metadata
language:
- en
base_model:
- microsoft/phi-4
pipeline_tag: text-generation
library_name: transformers
tags:
- phi
- fine-tuned
- full-finetune
- instruction-tuning
- text-generation
- recruitment
- resume-parsing
- job-description-generation
IMCatalina-v1.0
Model summary
IMCatalina-v1.0 is a fully fine-tuned version of Phi-4 specialized in recruitment document processing.
The model focuses exclusively on:
- Parsing unstructured CVs/resumes
- Converting CV content into structured formats (JSON / YAML)
- Generating professional job descriptions from structured inputs
This model was trained end-to-end (full fine-tuning) and does not perform candidate scoring, ranking, or hiring decisions.
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.