beratdgan/Qwen3-14B-Interview-Coach

AI Technical Interview Coach (Fine-Tuned Qwen-14B)

Model Overview

Qwen3-14B-Interview-Coach is a specialized Large Language Model fine-tuned to act as a Senior Technical Interviewer and Evaluator.

It is inspired by interview styles and evaluation standards commonly observed in large, high-performing technology organizations (e.g., FAANG-level engineering interviews), without any affiliation or endorsement.

Primary Use Case: This model serves as the backend intelligence for mock interview platforms. It takes a specific interview question as input and outputs the criteria required for a perfect answer. This "Answer Key" is then used to grade the candidate's actual performance.

Key Features

• Evaluator Logic: Trained to define what a "Good Answer" looks like (Rubric Generation) rather than just acting as a Chatbot.
• Company Personas: Can adopt specific engineering cultures. It knows that Amazon values "Leadership Principles," Netflix values "High Performance," and Google values "Engineering Excellence."
• Structured Output: Generates answer keys covering Technical Concepts, Time/Space Complexity (Big O), and Soft Skills (STAR Method).
• Efficient Deployment: Quantized to GGUF (q4_k_m) format, allowing it to run efficiently on consumer hardware (Apple Silicon M-Series, NVIDIA T4, L4).

Training Data & Technical Statistics

The model was fine-tuned using Unsloth on a Tesla A100 GPU. The training process utilized LoRA (Low-Rank Adaptation) to efficiently update the model weights.

Data Curation Process: The dataset was curated from a massive corpus of over 50,000 raw interview questions sourced from various open technical platforms. Through a rigorous data cleaning, deduplication, and synthetic enhancement pipeline (using AI to generate high-quality Rubrics), this raw data was distilled down to 10,708 high-quality instruction-response pairs. This ensured that the model was trained only on clear, solvable, and relevant questions.

Metric	Value
Raw Data Source	50,000+ Questions
Final Training Set	10,708 Filtered Pairs
Total Parameters	~14B
Trainable Parameters	256,901,120 (1.71%)
Final Training Loss	0.5128 (High Convergence)
Optimization	Unsloth (2x Faster Training, 60% Less VRAM)

The dataset covers three main domains:

• Behavioral: Application of the STAR method, conflict resolution, and leadership scenarios.
• System Design: Scalability, CAP theorem, database selection, load balancing.
• Technical Knowledge: Language specifics (Java/Python), algorithmic optimization.

Inference & Usage Examples

The model expects an Instruction (Persona) and an Input (Question). It generates the Output (Evaluation Criteria/Rubric).

Example 1: Behavioral (Leadership-Oriented Tech Company Persona)

Instruction: You are a Senior Technical Interviewer at large, leadership-driven technology company.. Define the 'Gold Standard' answer for a behavioral question regarding conflict.

Input: "Tell me about a time you disagreed with a manager."

Model Output (The Rubric): The ideal answer must demonstrate the STAR method and align with the "Have Backbone; Disagree and Commit" Leadership Principle.

• Situation: The candidate should describe a specific disagreement based on data or technical merit, not personal preference.
• Action: They must show they disagreed respectfully, presented evidence/data to support their view, and engaged in a constructive debate.
• Result: A positive outcome where the best solution was adopted, or if the decision went against them, they fully committed to the team's direction without resentment.

Example 2: System Design (High-Scale Streaming Platform Persona)

Instruction: You are a Senior Technical Interviewer at a global, high-scale streaming technology company. Define the 'Gold Standard' answer for a System Design question.

Input: "How would you design a video recommendation engine?"

Model Output (The Rubric): The candidate should approach this as a distributed system problem focusing on high availability and low latency.

• Architecture: Microservices architecture using Real-time (Apache Flink) or Batch processing (Spark).
• Data Storage: Handling high-cardinality data with Cassandra or DynamoDB.
• Trade-offs: Discussing Eventual Consistency vs. Strong Consistency (AP over CP in CAP theorem).
• Netflix Context: Must mention fault tolerance (Chaos Engineering) and user engagement metrics.

How to Run (Local Deployment)

To utilize the model's specific "Evaluator" capabilities, you must use the correct prompt format (ChatML).

Option 1: Using Ollama (Recommended)

1. Download the .gguf file to your local machine.
2. Create a file named Modelfile in the same directory with the following content:

FROM ./qwen3-14b.Q4_K_M.gguf
# System prompt sets the Interviewer/Evaluator Persona
SYSTEM "You are a Senior Technical Interviewer. Your task is to provide the 'Gold Standard' answer key and evaluation criteria for the given interview question. Do not answer as a candidate; answer as the grader."
# Low temperature for consistent, factual rubrics
PARAMETER temperature 0.2
PARAMETER num_ctx 4096

3. Open your terminal and run the following commands to build and test the model:

# Build the model
ollama create interview-coach -f Modelfile
# Run the model
ollama run interview-coach

Option 2: Python (llama-cpp-python)

This method is ideal for integrating the model into your Backend (FastAPI/Django).

from llama_cpp import Llama
# 1. Load the Model
llm = Llama(
    model_path="./qwen3-14b.Q4_K_M.gguf",
    n_ctx=4096,           # Context window size
    n_gpu_layers=-1,      # Set to -1 to offload all layers to GPU (Mac/NVIDIA)
    verbose=False
)
# 2. Define Dynamic Variables
# In your actual app, these values will come from your Database or RAG system.
company_name = "Netflix"      # Example: Amazon, Google, etc.
difficulty_level = "Hard"     # Example: Easy, Medium, Hard
interview_question = "How would you design a rate limiter?"
# 3. Construct the Prompt (ChatML Format)
# IMPORTANT: The 'user' role here represents your Backend asking the model
# to generate the Rubric (Answer Key). It is NOT the actual candidate.
prompt = f"""<|im_start|>system
You are a Senior Technical Interviewer at {company_name}. Your task is to define the 'Gold Standard' answer for a {difficulty_level}-level question.<|im_end|>
<|im_start|>user
{interview_question}<|im_end|>
<|im_start|>assistant
"""
# 4. Generate the Gold Standard Rubric
output = llm(
    prompt,
    max_tokens=512,       # Limit the length of the answer key
    stop=["<|im_end|>"],  # Stop generating when the model finishes
    temperature=0.2       # Keep it analytical and precise
)
# 5. Print the Result
print(output['choices'][0]['text'])

Tip: RAG Integration

This model works excellently as a Generator in a RAG pipeline. You can use ChromaDB to retrieve company-specific values (e.g., specific engineering principles) and feed them into the context window of this model to get highly customized interview questions

Safety & Limitations

This model is designed strictly for interview evaluation and rubric generation. It is not intended for generating real hiring decisions or replacing human interviewers.

License

This model is a fine-tuned version of Qwen/Qwen3-14B, which is licensed under Apache 2.0.

However, this specific fine-tuned checkpoint (Qwen3-14B-Interview-Coach) is released under the CC BY-NC 4.0 (Creative Commons Attribution-NonCommercial 4.0) license.

What does this mean?

• ✅ You can: Use this model for research, personal projects, and educational purposes.
• ❌ You cannot: Use this model for commercial applications, paid services, or corporate interview platforms without explicit permission.

For the full text of the license, see CC BY-NC 4.0.