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* toctree * not-doctested.txt * collapse sections * feedback * update * rewrite get started sections * fixes * fix * loading models * fix * customize models * share * fix link * contribute part 1 * contribute pt 2 * fix toctree * tokenization pt 1 * Add new model (#32615) * v1 - working version * fix * fix * fix * fix * rename to correct name * fix title * fixup * rename files * fix * add copied from on tests * rename to `FalconMamba` everywhere and fix bugs * fix quantization + accelerate * fix copies * add `torch.compile` support * fix tests * fix tests and add slow tests * copies on config * merge the latest changes * fix tests * add few lines about instruct * Apply suggestions from code review Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * fix * fix tests --------- Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * "to be not" -> "not to be" (#32636) * "to be not" -> "not to be" * Update sam.md * Update trainer.py * Update modeling_utils.py * Update test_modeling_utils.py * Update test_modeling_utils.py * fix hfoption tag * tokenization pt. 2 * image processor * fix toctree * backbones * feature extractor * fix file name * processor * update not-doctested * update * make style * fix toctree * revision * make fixup * fix toctree * fix * make style * fix hfoption tag * pipeline * pipeline gradio * pipeline web server * add pipeline * fix toctree * not-doctested * prompting * llm optims * fix toctree * fixes * cache * text generation * fix * chat pipeline * chat stuff * xla * torch.compile * cpu inference * toctree * gpu inference * agents and tools * gguf/tiktoken * finetune * toctree * trainer * trainer pt 2 * optims * optimizers * accelerate * parallelism * fsdp * update * distributed cpu * hardware training * gpu training * gpu training 2 * peft * distrib debug * deepspeed 1 * deepspeed 2 * chat toctree * quant pt 1 * quant pt 2 * fix toctree * fix * fix * quant pt 3 * quant pt 4 * serialization * torchscript * scripts * tpu * review * model addition timeline * modular * more reviews * reviews * fix toctree * reviews reviews * continue reviews * more reviews * modular transformers * more review * zamba2 * fix * all frameworks * pytorch * supported model frameworks * flashattention * rm check_table * not-doctested.txt * rm check_support_list.py * feedback * updates/feedback * review * feedback * fix * update * feedback * updates * update --------- Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> Co-authored-by: Quentin Gallouédec <45557362+qgallouedec@users.noreply.github.com>
70 lines
2.9 KiB
Markdown
70 lines
2.9 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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-->
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# Bamba
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<div class="flex flex-wrap space-x-1">
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<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
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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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## Overview
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Bamba-9B is a decoder-only language model based on the [Mamba-2](https://github.com/state-spaces/mamba) architecture and is designed to handle a wide range of text generation tasks. It is trained from scratch using a two-stage training approach. In the first stage, the model is trained on 2 trillion tokens from the Dolma v1.7 dataset. In the second stage, it undergoes additional training on 200 billion tokens, leveraging a carefully curated blend of high-quality data to further refine its performance and enhance output quality.
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Checkout all Bamba-9B model checkpoints [here](https://github.com/foundation-model-stack/bamba).
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## BambaConfig
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| Model | Params | # Layers | Hidden Dim. | Attention Heads | GQA | KV Heads | Context Length | Tied Embeddings |
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|-------------------|--------------|----------|-------------|-----------------|-----|----------|----------------|------------------|
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| Bamba | 9B (9.78B) | 32 | 4096 | 32 | Yes | 8 | 4096 | True |
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[[autodoc]] BambaConfig
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<!---
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## Usage Tips
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Tips:
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- The architecture is based on Mamba-2 models.
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## BambaModel
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[[autodoc]] BambaModel
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- forward
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-->
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## BambaForCausalLM
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("ibm-fms/Bamba-9B")
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tokenizer = AutoTokenizer.from_pretrained("ibm-fms/Bamba-9B")
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message = ["Mamba is a snake with following properties "]
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inputs = tokenizer(message, return_tensors='pt', return_token_type_ids=False)
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response = model.generate(**inputs, max_new_tokens=64)
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print(tokenizer.batch_decode(response, skip_special_tokens=True)[0])
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```
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[[autodoc]] BambaForCausalLM
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- forward
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This HF implementation is contributed by [ani300](https://github.com/ani300) and [fabianlim](https://github.com/fabianlim).
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