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	---
	base_model: jondurbin/bagel-dpo-7b-v0.1
	datasets:
	- ai2_arc
	- unalignment/spicy-3.1
	- codeparrot/apps
	- facebook/belebele
	- boolq
	- jondurbin/cinematika-v0.1
	- drop
	- lmsys/lmsys-chat-1m
	- TIGER-Lab/MathInstruct
	- cais/mmlu
	- Muennighoff/natural-instructions
	- openbookqa
	- piqa
	- Vezora/Tested-22k-Python-Alpaca
	- cakiki/rosetta-code
	- Open-Orca/SlimOrca
	- spider
	- squad_v2
	- migtissera/Synthia-v1.3
	- datasets/winogrande
	- nvidia/HelpSteer
	- Intel/orca_dpo_pairs
	- unalignment/toxic-dpo-v0.1
	- jondurbin/truthy-dpo-v0.1
	- allenai/ultrafeedback_binarized_cleaned
	inference: false
	license: apache-2.0
	model_creator: Jon Durbin
	model_name: Bagel DPO 7B v0.1
	model_type: mistral
	prompt_template: 'Below is an instruction that describes a task. Write a response
	that appropriately completes the request.


	### Instruction:

	{prompt}


	### Response:

	'
	quantized_by: TheBloke
	---
	<!-- markdownlint-disable MD041 -->

	<!-- header start -->
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	<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
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	<!-- header end -->

	# Bagel DPO 7B v0.1 - GGUF
	- Model creator: [Jon Durbin](https://huggingface.co/jondurbin)
	- Original model: [Bagel DPO 7B v0.1](https://huggingface.co/jondurbin/bagel-dpo-7b-v0.1)

	<!-- description start -->
	## Description

	This repo contains GGUF format model files for [Jon Durbin's Bagel DPO 7B v0.1](https://huggingface.co/jondurbin/bagel-dpo-7b-v0.1).

	These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).

	<!-- description end -->
	<!-- README_GGUF.md-about-gguf start -->
	### About GGUF

	GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.

	Here is an incomplete list of clients and libraries that are known to support GGUF:

	* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
	* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
	* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
	* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
	* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
	* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
	* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
	* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
	* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
	* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.

	<!-- README_GGUF.md-about-gguf end -->
	<!-- repositories-available start -->
	## Repositories available

	* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/bagel-dpo-7B-v0.1-AWQ)
	* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/bagel-dpo-7B-v0.1-GPTQ)
	* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/bagel-dpo-7B-v0.1-GGUF)
	* [Jon Durbin's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/jondurbin/bagel-dpo-7b-v0.1)
	<!-- repositories-available end -->

	<!-- prompt-template start -->
	## Prompt template: Alpaca

	```
	Below is an instruction that describes a task. Write a response that appropriately completes the request.

	### Instruction:
	{prompt}

	### Response:

	```

	<!-- prompt-template end -->


	<!-- compatibility_gguf start -->
	## Compatibility

	These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)

	They are also compatible with many third party UIs and libraries - please see the list at the top of this README.

	## Explanation of quantisation methods

	<details>
	<summary>Click to see details</summary>

	The new methods available are:

	* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
	* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
	* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
	* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
	* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw

	Refer to the Provided Files table below to see what files use which methods, and how.
	</details>
	<!-- compatibility_gguf end -->

