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README.md

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+## Use At your own Risk!
+
+4bit gptq marlin version of dbrx-base-converted-v2
+
+Run:
+1. Use PR https://github.com/AutoGPTQ/AutoGPTQ/pull/625
+2. Need ~68GB of VRAM (1xA100 80G will do)
+---
+inference: false
+license: other
+license_name: databricks-open-model-license
+license_link: https://www.databricks.com/legal/open-model-license
+---
+
+# DBRX Base
+
+* DBRX Base is a mixture-of-experts (MoE) large language model trained from scratch by Databricks.
+* We are releasing both DBRX Base, a pretrained base model, and DBRX Instruct, a fine-tuned version for few-turn interactions, under [an open license](https://www.databricks.com/legal/open-model-license).
+* This is the repository for DBRX Base. DBRX Instruct can be found [here](https://huggingface.co/databricks/dbrx-instruct).
+* For full details on the DBRX models, please read our [technical blog post](https://www.databricks.com/blog/introducing-dbrx-new-state-art-open-llm).
+
+
+## Model Overview
+DBRX is a [transformer-based](https://www.isattentionallyouneed.com/) decoder-only large language model (LLM) that was trained using next-token prediction. 
+It uses a *fine-grained* mixture-of-experts (MoE) architecture with 132B total parameters of which 36B parameters are active on any input. 
+It was pre-trained on 12T tokens of text and code data. 
+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. 
+This provides 65x more possible combinations of experts and we found that this improves model quality. 
+DBRX uses rotary position encodings (RoPE), gated linear units (GLU), and grouped query attention (GQA). 
+It uses the GPT-4 tokenizer as provided in the [tiktoken](https://github.com/openai/tiktoken) repository. 
+We made these choices based on exhaustive evaluation and scaling experiments.
+
+DBRX was pretrained on 12T tokens of carefully curated data and a maximum context length of 32K tokens. 
+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. 
+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. 
+We used curriculum learning for pretraining, changing the data mix during training in ways we found to substantially improve model quality.
+
+* **Inputs:** DBRX only accepts text-based inputs and accepts a context length of up to 32768 tokens.
+* **Outputs:** DBRX only produces text-based outputs.  
+* **Model Architecture:** 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).
+* **License:** [Databricks Open Model License](https://www.databricks.com/legal/open-model-license)
+* **Acceptable Use Policy:** [Databricks Open Model Acceptable Use Policy](https://www.databricks.com/legal/acceptable-use-policy-open-model)
+* **Version:** 1.0
+* **Owner:** Databricks, Inc.
+
+
+## Usage
+These are several general ways to use the DBRX models: 
+* DBRX Base and DBRX Instruct are available for download on HuggingFace (see our Quickstart guide below). This is the HF repository for DBRX Base; DBRX Instruct can be found [here](https://huggingface.co/databricks/dbrx-instruct). 
+* The DBRX model repository can be found on GitHub [here](https://github.com/databricks/dbrx). 
+* DBRX Base and DBRX Instruct are available with [Databricks Foundation Model APIs](https://docs.databricks.com/en/machine-learning/foundation-models/index.html) via both *Pay-per-token* and *Provisioned Throughput* endpoints. These are enterprise-ready deployments.
+* For more information on how to fine-tune using LLM-Foundry, please take a look at our LLM pretraining and fine-tuning [documentation](https://github.com/mosaicml/llm-foundry/blob/main/scripts/train/README.md).
+
+
+## Quickstart Guide
+**NOTE: This is DBRX Base, and has not been instruction finetuned. It has not been trained for interactive chat and is only a completion model.**
+If you are looking for the finetuned model, please use [DBRX Instruct](https://huggingface.co/databricks/dbrx-instruct).
+
+Getting started with DBRX models is easy with the `transformers` library. The model requires ~264GB of RAM and the following packages:
+
+```bash
+pip install "transformers>=4.39.2" "tiktoken>=0.6.0"
+```
+
+If you'd like to speed up download time, you can use the `hf_transfer` package as described by Huggingface [here](https://huggingface.co/docs/huggingface_hub/en/guides/download#faster-downloads).
+```bash
+pip install hf_transfer
+export HF_HUB_ENABLE_HF_TRANSFER=1
+```
+
+You will need to request access to this repository to download the model. Once this is granted, 
+[obtain an access token](https://huggingface.co/docs/hub/en/security-tokens) with `read` permission, and supply the token below.
+
+### Run the model on a CPU:
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("databricks/dbrx-base", trust_remote_code=True, token="hf_YOUR_TOKEN")
+model = AutoModelForCausalLM.from_pretrained("databricks/dbrx-base", device_map="cpu", torch_dtype=torch.bfloat16, trust_remote_code=True, token="hf_YOUR_TOKEN")
+
+input_text = "Databricks was founded in "
+input_ids = tokenizer(input_text, return_tensors="pt")
+
+outputs = model.generate(**input_ids, max_new_tokens=100)
+print(tokenizer.decode(outputs[0]))
+```
+
+### Run the model on multiple GPUs:
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("databricks/dbrx-base", trust_remote_code=True, token="hf_YOUR_TOKEN")
+model = AutoModelForCausalLM.from_pretrained("databricks/dbrx-base", device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True, token="hf_YOUR_TOKEN")
+
+input_text = "Databricks was founded in "
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**input_ids, max_new_tokens=100)
+print(tokenizer.decode(outputs[0]))
+```
+If your GPU system supports [FlashAttention2](https://huggingface.co/docs/transformers/perf_infer_gpu_one#flashattention-2), you can add `attn_implementation=”flash_attention_2”` as a keyword to `AutoModelForCausalLM.from_pretrained()` to achieve faster inference.
