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Phi-4-mini-FastDraft-120M-int8-ov

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  1. .gitattributes +1 -0
  2. README.md +91 -0
  3. added_tokens.json +12 -0
  4. config.json +42 -0
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.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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+
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+ <!-- Metadata section, filled in yaml format. Usually contains info about license -->
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+ ---
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+ license: Intel Research Use License Agreement
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+ ---
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+ <!-- Model name used as model card title -->
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+ # Phi-4-mini-FastDraft-120M-int8-ov
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+ <!-- Original model reference -->
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+ <!-- Description of converted model -->
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+ ## Description
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+
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+ 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.
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+ FastDraft was presented in https://arxiv.org/abs/2411.11055 at ENLSP@NeurIPS24 by Intel Labs.
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+
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+ This is a draft model that was trained with FastDraft to accompany [Phi-4-mini-instruct](https://huggingface.co/microsoft/Phi-4-mini-instruct).
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+ <!-- Comment and reference on NNCF applicable only for INT8 and INT4 models -->
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+ This is Phi-4-mini-FastDraft-120M model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2025/documentation/openvino-ir-format.html) (Intermediate Representation) format with weights compressed to INT8 by [Optimum](https://github.com/huggingface/optimum).
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+
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+ ## Quantization Parameters
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+
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+ Weight compression was performed using `nncf.compress_weights` with the following parameters:
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+
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+ * mode: **INT8_ASYM**
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+
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+ For more information on quantization, check the [OpenVINO model optimization guide](https://docs.openvino.ai/2024/openvino-workflow/model-optimization-guide/weight-compression.html).
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+
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+ <!-- Info about required openvino and optimum intel versions. Usually, versions used for model conversion used as lower bound -->
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+
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+ ## Compatibility
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+
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+ The provided OpenVINO™ IR model is compatible with:
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+
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+ * OpenVINO version <2025.1 > and higher
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+ * Optimum Intel <1.23.0> and higher
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+
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+ ## Running Model Inference with OpenVINO GenAI
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+
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+ <!-- Example model usage -->
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+
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+ 1. Install packages required for using [OpenVINO GenAI](https://github.com/openvinotoolkit/openvino.genai) with Speculative decoding:
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+
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+ ```
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+ pip install openvino-genai huggingface_hub
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+ ```
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+
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+ 2. Download models from HuggingFace Hub
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+ ```
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+ import huggingface_hub as hf_hub
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+
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+ main_model_id = "OpenVINO/Phi-4-mini-instruct-int4-ov"
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+ draft_model_id = "OpenVINO/Phi-4-mini-FastDraft-120M-int8-ov"
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+
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+ main_model_path = "main"
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+ draft_model_path = "draft"
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+
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+ hf_hub.snapshot_download(main_model_id, local_dir=main_model_path)
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+ hf_hub.snapshot_download(draft_model_id, local_dir=draft_model_path)
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+ ```
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+ 3. Run model inference using the speculative decoding and specify the pipeline parameters:
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+ ```
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+ import openvino_genai
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+
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+ prompt = “What is OpenVINO?”
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+
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+ config = openvino_genai.GenerationConfig()
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+ config.num_assistant_tokens = 3
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+ config.max_new_tokens = 128
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+
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+ def streamer(subword):
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+ print(subword, end='', flush=True)
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+ return False
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+
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+ main_device = "CPU"
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+ draft_device = "CPU"
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+
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+ draft_model = openvino_genai.draft_model(draft_model_path, draft_device)
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+
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+ pipe = openvino_genai.LLMPipeline(args.model_dir, main_device, draft_model=draft_model)
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+
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+ pipe.generate(prompt, config, streamer)
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+ ```
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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/tree/master/samples)
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+
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+ ## Legal Information
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+
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+ The model is distributed under the [Intel Research Use License Agreement](https://huggingface.co/OpenVINO/Llama-3.1-8B-Instruct-FastDraft-150M-int8-ov/blob/main/LICENSE.md).
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+
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+ ## Disclaimer
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+
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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.
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