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license: apache-2.0
base_model: HuggingFaceTB/SmolLM-360M-Instruct
tags:
- smollm
- customer-support
- fine-tuned
- lora
- instruct
language:
- en
---
# SmolLM-360M-CustomerSupport-Instruct
This is a fine-tuned version of [HuggingFaceTB/SmolLM-360M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM-360M-Instruct) trained on customer support conversations.
## Model Details
- **Base Model**: SmolLM-360M-Instruct (360M parameters)
- **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
- **Dataset**: Bitext Customer Support Dataset
- **Training Samples**: 2,000 customer support conversations
- **Use Case**: Customer service chatbot, support ticket handling
## Training Details
- **Epochs**: 3
- **Learning Rate**: 1e-4
- **Batch Size**: 8
- **Max Sequence Length**: 512
- **Optimizer**: AdamW with cosine learning rate schedule
- **Hardware**: Single GPU (Colab/Kaggle)
## Usage
```python
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](https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-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)
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