Instructions to use FinaPolat/phi4_adaptableIE_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FinaPolat/phi4_adaptableIE_v2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="FinaPolat/phi4_adaptableIE_v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FinaPolat/phi4_adaptableIE_v2")
model = AutoModelForCausalLM.from_pretrained("FinaPolat/phi4_adaptableIE_v2", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FinaPolat/phi4_adaptableIE_v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FinaPolat/phi4_adaptableIE_v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FinaPolat/phi4_adaptableIE_v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FinaPolat/phi4_adaptableIE_v2

SGLang

How to use FinaPolat/phi4_adaptableIE_v2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "FinaPolat/phi4_adaptableIE_v2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FinaPolat/phi4_adaptableIE_v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "FinaPolat/phi4_adaptableIE_v2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FinaPolat/phi4_adaptableIE_v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use FinaPolat/phi4_adaptableIE_v2 with Docker Model Runner:
```
docker model run hf.co/FinaPolat/phi4_adaptableIE_v2
```

FinaPolat commited on Feb 5

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@@ -27,7 +27,7 @@ Unlike traditional multi-stage pipelines that are prone to cascading error propa
 ## 🚀 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), ensuring data sovereignty without external API dependencies.
 - **Instruction Aligned:** Fine-tuned to follow strict negative constraints, ensuring zero conversational filler in outputs.
 ## 🛠 Methodology
@@ -112,9 +112,11 @@ outputs = llm.chat(batch_prompts, sampling_params=sampling_params, use_tqdm=True
 ### 4. 📦 Deployment & Hardware Requirements
-Deployment Mode QuantizationHardware RequirementTarget
-LatencyServer-sideBF161x NVIDIA A100/RTX 4090 (24GB+)Ultra-Low
-Local Consumer4-bit GGUF16GB RAM (Apple Silicon / PC CPU)Moderate
 For CPU-only local execution, refer to the GGUF version: phi4_adaptableIE_v2-gguf📜

 ## 🚀 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
 ### 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📜