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@@ -6,36 +6,19 @@ The quantized version of [stablelm-zephyr-3b](https://huggingface.co/stabilityai
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  You can use it like this (steps taken from the above link):
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- ```python
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- from pathlib import Path
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- import sys
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-
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-
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- genai_llm_bench = Path("openvino.genai/llm_bench/python")
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-
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- if not genai_llm_bench.exists():
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- !git clone https://github.com/openvinotoolkit/openvino.genai.git
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-
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- sys.path.append(str(genai_llm_bench))
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- ```
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-
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- then
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-
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- ```
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- pip install -q --extra-index-url https://download.pytorch.org/whl/cpu -r ./openvino.genai/llm_bench/python/requirements.txt
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- pip uninstall -q -y openvino openvino-dev openvino-nightly
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- pip install -q openvino-nightly
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  ```
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  then
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  ```python
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- from utils.ov_model_classes import register_normalized_configs
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  from optimum.intel.openvino import OVModelForCausalLM
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  from transformers import AutoConfig, AutoTokenizer
 
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- # Load model into Optimum Interface
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- register_normalized_configs()
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  model_path = 'Mihaiii/stablelm-zephyr-3b-OV_FP14-4BIT'
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  model = OVModelForCausalLM.from_pretrained(model_path, compile=False, config=AutoConfig.from_pretrained(model_path, trust_remote_code=True), stateful=True)
 
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  You can use it like this (steps taken from the above link):
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+ ```bash
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+ pip install -q git+https://github.com/huggingface/optimum-intel.git@e22a2ac26b3a6c7854da956d538f784ebeca879b onnx openvino-nightly
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ```
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  then
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  ```python
 
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  from optimum.intel.openvino import OVModelForCausalLM
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  from transformers import AutoConfig, AutoTokenizer
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+ from optimum.utils import NormalizedTextConfig, NormalizedConfigManager
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+ NormalizedConfigManager._conf['stablelm_epoch'] = NormalizedTextConfig.with_args(num_layers='num_hidden_layers', num_attention_heads='num_attention_heads')
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+ NormalizedConfigManager._conf['stablelm-epoch'] = NormalizedTextConfig.with_args(num_layers='num_hidden_layers', num_attention_heads='num_attention_heads')
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  model_path = 'Mihaiii/stablelm-zephyr-3b-OV_FP14-4BIT'
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  model = OVModelForCausalLM.from_pretrained(model_path, compile=False, config=AutoConfig.from_pretrained(model_path, trust_remote_code=True), stateful=True)