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--- |
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license: cc-by-sa-3.0 |
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datasets: |
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- euclaise/TinyCoT |
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- euclaise/reddit-instruct |
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- sablo/oasst2_curated |
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library_name: transformers |
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tags: |
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- supertrainer2000 |
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--- |
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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). |
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**Memphis was trained *only* on human data! No GPT generations here.** |
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Finetuning was performed using my [supertrainer2000](https://github.com/euclaise/supertrainer2000) framework, using my Adalite optimizer. |
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### Training Procedure |
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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) |
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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. |
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I then performed the following steps 3 times: |
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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. |
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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. |
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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). |
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### Hyperparameters |
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For the initial supervised finetuning step: |
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- Adalite optimizer, default hyperparameters of supertrainer2000 unless otherwise specified |
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- Lambda (Adalite's analogue to weight decay) of 0.01 |
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- LR of 1e-5 |
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- MixCE ratio of 0.75 |
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- Sequence length of 4096 |
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- Cosine decay with a 20% warmup |
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- Frozen embeddings |
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- No training on inputs |
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- Accumulated batch size of 128 |
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- NEFTune with an alpha of 10 |
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For the generations: |
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- Generated using the current git version of `vllm` |
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- N=8 |
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- Temperature of 0.5 |
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- `top_p` of 0.8 |
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- Maximum of 512 generated tokens, discarding responses that do not have a valid rationale and answer |
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For the rank finetuning: |
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- Adalite optimizer, default hyperparameters of supertrainer2000 unless otherwise specified |
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- Lambda of 0.01 |
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- LR of 5e-7 |
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- Rank loss weight of 5 |
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- Sequence length of 1024 |
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- Cosine schedule with 10% warmup |
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- Frozen embeddings |
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- No training on inputs |
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- Accumulated batch size of 128 |
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- NEFTune with an alpha of 10 |
