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--- |
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license: apache-2.0 |
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base_model: |
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- TinyLlama/TinyLlama-1.1B-Chat-v1.0 |
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--- |
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# TinyLlama-1.1B-Chat-v1.0-fp16-ov |
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* Model creator: [TinyLlama](https://huggingface.co/TinyLlama) |
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* Original model: [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) |
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## Description |
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This is [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2024/documentation/openvino-ir-format.html) (Intermediate Representation) format. |
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## Compatibility |
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The provided OpenVINO™ IR model is compatible with: |
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* OpenVINO version 2024.2.0 and higher |
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* Optimum Intel 1.17.0 and higher |
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## Running Model Inference with [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) |
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1. Install packages required for using [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) integration with the OpenVINO backend: |
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``` |
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pip install optimum[openvino] |
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``` |
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2. Run model inference: |
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``` |
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from transformers import AutoTokenizer |
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from optimum.intel.openvino import OVModelForCausalLM |
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model_id = "OpenVINO/TinyLlama-1.1B-Chat-v1.0-fp16-ov" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = OVModelForCausalLM.from_pretrained(model_id) |
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inputs = tokenizer("What is OpenVINO?", return_tensors="pt") |
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outputs = model.generate(**inputs, max_length=200) |
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text = tokenizer.batch_decode(outputs)[0] |
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print(text) |
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``` |
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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). |
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## Running Model Inference with [OpenVINO GenAI](https://github.com/openvinotoolkit/openvino.genai) |
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1. Install packages required for using OpenVINO GenAI. |
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``` |
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pip install openvino-genai huggingface_hub |
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``` |
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2. Download model from HuggingFace Hub |
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``` |
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import huggingface_hub as hf_hub |
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model_id = "OpenVINO/TinyLlama-1.1B-Chat-v1.0-fp16-ov" |
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model_path = "TinyLlama-1.1B-Chat-v1.0-fp16-ov" |
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hf_hub.snapshot_download(model_id, local_dir=model_path) |
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``` |
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3. Run model inference: |
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``` |
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import openvino_genai as ov_genai |
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device = "CPU" |
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pipe = ov_genai.LLMPipeline(model_path, device) |
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print(pipe.generate("What is OpenVINO?", max_length=200)) |
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``` |
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More GenAI usage examples can be found in OpenVINO GenAI library [docs](https://github.com/openvinotoolkit/openvino.genai/blob/master/src/README.md) and [samples](https://github.com/openvinotoolkit/openvino.genai?tab=readme-ov-file#openvino-genai-samples) |
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## Legal information |
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The original model is distributed under [apache-2.0](https://choosealicense.com/licenses/apache-2.0/) license. More details can be found in [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0). |
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## Disclaimer |
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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. |
