Update README.md

README.md

@@ -42,33 +42,35 @@ base_model_relation: quantized
 
 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).
 
+With the following pyproject.yoml
+```yaml
+[project]
+name = "export"
+version = "0.1.0"
+description = "Export models"
+readme = "README.md"
+requires-python = "==3.12.*"
+dependencies = [
+    "openvino==2025.2.0",
+    "optimum[openvino]",
+    "optimum-intel",
+    "openvino-genai",
+    "huggingface-hub==0.33.0",
+    "tokenizers==0.21.1"
+]
+```
+
+Then run the export
+```bash
+uv sync
+uv run optimum-cli export openvino --model microsoft/phi-4-mini-instruct --task text-generation-with-past --weight-format int4 --group-size -1 --ratio 1.0 --sym --trust-remote-code phi-4-mini-instruct/INT4-NPU_compressed_weights 
+```
+
 ## Compatibility
 The provided OpenVINO™ IR model is compatible with:
 * OpenVINO version 2025.2.0 and higher
 * Optimum Intel 1.23.0 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.
@@ -81,17 +83,24 @@ pip install 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"
+model_id = "bweng/phi-4-mini-instruct-int4-ov-npu"
+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))
+device = "NPU"
+pipe = ov_genai.LLMPipeline(model_path, "NPU", MAX_PROMPT_LEN=4096, MIN_RESPONSE_LEN=1024, CACHE_DIR="./cache")
+
+# Create a proper GenerationConfig object
+gen_config = GenerationConfig(apply_chat_template=True, max_new_tokens=1024)
+
+# Now call generate with the correct config object
+output = pipe.generate("How are you doing?", generation_config=gen_config)
+print(output)
+
 ```
 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)