoRA model sebagai PeftModel | |
| # model = PeftModel.from_pretrained(base_model, lora_model_name) | |
| # # Set pad_token_id jika belum diatur | |
| # if model.config.pad_token_id is None: | |
| # model.config.pad_token_id = model.config.eos_token_id | |
| # # Fungsi untuk menghasilkan respons | |
| # def generate_response(prompt): | |
| # # Tokenisasi input secara manual tanpa apply_chat_template | |
| # inputs = tokenizer(prompt, return_tensors="pt", padding=True) | |
| # input_ids = inputs["input_ids"].to("cpu") # Menggunakan CPU | |
| # attention_mask = inputs["attention_mask"].to("cpu") # Attention mask untuk input | |
| # # Streaming teks keluaran | |
| # text_streamer = TextStreamer(tokenizer, skip_prompt=True) | |
| # # Konfigurasi generate sesuai referensi | |
| # outputs = model.generate( | |
| # input_ids=input_ids, | |
| # attention_mask=attention_mask, | |
| # streamer=text_streamer, | |
| # max_new_tokens=128, | |
| # use_cache=True, | |
| # temperature=0.01, # Mengunci jawaban pada respons yang konsisten | |
| # top_p=1.0, # Tidak membatasi token dalam probabilitas kumulatif | |
| # top_k=1 # Memilih hanya token dengan probabilitas tertinggi | |
| # ) | |
| # response = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| # return response | |
| # # Interface Gradio | |
| # iface = gr.Interface( | |
| # fn=generate_response, | |
| # inputs="text", | |
| # outputs="text", | |
| # title="LLAMA-32 Fine-Tuned Model Demo", | |
| # description="Enter a prompt and the model will generate a response." | |
| # ) | |
| # iface.launch() | |
| from transformers import pipeline | |
| import gradio as gr | |
| def generate_response(inputs): | |
| messages = [ | |
| {"role": "user", "content": inputs}, | |
| ] | |
| pipe = pipeline("text-generation", model="nicolaakmal/llama32-lora-finetuned-v3-fp16") | |
| return pipe(messages) | |
| # Antarmuka Gradio | |
| iface = gr.Interface( | |
| fn=generate_response, | |
| inputs="text", | |
| outputs="text", | |
| title="Chatbot LLaMA Finetuned" | |
| ) | |
| # Jalankan aplikasi Gradio | |
| iface.launch() | |