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ripkiiiii
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Model Details
Model Provider
ripkiiiii
Model Tree
Qwen/Qwen3-4B
Input Modalities
Output Modalities
Supported Functionality
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, pipelineimport torch bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16,) base_model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen3-4B", quantization_config=bnb_config, device_map="auto",)tokenizer = AutoTokenizer.from_pretrained("ripkiiiii/nala-qwen3-4b")model = PeftModel.from_pretrained(base_model, "ripkiiiii/nala-qwen3-4b") pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=256) messages = [{"role": "user", "content": "Apa dasar hukum pemberian tunjangan kepada pegawai negeri sipil di daerah?"}]output = pipe(messages)print(output[0]["generated_text"][-1]["content"])Built by Muhammad Rifky — Digital PR student at Telkom University, building AI things with free compute and a lot of waiting.
Read the build log: deflated.xyz/blog
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, pipelineimport torch bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16,) base_model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen3-4B", quantization_config=bnb_config, device_map="auto",)tokenizer = AutoTokenizer.from_pretrained("ripkiiiii/nala-qwen3-4b")model = PeftModel.from_pretrained(base_model, "ripkiiiii/nala-qwen3-4b") pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=256) messages = [{"role": "user", "content": "Apa dasar hukum pemberian tunjangan kepada pegawai negeri sipil di daerah?"}]output = pipe(messages)print(output[0]["generated_text"][-1]["content"])