---
library_name: transformers
tags: []
---

# Model Card


## Model Description

This is a **preference-tuned** version of **DeepSeek-R1-Distill-Llama-8B**, optimized for **telecom-related queries**. The model has been fine-tuned to provide **concise and factual answers**, ensuring that it **does role-play as a customer service agent** while incorporating user-specific preferences.

- **Developed by:** Mohamed Abdulaziz

- **Model type:** Fine-tune-DeepSeek-R1-Distill-Llama-8B

- **Framework Used:** Unsloth for fine tuning and wandb for performance monitoring

- **Preference Tuning:** The model has been additionally fine-tuned using **Direct Preference Optimization (DPO)** to align with user preferences, enhancing response quality and relevance.

## Benefits of Preference Tuning with DPO

Direct Preference Optimization (DPO) improves model performance by directly optimizing for user-preferred responses without requiring reinforcement learning with human feedback (RLHF).

### **Advantages of DPO in this Model:**

- **Higher Quality Responses:** The model generates more accurate and contextually relevant answers by aligning with preferred outputs.
- **Better User Experience:** By fine-tuning responses based on preferred interactions, the model enhances satisfaction and usability.
- **Efficient Fine-Tuning:** DPO allows preference alignment with reduced computational overhead compared to RLHF, making it more scalable and adaptable.
- **Improved Robustness:** The model avoids inconsistencies by learning from direct user feedback rather than implicit reward functions.

## Uses

This model is designed for **customer support automation in the telecom industry**. It assists in:

- Answering common user queries about **5G, network issues, billing, and services**.
- Providing **concise and factually correct responses**.
- Reducing **workload on human support agents** by handling routine inquiries.
- **Adapting to user-specific preferences** to improve interaction quality.

### **Who can use this model?**

- **Telecom companies**: Automate customer service via chatbots.
- **Developers & researchers**: Fine-tune and adapt for different use cases.
- **Call centers**: Support agents in handling user requests efficiently.

### **Who might be affected?**

- **End-users** interacting with telecom chatbots.
- **Support agents** using AI-assisted tools.
- **Developers & data scientists** fine-tuning and deploying the model.

## How to Get Started with the Model

### **1️⃣ Import necessary libraries**

```python
import torch
from unsloth import FastLanguageModel
from transformers import AutoTokenizer
```

### **2️⃣ Define model path**

```python
model_path = "moo100/DeepSeek-R1-telecom-chatbot-v2"
```

### **3️⃣ Load the model and tokenizer**

```python
model, tokenizer = FastLanguageModel.from_pretrained(
    model_path,
    max_seq_length=1024,  
    dtype=None,
    device_map="auto",
    load_in_4bit=True,
    trust_remote_code=True
)
```

### **4️⃣ Optimize model for fast inference with Unsloth**

```python
model = FastLanguageModel.for_inference(model)
```

### **5️⃣ Move model to GPU if available, otherwise use CPU**

```python
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
```

### **6️⃣ Define system instruction to guide model responses**

```python
system_instruction = """You are an AI assistant. Answer user questions concisely and factually. 
Do NOT add extra details unless necessary."""
```

### **7️⃣ Define user input (Replace with any query)**

```python
user_input = "Hello"
```

### **8️⃣ Construct full prompt with instructions and user query**

```python
full_prompt = f"{system_instruction}\n\nUser: {user_input}\nAssistant:"
```

### **9️⃣ Tokenize input prompt**

```python
inputs = tokenizer(full_prompt, return_tensors="pt").to(device)
```

### **🔦 Generate model response with controlled stopping criteria**

```python
outputs = model.generate(
    input_ids=inputs.input_ids,
    attention_mask=inputs.attention_mask,
    max_new_tokens=50,  
    do_sample=True,  
    temperature=0.5,  
    top_k=50,
    eos_token_id=tokenizer.eos_token_id,  
)
```

### **1️⃣1️ Decode and extract only the newly generated response**

```python
response = tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True)
```

### **1️⃣2️ Print the AI-generated response**

```python
print(response.split("User: ")[0])
```



## Training Details

### Training Data

A **preference dataset** was created using **Phi4**, where the **chosen column** was generated via evaluation prompts. This dataset ensures the model aligns closely with user preferences by fine-tuning responses accordingly.

### Training Procedure

- **Training Loss:** Decreased steadily from **0.6997** at step 10 to **0.0966** at step 360, indicating stable model convergence.
- **Rewards / Chosen:** Improved consistently, reaching **1.4318**, showcasing enhanced preference alignment.
- **Rewards / Rejected:** Decreased over time, confirming the model's ability to distinguish between optimal and suboptimal responses.
- **Logits / Chosen & Rejected:** Demonstrated increasing separation, ensuring stronger differentiation between desirable and undesirable outputs.
- **Gradient Norm & Learning Rate Schedule:** Maintained stability with linear decay, preventing sudden fluctuations and ensuring smooth convergence.
- **Preference Alignment:** Fine-tuned using **DPO**, incorporating user feedback to optimize responses effectively.



# Evaluation

## Methodology

The chatbot was evaluated using Meta-Llama-3.3-70B-Instruct, assessing relevance, correctness, and fluency of its responses.

## Results

Meta-Llama-3.3-70B-Instruct Evaluation:

- **Relevance: 10**
  The chatbot's response is highly relevant to the user's greeting, as it acknowledges the greeting and opens the conversation for further discussion.

- **Correctness: 10**
  The chatbot's response is grammatically correct and uses proper language, making it a correct and appropriate response to the user's greeting.

- **Fluency: 10**
  The chatbot's response is fluent and natural-sounding, with a friendly and approachable tone that invites the user to engage in conversation.

Overall, the chatbot's response is a great example of a well-crafted greeting response that effectively sets the tone for a helpful and engaging conversation.