openvino-ci
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Upload folder using huggingface_hub
Browse files- README.md +19 -55
- config.json +5 -3
- generation_config.json +6 -0
- openvino_detokenizer.bin +2 -2
- openvino_detokenizer.xml +2 -2
- openvino_model.bin +2 -2
- openvino_model.xml +2 -2
- openvino_tokenizer.bin +2 -2
- openvino_tokenizer.xml +2 -2
- tokenizer.json +0 -0
- tokenizer_config.json +1 -1
README.md
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---
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license: mit
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---
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# dolly-v2-3b-fp16-ov
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* Model creator: [Databricks](https://huggingface.co/databricks)
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* Original model: [dolly-v2-3b](https://huggingface.co/databricks/dolly-v2-3b)
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## Description
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This is [dolly-v2-3b](https://huggingface.co/databricks/dolly-v2-3b) 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.
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* Optimum Intel 1.
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## Running Model Inference
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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/dolly-v2-3b-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
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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_path = "dolly-v2-3b-fp16-ov""
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hf_hub.snapshot_download(model_id, local_dir=model_path)
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```
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import openvino_genai as ov_genai
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print(
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```
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## Limitations
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Check the original model card for [
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## Legal information
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The original model is distributed under [
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## Disclaimer
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---
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license: mit
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license_link: https://choosealicense.com/licenses/mit/
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---
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# dolly-v2-3b-fp16-ov
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* Model creator: [Databricks](https://huggingface.co/databricks)
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* Original model: [dolly-v2-3b](https://huggingface.co/databricks/dolly-v2-3b)
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## Description
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## Compatibility
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The provided OpenVINO™ IR model is compatible with:
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* OpenVINO version 2024.4.0 and higher
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* Optimum Intel 1.20.0 and higher
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## Running Model Inference
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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/dolly-v2-3b-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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## Limitations
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Check the original model card for [original model card](https://huggingface.co/databricks/dolly-v2-3b) for limitations.
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## Legal information
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The original model is distributed under [mit](https://choosealicense.com/licenses/mit/) license. More details can be found in [original model card](https://huggingface.co/databricks/dolly-v2-3b).
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## Disclaimer
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config.json
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"model_type": "gpt_neox",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"rope_scaling": null,
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"rotary_emb_base": 10000,
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"rotary_pct": 0.25,
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"tie_word_embeddings": false,
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"torch_dtype": "
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"transformers_version": "4.
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"use_cache": true,
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"use_parallel_residual": true,
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"vocab_size": 50280
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}
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"model_type": "gpt_neox",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"partial_rotary_factor": 0.25,
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"rope_scaling": null,
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"rope_theta": 10000,
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"rotary_emb_base": 10000,
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"rotary_pct": 0.25,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.45.2",
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"use_cache": true,
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"use_parallel_residual": true,
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"vocab_size": 50280
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 0,
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"eos_token_id": 0,
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"transformers_version": "4.45.2"
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}
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openvino_detokenizer.bin
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openvino_detokenizer.xml
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openvino_model.bin
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openvino_model.xml
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openvino_tokenizer.bin
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openvino_tokenizer.xml
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tokenizer.json
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See raw diff
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tokenizer_config.json
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"### Response:"
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],
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"bos_token": "<|endoftext|>",
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"clean_up_tokenization_spaces":
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"eos_token": "<|endoftext|>",
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