Update README.md

README.md

@@ -1,8 +1,123 @@
 ---
+language:
+- multilingual
+- ar
+- zh
+- cs
+- da
+- nl
+- en
+- fi
+- fr
+- de
+- he
+- hu
+- it
+- ja
+- ko
+- 'no'
+- pl
+- pt
+- ru
+- es
+- sv
+- th
+- tr
+- uk
 license: mit
-base_model:
-- microsoft/Phi-4-mini-instruct
+license_link: https://huggingface.co/microsoft/Phi-4-mini-instruct/resolve/main/LICENSE
+pipeline_tag: text-generation
 tags:
-- openvino
-- phi4
----
+- nlp
+- code
+base_model: microsoft/Phi-4-mini-instruct
+base_model_relation: quantized
+
+---
+# Phi-4-mini-instruct-int4-ov
+* Model creator: [Microsoft](https://huggingface.co/microsoft)
+* Original model: [Phi-4-mini-instruct](https://huggingface.co/microsoft/Phi-4-mini-instruct)
+
+## Description
+
+This is [Phi-4-mini-instruct](https://huggingface.co/microsoft/Phi-4-mini-instruct) model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2025/documentation/openvino-ir-format.html) (Intermediate Representation) format with weights compressed to INT4 by [NNCF](https://github.com/openvinotoolkit/nncf).
+
+## Quantization Parameters
+
+Weight compression was performed using `nncf.compress_weights` with the following parameters:
+
+* mode: **INT4_ASYM**
+* ratio: **1.0**
+* group_size: **64**
+* awq: **True**
+* scale_estimation: **True**
+* dataset: [wikitext2](https://huggingface.co/datasets/mindchain/wikitext2)
+
+For more information on quantization, check the [OpenVINO model optimization guide](https://docs.openvino.ai/2025/openvino-workflow/model-optimization-guide/weight-compression.html)
+
+## Compatibility
+The provided OpenVINO™ IR model is compatible with:
+* OpenVINO version 2025.1.0 and higher
+* Optimum Intel 1.22.0 and higher
+
+## Running Model Inference with [Optimum Intel](https://huggingface.co/docs/optimum/intel/index)
+
+1. Install packages required for using [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) integration with the OpenVINO backend:
+```
+pip install optimum[openvino]
+```
+
+2. Run model inference:
+```
+from transformers import AutoTokenizer
+from optimum.intel.openvino import OVModelForCausalLM
+model_id = "OpenVINO/Phi-4-mini-instruct-int4-ov"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = OVModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
+
+inputs = tokenizer("What is OpenVINO?", return_tensors="pt")
+outputs = model.generate(**inputs, max_length=200)
+text = tokenizer.batch_decode(outputs)[0]
+print(text)
+```
+For more examples and possible optimizations, refer to [the Inference with Optimum Intel](https://docs.openvino.ai/2025/openvino-workflow-generative/inference-with-optimum-intel.html).
+
+## Running Model Inference with [OpenVINO GenAI](https://github.com/openvinotoolkit/openvino.genai)
+
+1. Install packages required for using OpenVINO GenAI.
+```
+pip install -U openvino openvino-tokenizers openvino-genai
+pip install huggingface_hub
+```
+
+2. Download model from HuggingFace Hub
+   
+```
+import huggingface_hub as hf_hub
+model_id = "OpenVINO/Phi-4-mini-instruct-int4-ov"
+model_path = "Phi-4-mini-instruct-int4-ov"
+hf_hub.snapshot_download(model_id, local_dir=model_path)
+```
+
+3. Run model inference:
+```
+import openvino_genai as ov_genai
+device = "CPU"
+pipe = ov_genai.LLMPipeline(model_path, device)
+print(pipe.generate("What is OpenVINO?", max_length=200))
+```
+More GenAI usage examples can be found in OpenVINO GenAI library [docs](https://docs.openvino.ai/2025/openvino-workflow-generative/inference-with-genai.html) and [samples](https://github.com/openvinotoolkit/openvino.genai?tab=readme-ov-file#openvino-genai-samples)
+
+You can find more detaild usage examples in OpenVINO Notebooks:
+
+- [LLM](https://openvinotoolkit.github.io/openvino_notebooks/?search=LLM)
+- [RAG text generation](https://openvinotoolkit.github.io/openvino_notebooks/?search=RAG+system&tasks=Text+Generation)
+
+## Limitations
+Check the original model card for [original model card](ttps://huggingface.co/microsoft/Phi-4-mini-instruct) for limitations.
+
+## Legal information
+The original model is distributed under [mit](https://huggingface.co/microsoft/Phi-4-mini-instruct/resolve/main/LICENSE) license. More details can be found in [original model card](ttps://huggingface.co/microsoft/Phi-4-mini-instruct).
+
+## Disclaimer
+Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights. See [Intel’s Global Human Rights Principles](https://www.intel.com/content/dam/www/central-libraries/us/en/documents/policy-human-rights.pdf). Intel’s products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.