chendren/contact-center-coaching-mistral-7b-mlx

Contact Center Coaching - Mistral 7B LoRA (MLX)

Fine-tuned Mistral-7B-Instruct-v0.3 model for contact center agent coaching and journey-specific analysis using Apple MLX framework.

Model Description

This model provides journey-specific coaching recommendations for contact center agents by analyzing customer conversations with temporal context awareness. It's optimized for Apple Silicon (M1/M2/M3/M4) using the MLX framework.

Key Capabilities:

• 🎯 Journey-specific coaching (Onboarding, Escalation, Retention, Sales/Upsell)
• ⏰ Temporal context interpretation (time, day, queue wait, peak season)
• 📊 Structured JSON output (immediate priorities, long-term development, strengths)
• 💡 Identifies agent strengths to leverage
• 🚀 Fast inference (4s latency, 27 tokens/sec)

Quick Start

Installation

pip install mlx-lm

Usage

from mlx_lm import load, generate

# Load model
model, tokenizer = load(
    "mlx-community/Mistral-7B-Instruct-v0.3",
    adapter_path="chendren/contact-center-coaching-mistral-7b-mlx"
)

# Analyze conversation
prompt = """<|user|>
[TEMPORAL CONTEXT]
Timestamp: 2026-01-02T14:30:00
Time of day: afternoon
Day: Thursday
Business hours: Yes
Queue wait: 3.0 minutes
Peak season: no

Analyze this agent conversation and provide coaching recommendations.

Conversation:
[14:30:00] <Agent>: Thank you for calling. How can I help you today?
[14:30:03] <Customer>: I'm having issues with my account login.
[14:30:08] <Agent>: I can help with that. Let me check your account.
[14:30:15] <Agent>: I see the issue. I'll reset your password now.
[14:30:25] <Customer>: Great, thank you!

---

**Call Summary:**
- Issue: Account access problem
- Resolution: Password reset
- Outcome: Resolved

Provide:
1. Immediate coaching priorities
2. Long-term development areas
3. Specific examples from the conversation
<|assistant|>"""

response = generate(model, tokenizer, prompt=prompt, max_tokens=300)
print(response)

Expected Output

{
  "immediate_priorities": [
    "Ask for confirmation after resolution",
    "Offer alternative authentication methods"
  ],
  "long_term": [
    "Develop scripts for common account issues",
    "Train on proactive security recommendations"
  ],
  "strengths_to_leverage": [
    "Quick problem identification",
    "Clear communication"
  ]
}

Training Details

Training Data

• Dataset Size: 1,500 high-quality contact center conversations
• Data Source: Synthetically generated using InceptionLabs.ai Mercury model
• Format: MLX JSONL with temporal features
• Features: 6 temporal context fields + full conversation transcripts

Training Configuration

Parameter	Value
Base Model	Mistral-7B-Instruct-v0.3 (MLX)
Method	LoRA (Low-Rank Adaptation)
Trainable Parameters	0.145% (10.5M / 7.2B)
Training Iterations	1,200 (early stopped at 1,360)
Batch Size	4
Learning Rate	1e-5
Training Time	5.3 hours
Hardware	M4 Max (128GB RAM)
Framework	MLX (Apple Silicon optimized)

Performance Metrics

Metric	Value	Quality
Validation Loss	0.268	74% improvement
Test Perplexity	1.35	Excellent (<5 is ideal)
Training Loss	0.198	81% improvement
Overfitting	Minimal	Train/Val gap: 35.4%

Inference Performance

Metric	Value
Latency	~4 seconds per analysis
Throughput	26-27 tokens/second
Memory Usage	~16 GB peak
Model Size	40 MB (LoRA adapters)

Journey Types Supported

The model adapts its coaching based on customer journey stage:

1. 🆕 Onboarding - New customer setup and first-time use
2. ⚠️ Escalation - Complex technical issues requiring escalation
3. 🔄 Retention - Cancellation prevention and value reinforcement
4. 📈 Sales/Upsell - Account upgrades and additional services

Model Architecture

• Base: Mistral-7B-Instruct-v0.3
• Fine-tuning: LoRA adapters (rank-based parameter-efficient tuning)
• Precision: bfloat16
• Context Length: 2048 tokens
• Optimizer: AdamW

Limitations

• Language: English only
• Domain: Contact center / customer service specific
• Context: Optimized for conversational customer support scenarios
• Sequence Length: Best performance on conversations <2048 tokens (98.4% of data)

Ethical Considerations

• Synthetic Data: All training data was synthetically generated, no real customer conversations used
• Privacy: No PII or sensitive customer information in training data
• Bias: Model trained on diverse synthetic scenarios to minimize bias
• Use Case: Designed for agent coaching and quality improvement, not customer-facing automation

Citation

@misc{contact-center-coaching-mistral-7b-mlx,
  title={Contact Center Coaching - Mistral 7B LoRA (MLX)},
  author={Chad Hendren},
  year={2026},
  publisher={HuggingFace},
  howpublished={\url{https://huggingface.co/chendren/contact-center-coaching-mistral-7b-mlx}}
}

License

Apache 2.0 (inherits from base Mistral-7B-Instruct-v0.3 model)

Model Card Authors

Chad Hendren (@chendren)

Contact

• GitHub: https://github.com/chendren
• HuggingFace: https://huggingface.co/chendren

Acknowledgments

• Apple MLX Team - For the excellent Apple Silicon ML framework
• Mistral AI - For the Mistral-7B base model
• InceptionLabs.ai - For synthetic data generation via Mercury model
• HuggingFace - For model hosting and MLX community support

---

Status: ✅ Ready for deployment and evaluation

Last Updated: January 1, 2026

Model Version: 1.0.0 (Iteration 1200)