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Model: FinaPolat/phi4_adaptableIE_v2 Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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base_model: microsoft/phi-4
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- phi-4
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- information-extraction
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- ner
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- relation-extraction
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- knowledge-graph
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- slm
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model_creator: FinaPolat
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language:
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- en
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---
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# Phi-4-AdaptableIE: Efficient Adaptive Knowledge Graph Extraction
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#### This model has gguf version: https://huggingface.co/FinaPolat/phi4_adaptableIE_v2-gguf
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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)**.
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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**.
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## 🚀 Model Highlights
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- **Joint Extraction:** Unified NER + RE reducing pipeline complexity.
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- **Ontology-Adaptive:** Zero-shot adaptation to diverse domains (Astronomy, Music, Healthcare, etc.) via dynamic schema variables.
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- **Local & Private:** Optimized for **local CPU-only inference** (via GGUF/Ollama - FinaPolat/phi4_adaptableIE_v2-gguf ), ensuring data sovereignty without external API dependencies.
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- **Instruction Aligned:** Fine-tuned to follow strict negative constraints, ensuring zero conversational filler in outputs.
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## 🛠 Methodology
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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:
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`Prompt = f(task, schema, example, text)`
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---
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## 📝 Prompting Strategy
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To achieve high-fidelity extraction, the model requires a specific prompt structure.
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### 1. System Prompt
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```json
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{
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"role": "system",
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"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."
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}
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```
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### 2. User Prompt Template
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```css
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Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data... [Context] ...
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Always extract numbers, dates, and currency values regardless of the specific task.
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The task at hand is {task}.
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Here is an example of task execution:
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{example}
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Analyze the text and targets carefully, identify relevant information.
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Extract the information in the following format: `{output_format}`.
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If no matching entities are found, return an empty list: [].
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Please provide only the extracted information without any explanations.
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Schema: {schema}
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Text: {inputs}
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```
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### 3. 💻 Usage Examples
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Option 1: Transformers (Single GPU)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "FinaPolat/phi4_adaptableIE_v2"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True)
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task = "Joint NER and RE"
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schema = "['CelestialBody', 'apoapsis', 'averageSpeed']"
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inputs = "(19255) 1994 VK8 has an average speed of 4.56 km per second."
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output_format = "[('subject', 'predicate', 'object')]"
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prompt = f"Task: {task}\nSchema: {schema}\nText: {inputs}\nExtract:"
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input_ids = tokenizer(prompt, return_tensors="pt").to("cuda")
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outputs = model.generate(**input_ids, max_new_tokens=256, temperature=0.0)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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Option 2: High-Throughput Batch Inference (vLLM)
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```python
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from vllm import LLM, SamplingParams
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llm = LLM(
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model="FinaPolat/phi4_adaptableIE_v2",
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dtype="bfloat16",
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trust_remote_code=True,
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gpu_memory_utilization=0.9,
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max_model_len=3000,
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enforce_eager=True,
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distributed_executor_backend="uni"
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)
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sampling_params = SamplingParams(temperature=0.0, max_tokens=256)
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outputs = llm.chat(batch_prompts, sampling_params=sampling_params, use_tqdm=True)
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```
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### 4. 📦 Deployment & Hardware Requirements
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| Deployment Mode | Quantization | Hardware Requirement | Target Latency |
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|-----------------|--------------|------------------------------------------|----------------|
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| Server-side | BF16 | 1× NVIDIA A100 / RTX 4090 (24GB+) | Ultra-Low |
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| Local Consumer | 4-bit GGUF | 16GB RAM (Apple Silicon / PC CPU) | Moderate |
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For CPU-only local execution, refer to the GGUF version: phi4_adaptableIE_v2-gguf📜
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### 5. Citation & Credits
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If you use this model in your research, please cite the Text2KGBench framework and the Microsoft Phi-4 technical report and our work:
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https://github.com/FinaPolat/ENEXA_adaptable_extraction
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Video: https://www.youtube.com/watch?v=your-video-
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