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bagel-34b-v0.2-3.0bpw-h6-exl2

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metadata
license: other
license_name: yi-license
license_link: https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE
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
  - Squish42/bluemoon-fandom-1-1-rp-cleaned
  - LDJnr/Capybara
  - JULIELab/EmoBank
  - kingbri/PIPPA-shareGPT

A bagel, with everything (except DPO)

bagel

Overview

An experimental fine-tune of yi-34b-200k using bagel

This is the model after the SFT phase, before DPO has been applied. You'll likely want to use the DPO'd version, rather than this one, but since I had it, I uploaded it.

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]