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

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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.

chat_template.jinja

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+{% for message in messages %}{% if (message['role'] == 'system') %}{{'<|im_start|>system<|im_sep|>' + message['content'] + '<|im_end|>'}}{% elif (message['role'] == 'user') %}{{'<|im_start|>user<|im_sep|>' + message['content'] + '<|im_end|>'}}{% elif (message['role'] == 'assistant') %}{{'<|im_start|>assistant<|im_sep|>' + message['content'] + '<|im_end|>'}}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant<|im_sep|>' }}{% endif %}

config.json

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+{
+  "architectures": [
+    "Phi3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 100257,
+  "dtype": "bfloat16",
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+  "eos_token_id": 100265,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 17920,
+  "max_position_embeddings": 16384,
+  "model_type": "phi3",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 40,
+  "num_key_value_heads": 10,
+  "original_max_position_embeddings": 16384,
+  "pad_token_id": 100265,
+  "partial_rotary_factor": 1.0,
+  "resid_pdrop": 0.0,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 250000,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.3",
+  "use_cache": true,
+  "vocab_size": 100352
+}

generation_config.json

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+  "eos_token_id": [
+    100265
+  ],
+  "pad_token_id": 100265,
+  "transformers_version": "4.57.3"
+}

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special_tokens_map.json

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tokenizer_config.json

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+}