Create README.md

README.md

@@ -0,0 +1,108 @@
+---
+license: apache-2.0
+license_link: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct/blob/main/LICENSE
+base_model: 
+  - Qwen/Qwen2.5-7B-Instruct
+base_model_relation: quantized
+
+---
+# Qwen2.5-7B-Instruct-int4-npu-ov
+ * Model creator: [Qwen](https://huggingface.co/Qwen)
+ * Original model: [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)
+
+## Description
+This is [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-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**
+* group_size: **128**
+
+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 1.24.0 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/qwen2.5-7b-instruct-int4-ov"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = OVModelForCausalLM.from_pretrained(model_id)
+
+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 openvino-genai huggingface_hub
+```
+
+2. Download model from HuggingFace Hub
+   
+```
+import huggingface_hub as hf_hub
+
+model_id = "OpenVINO/qwen2.5-7b-instruct-int4-ov"
+model_path = "qwen2.5-7b-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)
+- [Convert models from ModelScope to OpenVINO](https://openvinotoolkit.github.io/openvino_notebooks/?search=Convert+models+from+ModelScope+to+OpenVINO)
+
+## Limitations
+
+Check the original [model card](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) for limitations.
+
+## Legal information
+
+The original model is distributed under [Apache License Version 2.0](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct/blob/main/LICENSE) license. More details can be found in [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-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.