metadata
library_name: transformers
language:
- aa
metrics:
- accuracy
- bleu
- code_eval
base_model:
- meta-llama/Llama-2-7b-hf
---
base_model: meta-llama/Llama-2-7b-hf
library_name: peft
tags:
- llama2
- causal-lm
- instruction-tuning
- customer-support
- lora
- peft
---
# Model Card for `llama2-customer-support-ajay`
This is a fine-tuned version of **LLaMA-2 7B** using **LoRA adapters via PEFT** for customer support and story-style response generation. It has been trained on a small instruction dataset designed to simulate friendly, conversational replies to customer queries, ideal for support chatbots or virtual assistants.
---
## Model Details
### Model Description
This model was fine-tuned on a small instruction dataset with three fields: `instruction`, `input`, and `output`. The model learns to generate human-like, empathetic, and informative responses for common customer interactions such as applying promo codes, understanding shipping timelines, and subscription cancellations.
- **Developed by:** Ajay Kumar Jha
- **Model type:** Causal Language Model (Instruction-tuned)
- **Language(s):** English
- **License:** llama2 license (Meta AI)
- **Fine-tuned from model:** meta-llama/Llama-2-7b-hf
- **Library:** `transformers`, `peft`, `trl`
---
## Model Sources
- **Repository:** [https://huggingface.co/Ajaykumarjha/llama2-customer-support-ajay](https://huggingface.co/Ajaykumarjha/llama2-customer-support-ajay)
- **Demo:** Gradio/Colab Interface Available (see below)
---
## Uses
### Direct Use
- Generate customer support responses for predefined instruction+input pairs
- Can be integrated into chatbots or support ticket systems
### Downstream Use
- Plug into frontend chat interfaces (Gradio, Streamlit)
- Extend to more domains like e-commerce, education, healthcare
### Out-of-Scope Use
- Not suitable for medical, legal, or financial advice
- Not suitable for zero-shot generation outside the tuned task
- Not a replacement for professional customer service staff
---
## Bias, Risks, and Limitations
- The model was trained on a **small, synthetic dataset**, so it may generalize poorly to unexpected queries
- Biases from base model (`llama2-7b`) are inherited
- Not designed for adversarial, unethical, or misleading use
### Recommendations
Ensure model output is monitored and evaluated before integration in production. Human-in-the-loop review is encouraged.
---
## How to Get Started with the Model
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("YOUR_USERNAME/llama2-customer-support-ajay", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("YOUR_USERNAME/llama2-customer-support-ajay")
def generate_response(instruction, input_text):
prompt = f"### Instruction:\n{instruction}\n\n### Input:\n{input_text}\n\n### Response:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
Training Details
Training Data
Custom JSON dataset with instruction-based prompts and conversational, friendly outputs for support cases. Contains fewer than 100 examples.
Training Procedure
- Format: Alpaca-style (Instruction → Input → Output)
- Framework: 🤗
transformers,trl,peft - Fine-tuning Method: LoRA adapter (8-bit, 4-bit)
- Mixed Precision:
fp16
Training Hyperparameters
- Epochs: 3
- Batch size: 1
- LR: 2e-4
- Accumulation steps: 4
- Save strategy: epoch
Evaluation
Testing Data
10% of the training data used for eval split
Metrics
- No formal metrics due to size; manual evaluation on fluency and helpfulness
Results
- Outputs are stylistically aligned with helpful, cheerful assistant replies
- Generalizes decently to new inputs in the same domain
Environmental Impact
- Hardware Type: Google Colab Pro GPU (T4/A100)
- Hours used: ~1 hour
- Cloud Provider: Google
- Compute Region: Asia-South (India)
- Carbon Emitted: Low (due to short training duration)
Technical Specifications
Model Architecture
LLaMA 2 7B - decoder-only transformer with instruction-style prompt formatting
Compute Infrastructure
- Hardware: NVIDIA T4 GPU (Google Colab Pro)
- Software: Python 3.10, Transformers 4.39, PEFT 0.15.2, Accelerate
Citation
APA:
Ajay Kumar Jha. (2025). Fine-Tuned LLaMA2 for Customer Support Chatbots. HuggingFace. https://huggingface.co/YOUR_Ajaykumarjha/llama2-customer-support-ajay
Model Card Authors
Ajay Kumar Jha
Model Card Contact
- Email: ajaykumarjha382003@gmail.com
- Hugging Face: https://huggingface.co/YOUR_USERNAME
Framework versions
transformers: 4.39+peft: 0.15.2trl: 0.7+