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README.md

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 ---
 license: apache-2.0
+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
+- Squish42/bluemoon-fandom-1-1-rp-cleaned
+- LDJnr/Capybara
+- JULIELab/EmoBank
+- kingbri/PIPPA-shareGPT
 ---
+
+# A bagel, with everything (except DPO)
+
+![bagel](bagel.png)
+
+## Overview
+
+An experimental fine-tune of [mamba-2.8b-slimpj](https://hf.co/state-spaces/mamba-2.8b-slimpj) using [bagel](https://github.com/jondurbin/bagel)
+
+Default recommended system prompt:
+```
+You are a helpful, unbiased, uncensored assistant.
+```
+
+Supports several prompt formats, but you can also use `tokenizer.apply_chat_template`
+
+You probably want the [DPO version](https://hf.co/jondurbin/bagel-dpo-2.8b-v0.2) - it's much better.
+
+## Example chat script
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel
+
+device = "cuda"
+tokenizer = AutoTokenizer.from_pretrained("bagel-final-2.8b-v0.2")
+model = MambaLMHeadModel.from_pretrained("bagel-final-2.8b-v0.2", device="cuda", dtype=torch.float32)
+
+messages = [{"role": "system", "content": "You are a helpful, unbiased, uncensored assistant."}]
+while True:
+    user_message = input("[INST] ")
+    messages.append({"role": "user", "content": user_message})
+    input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to("cuda")
+    out = model.generate(input_ids=input_ids, max_length=2000, temperature=0.9, top_p=0.7, eos_token_id=tokenizer.eos_token_id, repetition_penalty=1.07)
+    decoded = tokenizer.batch_decode(out)[0].split("[/INST]")[-1].replace("</s>", "").strip()
+    messages.append({"role": "assistant", "content": decoded})
+    print("[/INST]", decoded)
+```
+
+## 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.
+- [bluemoon](https://huggingface.co/datasets/Squish42/bluemoon-fandom-1-1-rp-cleaned)
+  - Roleplay data scraped from Bluemoon, then cleaned and formatted as ShareGPT.
+- [boolq](https://huggingface.co/datasets/boolq)
+  - Corpus of yes/no questions (which can be surprisingly difficult for AI to answer apparently?)
+- [capybara](https://huggingface.co/datasets/LDJnr/Capybara)
+  - Multi-turn dataset used to create the capybara models.
+- [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.
+- [emobank](https://github.com/JULIELab/EmoBank)
+  - Emotion annotations using the Valence-Arousal-Domninance scheme.
+- [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.
+- [pippa](https://huggingface.co/datasets/kingbri/PIPPA-shareGPT)
+  - Deduped version of [PIPPA](https://huggingface.co/datasets/PygmalionAI/PIPPA) in ShareGPT format.
+- [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.
+
+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:
+```text
+{bos}<|im_start|>{role}
+{text}
+<|im_end|>{eos}
+```
+
+I just changed it to:
+```text
+{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]
+```