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95 lines
3.5 KiB
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95 lines
3.5 KiB
Markdown
<!--Copyright 2024 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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*This model was published in HF papers on 2024-11-22 and contributed to Hugging Face Transformers on 2025-01-27.*
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# Zamba2
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<div class="flex flex-wrap space-x-1">
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<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
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<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
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</div>
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[Zamba2](https://huggingface.co/papers/2411.15242) is a large language model (LLM) trained by Zyphra, and made available under an Apache 2.0 license. Please see the [Zyphra Hugging Face](https://huggingface.co/collections/zyphra/) repository for model weights.
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This model was contributed by [pglo](https://huggingface.co/pglo).
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## Model details
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[Zamba2-1.2B](https://www.zyphra.com/post/zamba2-mini), [Zamba2-2.7B](https://www.zyphra.com/post/zamba2-small) and [Zamba2-7B](https://www.zyphra.com/post/zamba2-7b) are hybrid models combining state-space models (Specifically [Mamba2](https://github.com/state-spaces/mamba)) and transformer, and were trained using next-token prediction. Zamba2 uses shared transformer layers after every 6 mamba blocks. It uses the [Mistral v0.1 tokenizer](https://huggingface.co/mistralai/Mistral-7B-v0.1). We came to this architecture after a series of ablations at small scales. Zamba2-1.2B, Zamba2-2.7B and Zamba2-7B were pre-trained on 2T and 3T tokens, respectively.
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<img src=https://github.com/user-attachments/assets/c2cff209-b901-483c-87aa-774b82a0769f width=30% height=40% />
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## Quick start
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### Presequities
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Zamba2 requires you use `transformers` version 4.48.0 or higher:
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```bash
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pip install transformers>=4.48.0
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```
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## Inference
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("Zyphra/Zamba2-7B")
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model = AutoModelForCausalLM.from_pretrained("Zyphra/Zamba2-7B", device_map="auto")
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input_text = "What factors contributed to the fall of the Roman Empire?"
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input_ids = tokenizer(input_text, return_tensors="pt").to(model.device)
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outputs = model.generate(**input_ids, max_new_tokens=100)
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print(tokenizer.decode(outputs[0]))
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```
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## Model card
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The model cards can be found at:
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* [Zamba2-1.2B](https://huggingface.co/Zyphra/Zamba2-1.2B)
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* [Zamba2-2.7B](https://huggingface.co/Zyphra/Zamba2-2.7B)
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* [Zamba2-7B](https://huggingface.co/Zyphra/Zamba2-7B)
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## Issues
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For issues with model output, or community discussion, please use the Hugging Face community [forum](https://huggingface.co/Zyphra/Zamba2-7B/discussions)
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## License
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The model weights are open-sourced via an Apache 2.0 license.
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## Zamba2Config
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[[autodoc]] Zamba2Config
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## Zamba2Model
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[[autodoc]] Zamba2Model
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- forward
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## Zamba2ForCausalLM
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[[autodoc]] Zamba2ForCausalLM
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- forward
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## Zamba2ForSequenceClassification
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[[autodoc]] transformers.Zamba2ForSequenceClassification
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- forward
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