	<!-- README_GGUF.md-provided-files start -->
	## Provided files

	\| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \|
	\| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \|
	\| [bagel-dpo-7b-v0.1.Q2_K.gguf](https://huggingface.co/TheBloke/bagel-dpo-7B-v0.1-GGUF/blob/main/bagel-dpo-7b-v0.1.Q2_K.gguf) \| Q2_K \| 2 \| 3.08 GB\| 5.58 GB \| smallest, significant quality loss - not recommended for most purposes \|
	\| [bagel-dpo-7b-v0.1.Q3_K_S.gguf](https://huggingface.co/TheBloke/bagel-dpo-7B-v0.1-GGUF/blob/main/bagel-dpo-7b-v0.1.Q3_K_S.gguf) \| Q3_K_S \| 3 \| 3.16 GB\| 5.66 GB \| very small, high quality loss \|
	\| [bagel-dpo-7b-v0.1.Q3_K_M.gguf](https://huggingface.co/TheBloke/bagel-dpo-7B-v0.1-GGUF/blob/main/bagel-dpo-7b-v0.1.Q3_K_M.gguf) \| Q3_K_M \| 3 \| 3.52 GB\| 6.02 GB \| very small, high quality loss \|
	\| [bagel-dpo-7b-v0.1.Q3_K_L.gguf](https://huggingface.co/TheBloke/bagel-dpo-7B-v0.1-GGUF/blob/main/bagel-dpo-7b-v0.1.Q3_K_L.gguf) \| Q3_K_L \| 3 \| 3.82 GB\| 6.32 GB \| small, substantial quality loss \|
	\| [bagel-dpo-7b-v0.1.Q4_0.gguf](https://huggingface.co/TheBloke/bagel-dpo-7B-v0.1-GGUF/blob/main/bagel-dpo-7b-v0.1.Q4_0.gguf) \| Q4_0 \| 4 \| 4.11 GB\| 6.61 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \|
	\| [bagel-dpo-7b-v0.1.Q4_K_S.gguf](https://huggingface.co/TheBloke/bagel-dpo-7B-v0.1-GGUF/blob/main/bagel-dpo-7b-v0.1.Q4_K_S.gguf) \| Q4_K_S \| 4 \| 4.14 GB\| 6.64 GB \| small, greater quality loss \|
	\| [bagel-dpo-7b-v0.1.Q4_K_M.gguf](https://huggingface.co/TheBloke/bagel-dpo-7B-v0.1-GGUF/blob/main/bagel-dpo-7b-v0.1.Q4_K_M.gguf) \| Q4_K_M \| 4 \| 4.37 GB\| 6.87 GB \| medium, balanced quality - recommended \|
	\| [bagel-dpo-7b-v0.1.Q5_0.gguf](https://huggingface.co/TheBloke/bagel-dpo-7B-v0.1-GGUF/blob/main/bagel-dpo-7b-v0.1.Q5_0.gguf) \| Q5_0 \| 5 \| 5.00 GB\| 7.50 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \|
	\| [bagel-dpo-7b-v0.1.Q5_K_S.gguf](https://huggingface.co/TheBloke/bagel-dpo-7B-v0.1-GGUF/blob/main/bagel-dpo-7b-v0.1.Q5_K_S.gguf) \| Q5_K_S \| 5 \| 5.00 GB\| 7.50 GB \| large, low quality loss - recommended \|
	\| [bagel-dpo-7b-v0.1.Q5_K_M.gguf](https://huggingface.co/TheBloke/bagel-dpo-7B-v0.1-GGUF/blob/main/bagel-dpo-7b-v0.1.Q5_K_M.gguf) \| Q5_K_M \| 5 \| 5.13 GB\| 7.63 GB \| large, very low quality loss - recommended \|
	\| [bagel-dpo-7b-v0.1.Q6_K.gguf](https://huggingface.co/TheBloke/bagel-dpo-7B-v0.1-GGUF/blob/main/bagel-dpo-7b-v0.1.Q6_K.gguf) \| Q6_K \| 6 \| 5.94 GB\| 8.44 GB \| very large, extremely low quality loss \|
	\| [bagel-dpo-7b-v0.1.Q8_0.gguf](https://huggingface.co/TheBloke/bagel-dpo-7B-v0.1-GGUF/blob/main/bagel-dpo-7b-v0.1.Q8_0.gguf) \| Q8_0 \| 8 \| 7.70 GB\| 10.20 GB \| very large, extremely low quality loss - not recommended \|

	Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.



	<!-- README_GGUF.md-provided-files end -->

	<!-- README_GGUF.md-how-to-download start -->
	## How to download GGUF files

	Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.

	The following clients/libraries will automatically download models for you, providing a list of available models to choose from:

	* LM Studio
	* LoLLMS Web UI
	* Faraday.dev

	### In `text-generation-webui`

	Under Download Model, you can enter the model repo: TheBloke/bagel-dpo-7B-v0.1-GGUF and below it, a specific filename to download, such as: bagel-dpo-7b-v0.1.Q4_K_M.gguf.

	Then click Download.

