---
license: apache-2.0
base_model: microsoft/phi-4
tags:
- text-generation-inference
- transformers
- unsloth
- phi-4
- information-extraction
- ner
- relation-extraction
- knowledge-graph
- slm
model_creator: FinaPolat
language:
- en
---

# 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}

```

### 3. 💻 Usage Examples
Option 1: Transformers (Single GPU)

```python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "FinaPolat/phi4_adaptableIE_v2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True)

task = "Joint NER and RE"
schema = "['CelestialBody', 'apoapsis', 'averageSpeed']"
inputs = "(19255) 1994 VK8 has an average speed of 4.56 km per second."
output_format = "[('subject', 'predicate', 'object')]"

prompt = f"Task: {task}\nSchema: {schema}\nText: {inputs}\nExtract:"

input_ids = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids, max_new_tokens=256, temperature=0.0)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

Option 2: High-Throughput Batch Inference (vLLM)

```python

from vllm import LLM, SamplingParams

llm = LLM(
    model="FinaPolat/phi4_adaptableIE_v2",
    dtype="bfloat16",
    trust_remote_code=True,
    gpu_memory_utilization=0.9,
    max_model_len=3000,
    enforce_eager=True, 
    distributed_executor_backend="uni" 
)

sampling_params = SamplingParams(temperature=0.0, max_tokens=256)
outputs = llm.chat(batch_prompts, sampling_params=sampling_params, use_tqdm=True)

```

### 4. 📦 Deployment & Hardware Requirements

| Deployment Mode | Quantization | Hardware Requirement                     | Target Latency |
|-----------------|--------------|------------------------------------------|----------------|
| Server-side     | BF16         | 1× NVIDIA A100 / RTX 4090 (24GB+)         | Ultra-Low      |
| Local Consumer  | 4-bit GGUF   | 16GB RAM (Apple Silicon / PC CPU)         | Moderate       |


For CPU-only local execution, refer to the GGUF version: phi4_adaptableIE_v2-gguf📜 

### 5. Citation & Credits

If you use this model in your research, please cite the Text2KGBench framework and the Microsoft Phi-4 technical report and our work: 
https://github.com/FinaPolat/ENEXA_adaptable_extraction

Video: https://www.youtube.com/watch?v=your-video-