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README.md
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@@ -64,7 +64,7 @@ python /opt/NeMo-Aligner/examples/nlp/gpt/serve_reward_model.py \
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inference.port=1424
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```
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2. Annotate data files using the served reward model. If you are seeking to reproduce training of [Llama2-70B-SteerLM-Chat](https://huggingface.co/nvidia/Llama2-70B-SteerLM-Chat), this will be the Open Assistant train/val files.
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```python
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python /opt/NeMo-Aligner/examples/nlp/data/steerlm/preprocess_openassistant_data.py --output_directory=data/oasst
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inference.port=1424
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```
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2. Annotate data files using the served reward model. If you are seeking to reproduce training of [Llama2-70B-SteerLM-Chat](https://huggingface.co/nvidia/Llama2-70B-SteerLM-Chat), this will be the Open Assistant train/val files. Then follow the next step to train a SteerLM model based on [SteerLM training user guide](https://docs.nvidia.com/nemo-framework/user-guide/latest/modelalignment/steerlm.html#step-5-train-the-attribute-conditioned-sft-model) .
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```python
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python /opt/NeMo-Aligner/examples/nlp/data/steerlm/preprocess_openassistant_data.py --output_directory=data/oasst
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