	### On the command line, including multiple files at once

	I recommend using the `huggingface-hub` Python library:

	```shell
	pip3 install huggingface-hub
	```

	Then you can download any individual model file to the current directory, at high speed, with a command like this:

	```shell
	huggingface-cli download TheBloke/bagel-dpo-7B-v0.1-GGUF bagel-dpo-7b-v0.1.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
	```

	<details>
	<summary>More advanced huggingface-cli download usage (click to read)</summary>

	You can also download multiple files at once with a pattern:

	```shell
	huggingface-cli download TheBloke/bagel-dpo-7B-v0.1-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf'
	```

	For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).

	To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:

	```shell
	pip3 install hf_transfer
	```

	And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:

	```shell
	HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/bagel-dpo-7B-v0.1-GGUF bagel-dpo-7b-v0.1.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
	```

	Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
	</details>
	<!-- README_GGUF.md-how-to-download end -->

	<!-- README_GGUF.md-how-to-run start -->
	## Example `llama.cpp` command

	Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.

	```shell
	./main -ngl 35 -m bagel-dpo-7b-v0.1.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:"
	```

	Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.

	Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.

	If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`

	For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)

	## How to run in `text-generation-webui`

	Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).

	## How to run from Python code

	You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.

	### How to load this model in Python code, using llama-cpp-python

	For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).

	#### First install the package

	Run one of the following commands, according to your system:

	```shell
	# Base ctransformers with no GPU acceleration
	pip install llama-cpp-python
	# With NVidia CUDA acceleration
	CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
	# Or with OpenBLAS acceleration
	CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
	# Or with CLBLast acceleration
	CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
	# Or with AMD ROCm GPU acceleration (Linux only)
	CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
	# Or with Metal GPU acceleration for macOS systems only
	CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python

	# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
	$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
	pip install llama-cpp-python
	```

	#### Simple llama-cpp-python example code

	```python
	from llama_cpp import Llama

	# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
	llm = Llama(
	model_path="./bagel-dpo-7b-v0.1.Q4_K_M.gguf", # Download the model file first
	n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
	n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
	n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
	)

	# Simple inference example
	output = llm(
	"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:", # Prompt
	max_tokens=512, # Generate up to 512 tokens
	stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
	echo=True # Whether to echo the prompt
	)

	# Chat Completion API

	llm = Llama(model_path="./bagel-dpo-7b-v0.1.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
	llm.create_chat_completion(
	messages = [
	{"role": "system", "content": "You are a story writing assistant."},
	{
	"role": "user",
	"content": "Write a story about llamas."
	}
	]
	)
	```

	## How to use with LangChain

	Here are guides on using llama-cpp-python and ctransformers with LangChain:

	* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
	* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)

	<!-- README_GGUF.md-how-to-run end -->

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	## Thanks, and how to contribute

	Thanks to the [chirper.ai](https://chirper.ai) team!

	Thanks to Clay from [gpus.llm-utils.org](llm-utils)!

	I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.

	If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.

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	Special thanks to: Aemon Algiz.

	Patreon special mentions: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros


	Thank you to all my generous patrons and donaters!

	And thank you again to a16z for their generous grant.

	<!-- footer end -->

	<!-- original-model-card start -->
	# Original model card: Jon Durbin's Bagel DPO 7B v0.1


	# A bagel, with everything

	![bagel](bagel.png)

	## Overview

	This is the DPO'd version of https://huggingface.co/jondurbin/bagel-7b-v0.1

	If you are getting too many AALLM or other refusals, even with explicitly human system prompts, you may want to try the non-DPO version.

	## Benchmarks

	I ran these against the latest main branch of lm-evaluation-harness (and opencompass/FastChat for agieval and mt-bench), since batch size/etc effects score for some benchmarks.

