--- license: apache-2.0 language: - en --- # codegen25-7b-multi * Model creator: [Salesforce](https://huggingface.co/Salesforce) * Original model: [CodeGen2.5-7B-multi](https://huggingface.co/Salesforce/codegen25-7b-multi_P) ## Description This is [CodeGen2.5-7B-multi](https://huggingface.co/Salesforce/codegen25-7b-multi_P) 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** 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] tiktoken ``` 2. Run model inference: ``` from transformers import AutoTokenizer from optimum.intel.openvino import OVModelForCausalLM model_id = "OpenVINO/codegen25-7b-multi-int8-ov" tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) model = OVModelForCausalLM.from_pretrained(model_id) text = "def hello_world():" input_ids = tokenizer(text, return_tensors="pt").input_ids generated_ids = model.generate(input_ids, max_length=128) print(tokenizer.decode(generated_ids[0], skip_special_tokens=True)) ``` 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). ## Limitations Check the original model card for [limitations](https://huggingface.co/Salesforce/codegen25-7b-instruct_P#intended-use-and-limitations). ## Legal information The original model is distributed under [Apache 2.0](https://choosealicense.com/licenses/apache-2.0/) license. More details can be found in [original model card](https://huggingface.co/Salesforce/codegen25-7b-multi_P).