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transformers/docs/source/en/model_doc/dbrx.md
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<!--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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-->
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*This model was released on 2024-03-27 and added to Hugging Face Transformers on 2024-04-18.*
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# DBRX
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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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DBRX is a [transformer-based](https://www.isattentionallyouneed.com/) decoder-only large language model (LLM) that was trained using next-token prediction.
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It uses a *fine-grained* mixture-of-experts (MoE) architecture with 132B total parameters of which 36B parameters are active on any input.
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It was pre-trained on 12T tokens of text and code data.
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Compared to other open MoE models like Mixtral-8x7B and Grok-1, DBRX is fine-grained, meaning it uses a larger number of smaller experts. DBRX has 16 experts and chooses 4, while Mixtral-8x7B and Grok-1 have 8 experts and choose 2.
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This provides 65x more possible combinations of experts and we found that this improves model quality.
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DBRX uses rotary position encodings (RoPE), gated linear units (GLU), and grouped query attention (GQA).
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It is a BPE based model and uses the GPT-4 tokenizer as described in the [tiktoken](https://github.com/openai/tiktoken) repository.
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We made these choices based on exhaustive evaluation and scaling experiments.
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DBRX was pretrained on 12T tokens of carefully curated data and a maximum context length of 32K tokens.
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We estimate that this data is at least 2x better token-for-token than the data we used to pretrain the MPT family of models.
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This new dataset was developed using the full suite of Databricks tools, including Apache Spark™ and Databricks notebooks for data processing, and Unity Catalog for data management and governance.
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We used curriculum learning for pretraining, changing the data mix during training in ways we found to substantially improve model quality.
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More detailed information about DBRX Instruct and DBRX Base can be found in our [technical blog post](https://www.databricks.com/blog/introducing-dbrx-new-state-art-open-llm).
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This model was contributed by [eitan-turok](https://huggingface.co/eitanturok) and [abhi-db](https://huggingface.co/abhi-db). The original code can be found [here](https://github.com/databricks/dbrx-instruct), though this may not be up to date.
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## Usage Examples
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The `generate()` method can be used to generate text using DBRX. You can generate using the standard attention implementation, flash-attention, and the PyTorch scaled dot product attention. The last two attention implementations give speed ups.
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```python
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from transformers import DbrxForCausalLM, AutoTokenizer
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import torch
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tokenizer = AutoTokenizer.from_pretrained("databricks/dbrx-instruct", token="YOUR_HF_TOKEN")
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model = DbrxForCausalLM.from_pretrained(
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"databricks/dbrx-instruct",
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device_map="auto",
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dtype=torch.bfloat16,
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token="YOUR_HF_TOKEN",
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)
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input_text = "What does it take to build a great LLM?"
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messages = [{"role": "user", "content": input_text}]
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input_ids = tokenizer.apply_chat_template(messages, return_dict=True, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)
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outputs = model.generate(**input_ids, max_new_tokens=200)
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print(tokenizer.decode(outputs[0]))
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```
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If you have flash-attention installed (`pip install flash-attn`), it is possible to generate faster. (The HuggingFace documentation for flash-attention can be found [here](https://huggingface.co/docs/transformers/perf_infer_gpu_one#flashattention-2).)
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```python
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from transformers import DbrxForCausalLM, AutoTokenizer
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import torch
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tokenizer = AutoTokenizer.from_pretrained("databricks/dbrx-instruct", token="YOUR_HF_TOKEN")
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model = DbrxForCausalLM.from_pretrained(
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"databricks/dbrx-instruct",
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device_map="auto",
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dtype=torch.bfloat16,
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token="YOUR_HF_TOKEN",
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attn_implementation="flash_attention_2",
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)
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input_text = "What does it take to build a great LLM?"
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messages = [{"role": "user", "content": input_text}]
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input_ids = tokenizer.apply_chat_template(messages, return_dict=True, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)
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outputs = model.generate(**input_ids, max_new_tokens=200)
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print(tokenizer.decode(outputs[0]))
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```
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You can also generate faster using the PyTorch scaled dot product attention. (The HuggingFace documentation for scaled dot product attention can be found [here](https://huggingface.co/docs/transformers/perf_infer_gpu_one#pytorch-scaled-dot-product-attention).)
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```python
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from transformers import DbrxForCausalLM, AutoTokenizer
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import torch
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tokenizer = AutoTokenizer.from_pretrained("databricks/dbrx-instruct", token="YOUR_HF_TOKEN")
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model = DbrxForCausalLM.from_pretrained(
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"databricks/dbrx-instruct",
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device_map="auto",
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dtype=torch.bfloat16,
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token="YOUR_HF_TOKEN",
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attn_implementation="sdpa",
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)
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input_text = "What does it take to build a great LLM?"
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messages = [{"role": "user", "content": input_text}]
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input_ids = tokenizer.apply_chat_template(messages, return_dict=True, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)
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outputs = model.generate(**input_ids, max_new_tokens=200)
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print(tokenizer.decode(outputs[0]))
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```
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## DbrxConfig
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[[autodoc]] DbrxConfig
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## DbrxModel
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[[autodoc]] DbrxModel
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- forward
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## DbrxForCausalLM
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[[autodoc]] DbrxForCausalLM
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- forward
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