	\| model \| arc_challenge \| boolq \| gsm8k \| hellaswag \| mmlu \| openbookqa \| piqa \| truthful_qa \| winogrande \|
	\| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \|
	\| bagel \| __0.6715__ \| 0.8813 \| __0.5618__ \| 0.8397 \| __0.6408__ \| __0.51__ \| __0.8406__ \| __0.6275__ \| __0.7561__ \|
	\| openhermes-2.5 \| 0.6476 \| __0.8835__ \| 0.4852 \| __0.8414__ \| 0.6347 \| 0.498 \| 0.8400 \| 0.5295 \| 0.7443 \|


	MT-Bench:
	```
	########## First turn ##########
	score
	model turn
	bagel-7b-v0.1 1 7.60625

	########## Second turn ##########
	score
	model turn
	bagel-7b-v0.1 2 7.00625

	########## Average ##########
	score
	model
	bagel-7b-v0.1 7.30625
	```

	## Data selection.

	The first step in the process is creating a dataset.
	In this case, we're actually creating a composite dataset, consisting of both supervised fine-tuning data (SFT) and direct preference optimization (DPO) data.

	All instruction data, that is, data that is not plain text (like project Gutenberg and items from Cinematika) or DPO, is converted into ShareGPT format so it's easier to work with.

	See the corresponding code in `bagel/data_sources/*.py` for full implementation for each data source.

	Deduplication is done by creating a uuid v5 of the instruction/text, then only adding items not previously seen (where datasets are loaded in order of the confidence score I assign them).
	This means that if an instruction is in data source "Foo" with confidence 4 as well as in data source "Bar" with confidence score 2, only the entry from "Foo" will be taken.

	### SFT data sources

	Yes, you will see benchmark names in the list, but this only uses the train splits, and a decontamination by cosine similarity is performed at the end as a sanity check

	- [ai2_arc](https://huggingface.co/datasets/ai2_arc)
	- Abstraction and reasoning dataset, useful in measuring "intelligence" to a certain extent.
	- [airoboros](https://huggingface.co/datasets/unalignment/spicy-3.1)
	- Variety of categories of synthetic instructions generated by gpt-4.
	- [apps](https://huggingface.co/datasets/codeparrot/apps)
	- Python coding dataset with 10k problems.
	- [belebele](https://huggingface.co/datasets/facebook/belebele)
	- Multi-lingual reading comprehension dataset.
	- [boolq](https://huggingface.co/datasets/boolq)
	- Corpus of yes/no questions (which can be surprisingly difficult for AI to answer apparently?)
	- [cinematika](https://huggingface.co/datasets/jondurbin/cinematika-v0.1) (instruction and plain text)
	- RP-style data synthesized from movie scripts so the model isn't quite as boring as it otherwise would be.
	- [drop](https://huggingface.co/datasets/drop)
	- More reading comprehension.
	- [gutenberg](https://www.gutenberg.org/) (plain text)
	- Books/plain text, again to make the model less boring, only a handful of examples supported by [chapterize](https://github.com/JonathanReeve/chapterize)
	- [lmsys_chat_1m](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) (only gpt-4 items, also used for DPO)
	- Chats collected by the lmsys chat arena, containing a wide variety of chats with various models.
	- [mathinstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct)
	- Composite dataset with a variety of math-related tasks and problem/question formats.
	- [mmlu](https://huggingface.co/datasets/cais/mmlu)
	- Massive Multitask Language Understanding - a wide variety of questions about various subject matters.
	- [natural_instructions](https://huggingface.co/datasets/Muennighoff/natural-instructions)
	- Millions of instructions from 1600+ task categories (sampled down substantially, stratified by task type)
	- [openbookqa](https://huggingface.co/datasets/openbookqa)
	- Question answering dataset.
	- [piqa](https://huggingface.co/datasets/piqa)
	- Phyiscal interaction question answering.
	- [python_alpaca](https://huggingface.co/datasets/Vezora/Tested-22k-Python-Alpaca)
	- Python instruction response pairs, validated as functional.
	- [rosetta_code](https://huggingface.co/datasets/cakiki/rosetta-code)
	- Code problems and solutions in a variety of programming languages taken from rosettacode.org.
	- [slimorca](https://huggingface.co/datasets/Open-Orca/SlimOrca)
	- Collection of ~500k gpt-4 verified chats from OpenOrca.
	- [spider](https://huggingface.co/datasets/spider)
	- SQL-targeted dataset.
	- [squad_v2](https://huggingface.co/datasets/squad_v2)
	- Contextual question answering (RAG).
	- [synthia](https://huggingface.co/datasets/migtissera/Synthia-v1.3)
	- GPT-4 generated data using advanced prompting from Migel Tissera.
	- [winogrande](https://huggingface.co/datasets/winogrande)
	- Fill in the blank style prompts.

