A bagel, with everything (except DPO)

Overview

This model is basically unusable; sadly tinyllama is not a useful base model

An experimental fine-tune of tinyllama using bagel

This is the model after the SFT phase, before DPO has been applied.

Hardware kindly provided by Massed Compute

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
  • Abstraction and reasoning dataset, useful in measuring "intelligence" to a certain extent.
• airoboros
  • Variety of categories of synthetic instructions generated by gpt-4.
• apps
  • Python coding dataset with 10k problems.
• belebele
  • Multi-lingual reading comprehension dataset.
• bluemoon
  • Roleplay data scraped from Bluemoon, then cleaned and formatted as ShareGPT.
• boolq
  • Corpus of yes/no questions (which can be surprisingly difficult for AI to answer apparently?)
• capybara
  • Multi-turn dataset used to create the capybara models.
• cinematika (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
  • More reading comprehension.
• emobank
  • Emotion annotations using the Valence-Arousal-Domninance scheme.
• gutenberg (plain text)
  • Books/plain text, again to make the model less boring, only a handful of examples supported by chapterize
• 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
  • Composite dataset with a variety of math-related tasks and problem/question formats.
• mmlu
  • Massive Multitask Language Understanding - a wide variety of questions about various subject matters.
• natural_instructions
  • Millions of instructions from 1600+ task categories (sampled down substantially, stratified by task type)
• openbookqa
  • Question answering dataset.
• pippa
  • Deduped version of PIPPA in ShareGPT format.
• piqa
  • Phyiscal interaction question answering.
• python_alpaca
  • Python instruction response pairs, validated as functional.
• rosetta_code
  • Code problems and solutions in a variety of programming languages taken from rosettacode.org.
• slimorca
  • Collection of ~500k gpt-4 verified chats from OpenOrca.
• spider
  • SQL-targeted dataset.
• squad_v2
  • Contextual question answering (RAG).
• synthia
  • GPT-4 generated data using advanced prompting from Migel Tissera.
• winogrande
  • Fill in the blank style prompts.

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).

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:

{bos}<|im_start|>{role}
{text}
<|im_end|>{eos}

I just changed it to:

{bos}{role}
{text}
{eos}

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]

Default via chat template

The model's tokenizer_config.json includes the default chat template (llama-2), so you can simply use the apply_chat_template method to build the full prompt.

import transformers
tokenizer = transformers.AutoTokenizer.from_pretrained('jondurbin/bagel-8x7b-v0.2')
chat = [
  {"role": "system", "content": "You are Bob, a friendly AI assistant."},
  {"role": "user", "content": "Hello, how are you?"},
  {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
  {"role": "user", "content": "I'd like to show off how chat templating works!"},
]
print(tokenizer.apply_chat_template(chat, tokenize=False))

Contribute

If you're interested in new functionality/datasets, take a look at bagel repo and either make a PR or open an issue with details.

To help me with the fine-tuning costs (which are extremely expensive for these large combined datasets):

• https://bmc.link/jondurbin
• ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11
• BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf

Licence and usage restrictions

The base model is tinyllama, which is licensed as apache-2.0 - no issues there.

The fine-tuning data, however, includes several datasets that have data generated at least in part by OpenAI's gpt-4.

I am not a lawyer, so I can't help determine if this is actually commercially viable, but some questions that often come up are:

• Does the OpenAI ToS apply only to the user who created the dataset initially, and not subsequent models?
• If the dataset was released under a permissive license, but actually includes OpenAI generated data, does that ToS supersede the license?
• Does the dataset fall completely under fair use anyways, since the model isn't really capable of reproducing the entire training set verbatim?

Use your best judgement and seek legal advice if you are concerned about the terms. In any case, by using this model, you agree to completely indemnify me.