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---
base_model: euclaise/Memphis-CoT-3B
datasets:
- euclaise/TinyCoT
- euclaise/reddit-instruct
- sablo/oasst2_curated
license: cc-by-sa-3.0
language:
- en
model_creator: euclaise
model_name: Memphis-CoT-3B
model_type: stablelm_epoch
inference: false
tags:
- supertrainer2000
- human-data
- stablelm_epoch
pipeline_tag: text-generation
prompt_template: |
{{system_message}}
### User:
{{prompt}}
### Assistant:
quantized_by: brittlewis12
---
# Memphis-CoT-3B GGUF
Original model: [Memphis-CoT-3B](https://huggingface.co/euclaise/Memphis-CoT-3B)
Model creator: [euclaise](https://huggingface.co/euclaise)
This repo contains GGUF format model files for euclaise’s Memphis-CoT-3B, updated for the latest training run as of 2/2/24.
> Memphis-CoT is a finetune of StableLM 3b 4e1t on TinyCoT, along with reddit-instruct (subset to 5000 examples, excluding posts with brackets in the title) and a curated subset of oasst2.
### What is GGUF?
GGUF is a file format for representing AI models. It is the third version of the format, introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Converted using llama.cpp b2022 ([8f8ddfc](https://github.com/ggerganov/llama.cpp/commits/8f8ddfcfadc830b936318c3ea9fe2e8e3365aa85))
### Prompt template:
```
{{system_message}}
### User:
{{prompt}}
### Assistant:
```
or Tiny CoT:
```
### User:
{{prompt}}
### Rationale:
[...]
### Answer:
```
---
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---
## Original Model Evaluations:
| 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. |