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FinaPolat/
phi4_adaptableIE_v2-gguf

Name: phi4_adaptableIE_v2-gguf
Availability: InStock

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phi4_adaptableIE_v2-gguf : GGUF

This model was finetuned and converted to GGUF format using Unsloth.

Example usage:

For text only LLMs: ./llama.cpp/llama-cli -hf FinaPolat/phi4_adaptableIE_v2-gguf --jinja
For multimodal models: ./llama.cpp/llama-mtmd-cli -hf FinaPolat/phi4_adaptableIE_v2-gguf --jinja

Available Model files:

FinaPolat/phi4_adaptableIE_v2-gguf

Ollama

An Ollama Modelfile is included for easy deployment. Please see: https://github.com/EnexaProject/phi4-ie-demo This was trained 2x faster with Unsloth

Phi-4-AdaptableIE: Efficient Adaptive Knowledge Graph Extraction

This model has gguf version: https://huggingface.co/FinaPolat/phi4_adaptableIE_v2-gguf

Phi-4-AdaptableIE is a specialized 14.7B parameter Small Language Model (SLM) optimized via Supervised Fine-Tuning (SFT) for high-precision, Joint Named Entity Recognition (NER) and Relation Extraction (RE).

Unlike traditional multi-stage pipelines that are prone to cascading error propagation, this model performs entity identification and relational mapping in a single cohesive pass. It is designed to be ontology-adaptive, allowing it to conform to dynamic, unseen schemas at inference time through a specialized Structured Prompt Architecture.

🚀 Model Highlights

Joint Extraction: Unified NER + RE reducing pipeline complexity.
Ontology-Adaptive: Zero-shot adaptation to diverse domains (Astronomy, Music, Healthcare, etc.) via dynamic schema variables.
Local & Private: Optimized for local CPU-only inference (via GGUF/Ollama - FinaPolat/phi4_adaptableIE_v2-gguf ), ensuring data sovereignty without external API dependencies.
Instruction Aligned: Fine-tuned to follow strict negative constraints, ensuring zero conversational filler in outputs.

🛠 Methodology

The model was fine-tuned using QLoRA on the WebNLG subset of the Text2KGBench benchmark. The training process focused on Conversational Alignment, ensuring the model treats extraction as a strict logical mapping: Prompt = f(task, schema, example, text)

📝 Prompting Strategy

To achieve high-fidelity extraction, the model requires a specific prompt structure.

1. System Prompt

json

{
  "role": "system",
  "content": "You are a helpful AI assistant specializing in Information Extraction tasks such as Named Entity Recognition and Relation Extraction. Follow the instructions given by the user."
}

2. User Prompt Template

css

Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data... [Context] ...
Always extract numbers, dates, and currency values regardless of the specific task.

The task at hand is {task}.

Here is an example of task execution:
{example}

Analyze the text and targets carefully, identify relevant information.
Extract the information in the following format: `{output_format}`. 
If no matching entities are found, return an empty list: []. 
Please provide only the extracted information without any explanations.

Schema: {schema}
Text: {inputs}