+
+
+## Limitations and Ethical Considerations
+### Training Dataset Limitations
+The DBRX models were trained on 12T tokens of text, with a knowledge cutoff date of December 2023.
+
+The training mix used for DBRX contains both natural-language and code examples. The vast majority of our training data is in the English language. We did not test DBRX for non-English proficiency. Therefore, DBRX should be considered a generalist model for text-based use in the English language.
+
+DBRX does not have multimodal capabilities.
+
+### Associated Risks and Recommendations 
+All foundation models are novel technologies that carry various risks, and may output information that is inaccurate, incomplete, biased, or offensive. 
+Users should exercise judgment and evaluate such output for accuracy and appropriateness for their desired use case before using or sharing it. 
+Databricks recommends [using retrieval augmented generation (RAG)](https://www.databricks.com/glossary/retrieval-augmented-generation-rag) in scenarios where accuracy and fidelity are important. 
+We also recommend that anyone using or fine-tuning either DBRX Base or DBRX Instruct perform additional testing around safety in the context of their particular application and domain. 
+
+
+## Intended Uses
+### Intended Use Cases
+The DBRX models are open, general-purpose LLMs intended and licensed for both commercial and research applications. 
+They can be further fine-tuned for various domain-specific natural language and coding tasks. 
+DBRX Base can be used as an off-the-shelf model for text completion for general English-language and coding tasks. 
+
+Please review the Associated Risks section above, as well as the [Databricks Open Model License](https://www.databricks.com/legal/open-model-license) and [Databricks Open Model Acceptable Use Policy](https://www.databricks.com/legal/acceptable-use-policy-open-model) for further information about permissible uses of DBRX Base and its derivatives. 
+
+### Out-of-Scope Use Cases
+DBRX models are not intended to be used out-of-the-box in non-English languages and do not support native code execution, or other forms of function-calling. 
+DBRX models should not be used in any manner that violates applicable laws or regulations or in any other way that is prohibited by the [Databricks Open Model License](https://www.databricks.com/legal/open-model-license) and [Databricks Open Model Acceptable Use Policy](https://www.databricks.com/legal/acceptable-use-policy-open-model). 
+
+
+## Training Stack
+MoE models are complicated to train, and the training of DBRX Base and DBRX Instruct was heavily supported by Databricks’ infrastructure for data processing and large-scale LLM training (e.g., [Composer](https://github.com/mosaicml/composer), [Streaming](https://github.com/mosaicml/streaming), [Megablocks](https://github.com/stanford-futuredata/megablocks), and [LLM Foundry](https://github.com/mosaicml/llm-foundry)). 
+
+Composer is our core library for large-scale training. 
+It provides an optimized training loop, easy [checkpointing](https://docs.mosaicml.com/projects/composer/en/latest/trainer/checkpointing.html) and [logging](https://docs.mosaicml.com/projects/composer/en/latest/trainer/logging.html#wood-logging), 
+[FSDP](https://pytorch.org/docs/stable/fsdp.html)-based [model sharding](https://docs.mosaicml.com/projects/composer/en/latest/notes/distributed_training.html#fullyshardeddataparallel-fsdp), 
+convenient [abstractions](https://docs.mosaicml.com/projects/composer/en/latest/trainer/time.html), extreme customizability via [callbacks](https://docs.mosaicml.com/projects/composer/en/latest/trainer/callbacks.html), and more.
+
+Streaming enables fast, low cost, and scalable training on large datasets from cloud storage. It handles a variety of challenges around deterministic resumption as node counts change, avoiding redundant downloads across devices, high-quality shuffling at scale, sample-level random access, and speed.
+
+Megablocks is a lightweight library for MoE training. Crucially, it supports “dropless MoE,” which avoids inefficient padding and is intended to provide deterministic outputs for a given sequence no matter what other sequences are in the batch.
+
+LLM Foundry ties all of these libraries together to create a simple LLM pretraining, fine-tuning, and inference experience.
+
+DBRX was trained using proprietary optimized versions of the above open source libraries, along with our [LLM training platform](https://www.databricks.com/product/machine-learning/mosaic-ai-training). 
+
+
+## Evaluation
+We find that DBRX outperforms established open-source and open-weight base models on the [Databricks Model Gauntlet](https://www.databricks.com/blog/llm-evaluation-for-icl), the [Hugging Face Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard), and HumanEval. 
+The Databricks Model Gauntlet measures performance on more than 30 tasks across six categories: world knowledge, common sense reasoning, language understanding, reading comprehension, symbolic problem solving, and programming. 
+The Hugging Face Open LLM Leaderboard measures the average of ARC-Challenge, HellaSwag, MMLU, TruthfulQA, Winogrande and GSM8k. 
+HumanEval measures coding ability.
+
+Full evaluation details can be found in our [technical blog post](https://www.databricks.com/blog/introducing-dbrx-new-state-art-open-llm). 
+
+
+## Acknowledgements
+The DBRX models were made possible thanks in large part to the open-source community, especially:
+* The [MegaBlocks](https://arxiv.org/abs/2211.15841) library, which established a foundation for our MoE implementation.
+* [PyTorch FSDP](https://arxiv.org/abs/2304.11277), which we built on for distributed training.