license: other
Phi-3-mini-FastDraft-50M-int8-ov
Description
FastDraft is a novel and efficient approach for pre-training and aligning a draft model to any LLM to be used with speculative decoding, by incorporating efficient pre-training followed by fine-tuning over synthetic datasets generated by the target model. FastDraft was presented in https://arxiv.org/abs/2411.11055 at ENLSP@NeurIPS24 by Intel Labs.
This is a draft model that was trained with FastDraft to accompany Phi-3-mini-4k-instruct.
This is Phi-3-mini-FastDraft-50M model converted to the OpenVINO™ IR (Intermediate Representation) format with weights compressed to int8 by NNCF.
Quantization Parameters
Weight compression was performed using nncf.compress_weights with the following parameters:
For more information on quantization, check the OpenVINO model optimization guide.
Compatibility
The provided OpenVINO™ IR model is compatible with:
- OpenVINO version <2024.4 > and higher
- Optimum Intel <1.20.0> and higher
Running Model Inference with OpenVINO GenAI
- Install packages required for using OpenVINO GenAI with Speculative decoding:
pip install openvino-genai huggingface_hub
- Download models from HuggingFace Hub
import huggingface_hub as hf_hub
main_model_id = “OpenVINO/Phi-3-mini-4k-instruct-int4-ov”
draft_model_id = "OpenVINO/Phi-3-mini-FastDraft-50M-int8-ov"
main_model_path = "main"
draft_model_path = “draft”
hf_hub.snapshot_download(main_model_id, local_dir=main_model_path)
hf_hub.snapshot_download(draft_model_id, local_dir=draft_model_path)
- Run model inference using the speculative decoding and specify the pipeline parameters:
import openvino_genai
prompt = “What is OpenVINO?”
config = openvino_genai.GenerationConfig()
config.num_assistant_tokens = 3
config.max_new_tokens = 128
def streamer(subword):
print(subword, end='', flush=True)
return False
main_device = "CPU"
draft_device = "CPU"
draft_model = openvino_genai.draft_model(draft_model_path, draft_device)
scheduler_config = openvino_genai.SchedulerConfig()
scheduler_config.cache_size = 2
pipe = openvino_genai.LLMPipeline(main_model_path, main_device, scheduler_config=scheduler_config, draft_model=draft_model)
pipe.generate(prompt, config, streamer)
More GenAI usage examples can be found in OpenVINO GenAI library docs and samples
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. 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.