metadata

license: mit
language:
  - en
metrics:
  - bleurt
  - bleu
  - chrf
datasets:
  - Raiff1982/coredata
  - Raiff1982/pineco
base_model:
  - tiiuae/falcon-40b
  - mistralai/Mistral-7B-v0.3
tags:
  - medical
  - code
  - text-generation
  - transformers
pipeline_tag: text-generation
library_name: transformers

Codette - Falcon & Mistral Merged Model

🧠 Overview

Codette is an advanced AI assistant designed to support users across cognitive, creative, and analytical tasks.
This model merges Falcon-40B and Mistral-7B to deliver high performance in text generation, medical diagnostics, and code reasoning.

⚡ Features

✅ Merges Falcon-40B & Mistral-7B for enhanced capabilities
✅ Supports multi-modal text generation, medical analysis, and code synthesis
✅ Fine-tuned on domain-specific datasets (Raiff1982/coredata, Raiff1982/pineco)
✅ Optimized for research, enterprise AI, and advanced reasoning

📂 Model Details

Base Models: Falcon-40B, Mistral-7B-v0.3
Architecture: Transformer-based language model
Use Cases: Text generation, code assistance, research, medical insights
Training Datasets:
- Raiff1982/coredata: medical and reasoning-focused samples
- Raiff1982/pineco: mixed domain creative + technical prompts

📖 Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Raiff1982/Codette"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)

prompt = "How can AI improve medical diagnostics?"
inputs = tokenizer(prompt, return_tensors="pt") 
output = model.generate(**inputs, max_length=200)
print(tokenizer.decode(output[0], skip_special_tokens=True))