This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2 on a dataset of customer service client-agent conversations (Lakshan2003).

Optimized for dialogue, polite response, and multi-turn coherence.
Trained using LoRA (rank=16, alpha=32, dropout=0.05) with H2O LLM Studio.
Dataset size: ~5k samples (balanced customer ↔ agent turns)

Example Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "n0xgg04/mistral-customer-service-full"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="bfloat16")

prompt = "Hi! Can you help me change my shipping address?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=150)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))