from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "sweatSmile/SmolLM-360M-CustomerSupport-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Format your prompt
prompt = "<|im_start|>user\nHow do I reset my password?<|im_end|>\n<|im_start|>assistant\n"

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=150, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)

Example Outputs

Input: "My order hasn't arrived yet, what should I do?"

Output: "I apologize for the delay with your order. Let me help you track it. Could you please provide your order number? Once I have that, I can check the current status and estimated delivery date."

Limitations

Trained on English customer support conversations only
Best for customer service domains (banking, e-commerce, general support)
May not perform well on highly technical or domain-specific queries outside training data
Small model size (360M) - not as capable as larger models but much faster

Intended Use

Customer support chatbots
Automated ticket response systems
FAQ assistance
Initial customer inquiry handling

Training Data

The model was fine-tuned on the Bitext Customer Support Dataset, which contains diverse customer service scenarios.

Citation

bibtex

@misc{smollm-customer-support-2025,
  author = {sweatSmile},
  title = {SmolLM-360M Fine-tuned for Customer Support},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/sweatSmile/SmolLM-360M-CustomerSupport-Instruct}
}

License

Apache 2.0 (same as base model)