--- license: cc-by-sa-3.0 library_name: transformers tags: - supertrainer2000 - human-data datasets: - euclaise/TinyCoT - euclaise/reddit-instruct - sablo/oasst2_curated metrics: - accuracy --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64137e2150358a805203cbac/DlTWku8gant1yx6NaxqJX.png) 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) (subset to 5000 examples, excluding posts with brackets in the title) and a [curated](https://huggingface.co/datasets/sablo/oasst2_curated) subset of [oasst2](https://huggingface.co/datasets/OpenAssistant/oasst2). Here is the [GGUF](https://huggingface.co/brittlewis12/Memphis-CoT-3B-GGUF) you could try out. **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 4 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). ## Prompt formats The format for reddit-instruct and oasst2 was: ``` ### User: [insert instruction here] ### Assistant: [insert response here] ### User: ... ``` The format for TinyCoT was: ``` ### User: [insert instruction here] ### Rationale: [insert reasoning here] ### Answer: [insert direct answer here] ``` ## Benchmarks | Model | Size | Data | Method | GSM8K (5-shot) | AGIEval (English/Nous subset, acc_norm) | BIG Bench Hard (CoT, few-shot*) | |:-----------------------------------------------------------------------|--------|:--------------------|---------------|:---------------|:----------------------------------------|:------------------------------ | | [StableLM 3B Base](https://hf.co/stabilityai/stablelm-3b-4e1t) | 3B | Base | Base | 2.05% | 25.14% | 36.75% | | [StableHermes 3B](https://hf.co/cxllin/StableHermes-3b) | 3B | GPT | SFT | 3.64% | 24.31% | *37.28%* | | [MPT 7B Instruct](https://hf.co/mosaicml/mpt-7b-instruct) | **7B** | **Human**+Anthropic | SFT | 2.05% | 24.12% | 11.01% | | [OpenLLaMA 7B v2 open-instruct](http://hf.co/VMware/open-llama-7b-v2-open-instruct) | **7B** | **Human** (nearly: ecqa is an exception) | SFT | 8.64% | 23.21% | 29.84% | | [StableLM Zephyr 3B](https://hf.co/stabilityai/stablelm-zephyr-3b) | 3B | GPT | DPO | possibly contaminated (45.72%) | **33.31%** | 0.91% | | [**Memphis-CoT 3B**](https://hf.co/euclaise/memphis-cot-3b) | 3B | **Human** | Self-teaching | **13.8%** | *26.24%* | **38.24%** | *5-shot, as performed automatically by LM Evaluation Harness bbh_cot_fewshot even with num_fewshot=0 Memphis outperforms other primarily-human-data models that are over twice its size, along with SFT models of its size, and trades with the Zephyr DPO model. That said, Zephyr uses synthetic data, and *much* more of it. Note that BBH results have wide SEs, sometimes even exceeding 16%. It is unclear why Zephyr performs so poorly on BBH. Perhaps it is overfit, or maybe there was an issue with vllm. Notes: - Evaluations were performed using the `agieval` branch of [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) (commit `0bef5c9c273b1c2f68e6018d4bb9c32b9aaff298`), using the `vllm` model. - I tried to find human-data-trained StableLM models, but couldn't find any. I did find a few OpenLLaMA models, but they wouldn't load with LM Eval Harness and vllm. (I believe this can be fixed by changing the xformers backend, but I'm too lazy for that) - OpenLLaMA 7B v2 open-instruct is a particularly relevant comparison, as it was trained on a *very* similar dataset. ## Hyperparameters For the initial supervised finetuning step: - Adalite optimizer, default hyperparameters of supertrainer2000 unless otherwise specified - Lambda (Adalite's analogue to weight decay, see [here](https://arxiv.org/abs/2103.06583) for details) 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