🎙️ Alex-Tech-Recruiter-v1 (Llama 3.1 8B)

A fine-tuned Llama 3.1 8B Instruct model that simulates a professional technical recruiter named "Alex" for realistic multi-turn interview roleplay.

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📋 Model Overview

Key	Value
License	Llama 3.1 Community License
Base Model	unsloth/llama-3-8b-Instruct-bnb-4bit
Finetuning Method	QLoRA (Quantized Low-Rank Adaptation) via Unsloth
Language	English
Pipeline Tag	text-generation

Tags

llama-3.1 · recruiter · hr-tech · interview-simulation · roleplay · qlora · finetuned

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📖 Model Description

Alex-Tech-Recruiter-v3 is a fine-tuned version of Llama 3.1 8B Instruct, designed to simulate a highly realistic, professional technical recruiter named "Alex." It specializes in conducting multi-turn voice-style interviews grounded in specific candidate CVs and Job Descriptions (JDs).

Model Sources

Resource	Link
Repository	[https://huggingface.co/oussema2021/fintuned_v3_AiRecruter]
Dataset	[pending owner consent... ]
Base Model	unsloth/llama-3-8b-Instruct-bnb-4bit

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🚀 Uses

Direct Use

This model is intended for:

• Interview Simulation — Acting as a practice partner for candidates preparing for technical interviews. Alex maintains a natural conversational tone, asks probing follow-up questions, and keeps the dialogue flowing realistically across multiple turns.

• Recruiter Training — Demonstrating ideal probing techniques and active listening for junior recruiters. The model showcases how to build rapport, dig deeper into candidate experience, and structure a multi-stage technical conversation.

• HR Tech Prototyping — Serving as the core conversational engine for automated screening tools, chatbot-based interview platforms, and recruitment pipeline applications.

Out-of-Scope Use

⚠️ This model should not be used for making hiring decisions autonomously. It is a simulation tool and may hallucinate details not present in the provided context. Always pair it with human oversight in any real-world hiring workflow.

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⚠️ Bias, Risks, and Limitations

Limitation	Description
Hallucination	The model may occasionally hallucinate details if the CV/JD context is missing or ambiguous. Always provide structured, up-to-date context for best results.
Length Bias	While trained on full transcripts, extremely long conversations (>20 turns) may see a degradation in context retention. Consider summarizing earlier turns for extended sessions.
Persona Rigidity	The model is heavily fine-tuned on the "Alex" persona. Switching to a different persona via system prompt may require additional few-shot examples to override the training.

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🛠️ How to Get Started

Installation

pip install unsloth transformers accelerate bitsandbytes

Python Code

from unsloth import FastLanguageModel
import torch

# Load the model and tokenizer
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "your-username/alex-tech-recruiter-v1",  # Replace with your HF ID
    max_seq_length = 4096,
    dtype = None,
    load_in_4bit = True,
)

# Enable faster inference
FastLanguageModel.for_inference(model)

# Define the input data
candidate_name = "John Doe"
job_role = "Senior Data Engineer"
job_description = """
We are looking for a Senior Data Engineer to build scalable pipelines...
"""
candidate_cv = """
John Doe
Senior Data Engineer at Google (2020-Present)
Skills: Python, Spark, Kubernetes...
Experience: Led migration of legacy data warehouse to BigQuery...
"""

# Format the System Prompt
system_prompt = f"""You are Alex, a warm and professional senior technical recruiter \
conducting a live voice interview with {candidate_name} for the role of {job_role}.

This is a natural spoken conversation, not a written exchange. Speak as you would in a real interview.

## Conversation Flow
**Opening (first turn only):**
- Greet the candidate warmly.
- Set a friendly tone and ask an opening question about their background or a highlight from their CV.

**During the interview:**
- Always acknowledge what the candidate just said before asking your next question.
- Ask ONE focused follow-up question that digs deeper into their experience.

**Closing:**
- Wrap up the interview naturally, thank the candidate, and outline next steps.
"""

# Build the conversation messages
messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": candidate_cv},
]

# Generate the first recruiter response
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(
    input_ids=inputs,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.9,
    do_sample=True,
)
response = tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True)
print(response)

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🏗️ Training Details

• Finetuning Method: QLoRA (Quantized Low-Rank Adaptation)
• Framework: Unsloth
• Precision: 4-bit quantization (BitsAndBytes)
• Max Sequence Length: 4096 tokens

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📄 License

This model is released under the Llama 3.1 Community License. Please review the full license terms before using this model in production or commercial applications.

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Developed with ❤️ using [] and Hugging Face Transformers.