---
license: cc-by-sa-3.0
datasets:
- euclaise/TinyCoT
- euclaise/reddit-instruct
- sablo/oasst2_curated
library_name: transformers
tags:
- supertrainer2000
---


Memphis-CoT is a finetune of [StableLM 3b 4e1t](stabilityai/stablelm-3b-4e1t) on [TinyCoT](https://huggingface.co/datasets/euclaise/TinyCoT), along with [reddit-instruct](https://huggingface.co/datasets/euclaise/reddit-instruct) and a [curated](https://huggingface.co/datasets/sablo/oasst2_curated) subset of [oasst2](https://huggingface.co/datasets/OpenAssistant/oasst2).

**Memphis was trained *only* on human data! No GPT generations here.**

Finetuning was performed using my [supertrainer2000](https://github.com/euclaise/supertrainer2000) framework, using my Adalite optimizer.


### Training Procedure
I finetuned the model using an iterative rationale-bootstrapping procedure inspired by [STaR](https://research.google/pubs/star-self-taught-reasoner-bootstrapping-reasoning-with-reasoning/) and [SPIN](https://arxiv.org/abs/2401.01335)

First, I finetuned the model on all the datasets using a [MixCE](https://arxiv.org/abs/2305.16958) loss and [NEFTune](https://arxiv.org/abs/2310.05914), for 2 epochs.

I then performed the following steps 3 times:
1. Generate responses for each question in TinyCoT using the current model, check each response for correctness, and create a dataset of (correct, incorrect) pairs. Extra values are discarded, such that each correct and incorrect response is unique.
2. Finetune the model for 1 epoch using a ranking loss over length-normalized log-probabilities of each sequence, similar to [Preference Ranking Optimization](https://arxiv.org/abs/2306.17492), comparing the correct vs incorrect generated response. A standard CE loss over the ground-truth was included to prevent excessive drift.

This should be more efficient than either STaR or SPIN, as it uses a ranking loss rather than rejection sampling (unlike STaR), and verifies correctness instead of assuming all model responses are incorrect (unlike SPIN).

### Hyperparameters

For the initial supervised finetuning step:
- Adalite optimizer, default hyperparameters of supertrainer2000 unless otherwise specified
- Lambda (Adalite's analogue to weight decay) of 0.01
- LR of 1e-5
- MixCE ratio of 0.75
- Sequence length of 4096
- Cosine decay with a 20% warmup
- Frozen embeddings
- No training on inputs
- Accumulated batch size of 128
- NEFTune with an alpha of 10

For the generations:
- Generated using the current git version of `vllm`
- N=8
- Temperature of 0.5
- `top_p` of 0.8
- Maximum of 512 generated tokens, discarding responses that do not have a valid rationale and answer

For the rank finetuning:
- Adalite optimizer, default hyperparameters of supertrainer2000 unless otherwise specified
- Lambda of 0.01
- LR of 5e-7
- Rank loss weight of 5
- Sequence length of 1024
- Cosine schedule with 10% warmup
- Frozen embeddings
- No training on inputs
- Accumulated batch size of 128
- NEFTune with an alpha of 10