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README.md

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-license: mit
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+---
+license: mit
+---
+
+# notus-7b-v1-fp16-ov
+
+ * Model creator: [Argilla](https://huggingface.co/argilla)
+ * Original model: [notus-7b-v1](https://huggingface.co/argilla/notus-7b-v1)
+
+## Description
+
+This is [notus-7b-v1](https://huggingface.co/argilla/notus-7b-v1) model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2024/documentation/openvino-ir-format.html) (Intermediate Representation) format with weights compressed to int8 by [NNCF](https://github.com/openvinotoolkit/nncf).
+
+## Quantization Parameters
+
+Weight compression was performed using `nncf.compress_weights` with the following parameters:
+
+* mode: **INT8_ASYM**
+* sensitivity_metric: **weight_quantization_error**
+
+For more information on quantization, check the [OpenVINO model optimization guide](https://docs.openvino.ai/2024/openvino-workflow/model-optimization-guide/weight-compression.html).
+
+## Compatibility
+
+The provided OpenVINO™ IR model is compatible with:
+
+* OpenVINO version 2024.1.0 and higher
+* Optimum Intel 1.16.0 and higher
+
+## Running Model Inference
+
+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/notus-7b-v1-fp16-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 [OpenVINO Large Language Model Inference Guide](https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide.html).
+
+## Legal information
+
+The original model is distributed under mit license. More details can be found in [original model card](https://huggingface.co/argilla/notus-7b-v1).