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Rumiii
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Model Details
Model Provider
Rumiii
Model Tree
unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit
Input Modalities
Output Modalities
Supported Functionality
python
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfigimport torchbnb_config = BitsAndBytesConfig(load_in_4bit=True,bnb_4bit_quant_type="nf4",bnb_4bit_compute_dtype=torch.float16,)tokenizer = AutoTokenizer.from_pretrained("Rumiii/LlamaMed-3.1-8B-Reasoner")model = AutoModelForCausalLM.from_pretrained("Rumiii/LlamaMed-3.1-8B-Reasoner",device_map={"": 0},quantization_config=bnb_config,)messages = [{"role": "user", "content": "A 45-year-old man presents with polyuria, polydipsia, and weight loss. Fasting blood glucose is 210 mg/dL. What is the most likely diagnosis?\nA. Type 1 Diabetes Mellitus\nB. Type 2 Diabetes Mellitus\nC. Diabetes Insipidus\nD. Cushing's Syndrome"},]inputs = tokenizer.apply_chat_template(messages,add_generation_prompt=True,tokenize=True,return_dict=True,return_tensors="pt",).to(model.device)outputs = model.generate(**inputs, max_new_tokens=1500, temperature=0.6, top_p=0.95)print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
Trained on a single Tesla T4 GPU using Unsloth for memory-efficient QLoRA fine-tuning, with periodic adapter checkpoints saved during training.
This model is a research checkpoint intended for exploring medical reasoning fine-tunes. It is not validated for clinical use and should not be used to inform real medical decisions.