	### DPO data sources
	- [airoboros 3.1](https://huggingface.co/datasets/unalignment/spicy-3.1) vs [airoboros 2.2.1](https://huggingface.co/datasets/jondurbin/airoboros-gpt4-1.4.1)
	- The creative/writing tasks from airoboros-2.2.1 were re-generated using gpt4-0314 and a custom prompt to get longer, more creative, less clichè responses for airoboros 3.1, so we can use the shorter/boring version as the "rejected" value and the rerolled response as "chosen"
	- [helpsteer](https://huggingface.co/datasets/nvidia/HelpSteer)
	- Really neat dataset provided by the folks at NVidia with human annotation across a variety of metrics. Only items with the highest "correctness" value were used for DPO here, with the highest scoring output as "chosen" and random lower scoring value as "rejected"
	- [orca_dpo_pairs](https://huggingface.co/datasets/Intel/orca_dpo_pairs)
	- Another interesting dataset by Intel, which provides various DPO pairs generated from prompts included in the SlimOrca dataset.
	- [toxic-dpo](https://huggingface.co/datasets/unalignment/toxic-dpo-v0.1)
	- __highly toxic and potentially illegal content!__ De-censorship, for academic and lawful purposes only, of course. Generated by llama-2-70b via prompt engineering.
	- [truthy](https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1)
	- DPO pairs meant to increase truthfulness of the model, e.g. common misconceptions, differentiate between AI assistants and roleplayed human in terms of corporeal awareness/locality/etc.
	- [ultrafeedback](https://huggingface.co/datasets/allenai/ultrafeedback_binarized_cleaned)
	- One of the bits of magic behind the Zephyr model. Only the items with a chosen score of 8 or higher were included.

	Only the train splits were used (if a split was provided), and an additional pass of decontamination is performed using approximate nearest neighbor search (via faiss).

	### Total dataset size

	The deduplicated and decontamined list of instructions contains 1,671,822 items:

	- 1,602,217 SFT/instructions
	- 59,247 DPO pairs
	- 1606 with both SFT and DPO data

	Keep in mind, this number becomes 4x larger when applying the various prompt formats.

	## Prompt formatting

	In sticking with the theme of the bagel, I didn't want to use a single prompt format, so I used 4 - vicuna, llama-2, alpaca, and chat-ml (sorta).
	I also didn't want to randomly select a single prompt format for each item (hoping each instruction would generalize more when used in a variety of prompt formats), so each instruction is actually converted into every prompt format.

	This means each epoch of our fine-tune is really basically 4 epochs. So, for the fine-tunes, I would recommend only doing 1 epoch (or 0.75 epochs). I am testing with a single epoch using a relatively low learning rate.

	### Alpaca (sort of)

	```
	Below is an instruction that describes a task. Write a response that appropriately completes the request.

	### Instruction:
	{system prompt, if provided}
	{instruction}

	### Response:
	```

	The main difference here is that because of the dataset formatting and variety of data sources, it would have been much to tedious to add an `### Input:` block, so the inputs are just in the instruction section.

	### Vicuna

	```
	{system prompt, if provided, randomly defaulting to "A chat between a user and an unbiased, uncensored assistant."}
	USER: {instruction}
	ASSISTANT:
	```

	### ChatML (sort of)

	I don't really understand the point of having special tokens for `<\|im_start\|>` and `<\|im_end\|>`, because in practice they just act as BOS and EOS tokens (but, please correct me if I'm wrong).

	So, instead of:
	```text
	{bos}<\|im_start\|>{role}
	{text}
	<\|im_end\|>{eos}
	```

	I just changed it to:
	```text
	{bos}{role}
	{text}
	{eos}
	```

	In practice, this would mean tokenization code like such:
	```python
	tokenizer = AutoTokenizer.from_pretrained('mistralai/mistral-7b-v0.1')

	input_str = f"""system
	You are a goat.
	{tokenizer.eos_token}
	{tokenizer.bos_token}user
	Tell me how to fry an egg.
	{tokenizer.eos_token}
	{tokenizer.bos_token}assistant
	"""

	inputs = tokenizer(input_str, return_tensors="pt")
	```

	If you really want to use `<\|im_start\|>` and `<\|im_end\|>`, just update your `tokenizer_config.json` to use `<\|im_start\|>` instead of `<s>` and `<\|im_end\|>` instead of `</s>` and when tokenizing. And if you still don't like what I've done to this chat-ml-ish format, feel free to cry into your pillow or fork the code and do a new fine-tune.

