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66 lines
2.4 KiB
Markdown
66 lines
2.4 KiB
Markdown
<!--Copyright 2025 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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*This model was published in HF papers on 2024-08-23 and contributed to Hugging Face Transformers on 2025-02-14.*
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# GraniteMoeShared
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## Overview
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The GraniteMoe model was proposed in [Power Scheduler: A Batch Size and Token Number Agnostic Learning Rate Scheduler](https://huggingface.co/papers/2408.13359) by Yikang Shen, Matthew Stallone, Mayank Mishra, Gaoyuan Zhang, Shawn Tan, Aditya Prasad, Adriana Meza Soria, David D. Cox and Rameswar Panda.
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Additionally this class GraniteMoeSharedModel adds shared experts for Moe.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_path = "ibm-research/moe-7b-1b-active-shared-experts"
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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# drop device_map if running on CPU
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model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto")
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model.eval()
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# change input text as desired
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prompt = "Write a code to find the maximum value in a list of numbers."
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# tokenize the text
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input_tokens = tokenizer(prompt, return_tensors="pt").to(model.device)
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# generate output tokens
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output = model.generate(**input_tokens, max_new_tokens=100)
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# decode output tokens into text
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output = tokenizer.batch_decode(output)
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# loop over the batch to print, in this example the batch size is 1
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for i in output:
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print(i)
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```
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This HF implementation is contributed by [Mayank Mishra](https://huggingface.co/mayank-mishra), [Shawn Tan](https://huggingface.co/shawntan) and [Sukriti Sharma](https://huggingface.co/SukritiSharma).
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## GraniteMoeSharedConfig
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[[autodoc]] GraniteMoeSharedConfig
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## GraniteMoeSharedModel
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[[autodoc]] GraniteMoeSharedModel
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- forward
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## GraniteMoeSharedForCausalLM
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[[autodoc]] GraniteMoeSharedForCausalLM
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- forward
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