初始化项目，由ModelHub XC社区提供模型

Model: rmtlabs/IMCatalina-v1.0
Source: Original Platform

README.md

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+---
+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.