	### Llama-2 chat

	```
	[INST] <<SYS>>
	{system}
	<</SYS>>

	{instruction} [/INST]
	```

	## Fine tuning

	### SFT phase

	An example for mistral-7b:

	Note: I actually used my fork of [qlora](https://github.com/jondurbin/qlora)'s `train.py` for this, but I'm porting it to a minified version here, not tested yet!

	More notes: I stopped the SFT phase around 50% because of budget constraints.

	```bash
	export BASE_DIR=/workspace
	export WANDB_API_KEY=[redacted]
	export WANDB_PROJECT=bagel-7b-v0.1

	# Run the pretraining.
	accelerate launch bagel/tune/sft.py \
	--model_name_or_path $BASE_DIR/mistral-7b \
	--final_output_dir $BASE_DIR/$WANDB_PROJECT \
	--output_dir $BASE_DIR/$WANDB_PROJECT-workdir \
	--num_train_epochs 1 \
	--logging_steps 1 \
	--save_strategy steps \
	--save_steps 200 \
	--save_total_limit 5 \
	--data_seed 42 \
	--evaluation_strategy steps \
	--eval_dataset_size 0.0006 \
	--eval_steps 200 \
	--max_new_tokens 4096 \
	--dataloader_num_workers 3 \
	--logging_strategy steps \
	--remove_unused_columns False \
	--do_train \
	--full_finetune \
	--bf16 \
	--bits 16 \
	--optim adamw_torch \
	--lr_scheduler_type linear \
	--dataset $BASE_DIR/bagel/bagel-input-output-v0.1.parquet \
	--dataset_format input-output \
	--model_max_len 4096 \
	--per_device_train_batch_size 8 \
	--learning_rate 3.5e-7 \
	--warmup_ratio 0.005 \
	--adam_beta2 0.999 \
	--max_grad_norm 0.3 \
	--weight_decay 0.001 \
	--seed 42 \
	--report_to wandb \
	--gradient_checkpointing True \
	--gradient_accumulation_steps 4 \
	--skip_excess_length False \
	--ddp_find_unused_parameters False \
	--use_flash_attention_2 \
	--deepspeed deepspeed.json
	```

	Deepspeed configuration:
	```json
	{
	"gradient_accumulation_steps": "auto",
	"gradient_clipping": "auto",
	"train_batch_size": "auto",
	"train_micro_batch_size_per_gpu": "auto",
	"bf16": {
	"enabled": true
	},
	"zero_optimization": {
	"stage": 2,
	"contiguous_gradients": true,
	"overlap_comm": true,
	"reduce_scatter": true,
	"reduce_bucket_size": 5e8,
	"allgather_bucket_size": 5e8
	}
	}
	```

	### DPO phase

	An example of the DPO phase for mistral-7b (requires first running the SFT):

	```bash
	export BASE_DIR=/mnt/data
	export WANDB_API_KEY=[redacted]
	export WANDB_PROJECT=bagel-dpo-7b-v0.1

	accelerate launch bagel/tune/dpo.py \
	--model_name_or_path bagel-7b-v0.1 \
	--learning_rate 3e-7 \
	--per_device_train_batch_size 2 \
	--gradient_accumulation_steps 4 \
	--max_length 4096 \
	--max_prompt_length 1024 \
	--max_target_length 3092 \
	--num_train_epochs 3 \
	--report_to wandb \
	--gradient_checkpointing true \
	--use_flash_attention_2 true \
	--dataset $BASE_DIR/bagel/bagel-dpo-v0.1.parquet \
	--eval_steps 5 \
	--eval_dataset_size 0.03 \
	--workdir $BASE_DIR/$WANDB_PROJECT-workdir \
	--output_dir $BASE_DIR/$WANDB_PROJECT \
	--deepspeed deepspeed.json \
	--save_steps 25 \
	--save_total_limit 5
	```

	<!-- original-model-card end -->