IMCatalina-v1.0

Model summary

• Parsing unstructured CVs/resumes
• Converting CV content into structured formats (JSON / YAML)
• Generating professional job descriptions from structured inputs

Intended use

Primary use cases

• CV and resume parsing
• Structured CV normalization (JSON / YAML)
• Extraction of skills, roles, education, and experience
• Job description generation for recruitment platforms
• Preprocessing for ATS and HR systems

Explicitly out-of-scope

• Candidate ranking or scoring
• Hiring recommendations
• Candidate–job matching
• Automated decision-making
• Psychological or behavioral inference

Model details

• Base model: microsoft/phi-4
• Model type: Decoder-only causal language model
• Architecture: Transformer (Phi family)
• Parameters: ~14B
• Context length: up to 16k tokens
• Languages: English
• Training type: Full fine-tuning

Training

Training data

• Domain: Recruitment and HR documentation
• Data type: Synthetic and curated structured data
• Formats:
  • Instruction–response
  • Schema-constrained generation
• Content includes:
  • CVs and resumes
  • Job descriptions
  • Skills, roles, education, and experience fields
• Data processing:
  • Deduplication
  • Schema validation
  • Removal of malformed samples
  • Consistency and format checks

No real personal data was intentionally included in the training datasets.