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Memphis: Advancing language model reasoning without relying on proprietary model outputs
Memphis: Advancing language model reasoning without relying on proprietary model outputs
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Memphis is a series of models which advance human-data models, offering good performance without relying on proprietary model outputs (e.g. GPT-generated datasets). I've developed a new iterative finetuning procedure to improve the reasoning ability of these models beyond what is possible using only SFT on the same data.
Memphis is a series of models which advance human-data models, offering good performance without relying on proprietary model outputs (e.g. GPT-generated datasets). I've developed a new iterative finetuning procedure to improve the reasoning ability of these models beyond what is possible using only SFT on the same data.
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Currently, I've released two models: Memphis-CoT-3B, and Memphis-scribe-3B.
Currently, I've released two models: Memphis-CoT-3B, and Memphis-scribe-3B.
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To create these models, I've created new datasets:
To create these models, I've created new datasets:
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- 
resource
https://huggingface.co/datasets/euclaise/reddit-instruct
dataset
euclaise/reddit-instruct
https://huggingface.co/datasets/euclaise/reddit-instruct
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 : A dataset of instruction/QA-like data scraped from Reddit. A curated version, filtered using Lilac and neural embedding models, is available at 
 : A dataset of instruction/QA-like data scraped from Reddit. A curated version, filtered using Lilac and neural embedding models, is available at 
resource
https://huggingface.co/datasets/euclaise/reddit-instruct-curated
dataset
euclaise/reddit-instruct-curated
https://huggingface.co/datasets/euclaise/reddit-instruct-curated
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resource
https://huggingface.co/datasets/euclaise/TinyCoT
dataset
euclaise/TinyCoT
https://huggingface.co/datasets/euclaise/TinyCoT
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 : TinyCoT is a mtea-dataset that aggregates a variety of different human-sourced reasoning data. It is a curated version of my previous MegaCoT dataset 
 : TinyCoT is a mtea-dataset that aggregates a variety of different human-sourced reasoning data. It is a curated version of my previous MegaCoT dataset 
resource
https://huggingface.co/datasets/euclaise/MegaCoT
dataset
euclaise/MegaCoT
https://huggingface.co/datasets/euclaise/MegaCoT
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, which contains 629k responses which get cut down to 28k for TinyCoT.  There's also an intermediate version 
, which contains 629k responses which get cut down to 28k for TinyCoT.  There's also an intermediate version 
resource
https://huggingface.co/datasets/euclaise/MiniCoT
dataset
euclaise/MiniCoT
https://huggingface.co/datasets/euclaise/MiniCoT
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, which has 129k responses.
, which has 129k responses.
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Memphis-CoT is trained on reddit-instruct, a filtered version of oasst2 
Memphis-CoT is trained on reddit-instruct, a filtered version of oasst2 
resource
https://huggingface.co/datasets/sablo/oasst2_curated
dataset
sablo/oasst2_curated
https://huggingface.co/datasets/sablo/oasst2_curated
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, and TinyCoT. Multiple iterations were performed on TinyCoT, while reddit-instruct and oasst2 were only used for the initial model.
, and TinyCoT. Multiple iterations were performed on TinyCoT, while reddit-instruct and oasst2 were only used for the initial model.
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Memphis-scribe further finetunes Memphis-CoT on more creative tasks. It was finetuned from Memphis-CoT on 18 different datasets, including datasets like 
Memphis-scribe further finetunes Memphis-CoT on more creative tasks. It was finetuned from Memphis-CoT on 18 different datasets, including datasets like 
resource
https://huggingface.co/datasets/euclaise/WritingPrompts_curated
dataset
euclaise/WritingPrompts_curated
https://huggingface.co/datasets/euclaise/WritingPrompts_curated
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, 
, 
resource
https://huggingface.co/datasets/lemonilia/LimaRP
dataset
lemonilia/LimaRP
https://huggingface.co/datasets/lemonilia/LimaRP
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, and more.
, and more.
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To prevent catastrophic forgetting, I used weight averaging between iterations.
To prevent catastrophic forgetting, I used weight averaging between iterations.
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resource
https://huggingface.co/euclaise/Memphis-CoT-3B
model
euclaise/Memphis-CoT-3B
https://huggingface.co/euclaise/Memphis-CoT-3B
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resource
https://huggingface.co/euclaise/Memphis-scribe-3B
model
euclaise/Memphis-scribe-3B
https://huggingface.co/euclaise/Memphis-scribe-3B
Memphis: Advancing language model reasoning without relying on proprietary model outputs

Memphis is a series of models which advance human-data models, offering good performance without relying on proprietary model outputs (e.g. GPT-generated datasets). I've developed a new iterative finetuning procedure to improve the reasoning ability of these models beyond what is possible using only SFT on the same data.

Currently, I've released two models: Memphis-CoT-3B, and Memphis-scribe-3B.

To create these models, I've created new datasets:
- https://huggingface.co/datasets/euclaise/reddit-instruct : A dataset of instruction/QA-like data scraped from Reddit. A curated version, filtered using Lilac and neural embedding models, is available at https://huggingface.co/datasets/euclaise/reddit-instruct-curated
- https://huggingface.co/datasets/euclaise/TinyCoT : TinyCoT is a mtea-dataset that aggregates a variety of different human-sourced reasoning data. It is a curated version of my previous MegaCoT dataset https://huggingface.co/datasets/euclaise/MegaCoT, which contains 629k responses which get cut down to 28k for TinyCoT.  There's also an intermediate version https://huggingface.co/datasets/euclaise/MiniCoT, which has 129k responses.

Memphis-CoT is trained on reddit-instruct, a filtered version of oasst2 https://huggingface.co/datasets/sablo/oasst2_curated, and TinyCoT. Multiple iterations were performed on TinyCoT, while reddit-instruct and oasst2 were only used for the initial model.

Memphis-scribe further finetunes Memphis-CoT on more creative tasks. It was finetuned from Memphis-CoT on 18 different datasets, including datasets like https://huggingface.co/datasets/euclaise/WritingPrompts_curated, https://huggingface.co/datasets/lemonilia/LimaRP, and more.

To prevent catastrophic forgetting, I used weight averaging between iterations.

- https://huggingface.co/euclaise/Memphis-CoT-3B
- https://huggingface.co/euclaise/Memphis-scribe-3B
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