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KlibiYoussef
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Model Details
Model Provider
KlibiYoussef
Model Tree
Qwen/Qwen3-4B-Base
Input Modalities
Output Modalities
Supported Functionality
The model has been optimized using a custom, strictly filtered bilingual dataset to handle three main responsibilities:
The adapter follows the ChatML prompt structure. Below is an optimized script demonstrating how to programmatically initialize the base model along with this adapter using the platform's official system prompt setup:
python
import torchfrom transformers import AutoTokenizer, AutoModelForCausalLMfrom peft import PeftModel # Repository ConfigurationsBASE_MODEL_ID = "Qwen/Qwen3-4B-Base"ADAPTER_PATH = "KlibiYoussef/smart-plate-nutrition-adapter" # 1. Load Tokenizer and Base Model with the LoRA Adaptertokenizer = AutoTokenizer.from_pretrained(ADAPTER_PATH)base_model = AutoModelForCausalLM.from_pretrained( BASE_MODEL_ID, torch_dtype=torch.float16, device_map="auto")model = PeftModel.from_pretrained(base_model, ADAPTER_PATH)model.eval() # 2. Construct the Prompt in ChatML format using the official platform promptprompt = ( "<|im_start|>system\n" "You are the 'Smart Plate Nutrition' AI assistant. Your role is to provide ultra-precise, " "scientific, and personalized nutritional information. You must always be polite, helpful, " "start with a warm greeting, and end with an encouraging health-focused closing statement." "<|im_end|>\n" "<|im_start|>user\n" "How can I lose fat without losing muscle?\n" "<|im_end|>\n" "<|im_start|>assistant\n") # 3. Generate Responseinputs = tokenizer(prompt, return_tensors="pt").to(model.device)with torch.no_grad(): outputs = model.generate( **inputs, max_new_tokens=256, temperature=0.3, do_sample=True, repetition_penalty=1.1 ) # Decode the Outputgenerated_tokens = outputs[0][inputs.input_ids.shape[1]:]response = tokenizer.decode(generated_tokens, skip_special_tokens=True)print(response)import torchfrom transformers import AutoTokenizer, AutoModelForCausalLMfrom peft import PeftModel # Repository ConfigurationsBASE_MODEL_ID = "Qwen/Qwen3-4B-Base"ADAPTER_PATH = "KlibiYoussef/smart-plate-nutrition-adapter" # 1. Load Tokenizer and Base Model with the LoRA Adaptertokenizer = AutoTokenizer.from_pretrained(ADAPTER_PATH)base_model = AutoModelForCausalLM.from_pretrained( BASE_MODEL_ID, torch_dtype=torch.float16, device_map="auto")model = PeftModel.from_pretrained(base_model, ADAPTER_PATH)model.eval() # 2. Construct the Prompt in ChatML format using the official platform promptprompt = ( "<|im_start|>system\n" "You are the 'Smart Plate Nutrition' AI assistant. Your role is to provide ultra-precise, " "scientific, and personalized nutritional information. You must always be polite, helpful, " "start with a warm greeting, and end with an encouraging health-focused closing statement." "<|im_end|>\n" "<|im_start|>user\n" "How can I lose fat without losing muscle?\n" "<|im_end|>\n" "<|im_start|>assistant\n") # 3. Generate Responseinputs = tokenizer(prompt, return_tensors="pt").to(model.device)with torch.no_grad(): outputs = model.generate( **inputs, max_new_tokens=256, temperature=0.3, do_sample=True, repetition_penalty=1.1 ) # Decode the Outputgenerated_tokens = outputs[0][inputs.input_ids.shape[1]:]response = tokenizer.decode(generated_tokens, skip_special_tokens=True)print(response)