Kavyaah

Predict ICD-10 and CPT codes from clinical notes using a fine-tuned LLM. This model is fine-tuned on clinical notes using Phi-3-mini with LoRA and 4-bit quantization. It can generate both ICD/CPT codes and short explanations, helping automate the medical coding process. Training Dataset: Custom dataset of clinical notes, ICD codes, and supporting evidence Usage # from transformers import AutoTokenizer, AutoModelForCausalLM import torch, re # Load tokenizer and model tokenizer = AutoTokenizer.frompretrained("Kavyaah/medical-coding-llm") model = AutoModelForCausalLM.frompretrained("Kavyaah/medical-coding-llm") model.eval() # Function to predict ICD/CPT codes def getcode(statement, maxnewtokens=50): prompt = f"Assign the correct ICD or CPT medical code for this case:\n{statement}\nCode:" inputs = tokenizer(prompt, returntensors="pt") with torch.nograd(): outputs = model.generate(inputs, maxnewtokens=maxnewtokens, dosample=False) result = tokenizer.decode(outputs[0], skipspecialtokens=True) # Extract code using regex if "Code:" in result: result = result.split("Code:")[-1] match = re.search(r"\b[A-Z]\d{1,3}\.?[A-Z0-9]\b", result) return match.group(0).strip() if match else result.strip() # Example statement = "Patient diagnosed with Type 2 diabetes mellitus without complications." print(getcode(statement)) # Output: E11.9 Assisting medical coders and healthcare professionals. Automating initial code suggestions from clinical notes. Trained on a small dataset; may not cover all ICD/CPT codes. Use as an assistive tool, not a replacement for professional judgment. Always review predicted codes before clinical or billing use. MIT License — feel free to use and adapt for non-commercial purposes.

Generate short, high-converting push notifications and ad copies. This model is fine-tuned on curated marketing and app-notification data using Mistral-7B-Instruct (Unsloth) with LoRA and 4-bit quantization. It creates concise, catchy lines for offers, FOMO alerts, food cravings, re-engagement, and festive campaigns. Model Details Property Value Base Model unsloth/mistral-7b-instruct-v0.3 Fine-Tuning LoRA (r = 16, α = 16, dropout = 0.0) Quantization 4-bit (QLoRA NF4) Dataset 3 000 handcrafted marketing prompts & responses Task Causal Language Modeling for short-form copywriting Context Length 2048 tokens Usage # from transformers import AutoTokenizer, AutoModelForCausalLM import torch # Load tokenizer & model tokenizer = AutoTokenizer.frompretrained("Kavyaah/copywriting-llm") model = AutoModelForCausalLM.frompretrained("Kavyaah/copywriting-llm", torchdtype="auto") model.eval() # Function to generate push notification def generatecopy(brand, offer, tone="fun", maxnewtokens=40): prompt = f"""You are an expert marketing copywriter. Write a short, catchy push notification in a {tone} tone. It should promote {brand}'s offer: "{offer}". Keep it under 20 words, engaging, and persuasive.""" inputs = tokenizer(prompt, returntensors="pt") with torch.nograd(): outputs = model.generate( inputs, maxnewtokens=maxnewtokens, temperature=0.9, topp=0.9, dosample=True ) return tokenizer.decode(outputs[0], skipspecialtokens=True) Example print(generatecopy("Zomato", "Flat 60% off on dinner combos this weekend!")) # Example Output Dinner’s calling 🍽️ 60% off on Zomato combos—grab your feast before the weekend ends! Generating push notifications, app banners, and micro-ad copies Automating A/B test copy variants for offers and sales May produce overly playful or repetitive content if prompts are vague Avoid using for sensitive topics or regulated industries Food Craving “Lunch o’clock alert! Your cravings just went live 🍛” FOMO “Blink and it’s gone 👀 Flash sale ends in 2 hours!” Re-engagement “We miss your clicks 😢 Come back for something tasty!” Festive “Play with colors, not your budget! Holi offers just dropped 🎨” Fashion “New drops just landed 💃 Make your wardrobe jealous!” MIT License - open for research and non-commercial use. Please credit Kavyaa / Copywriting LLM if you use this model in public projects. Created by Kavyaa for creative and marketing AI research