
# Memphis-CoT-3B GGUF

![](https://cdn-uploads.huggingface.co/production/uploads/64137e2150358a805203cbac/DlTWku8gant1yx6NaxqJX.png)

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.
{"language": ["en"], "license": "cc-by-sa-3.0", "tags": ["supertrainer2000", "human-data", "stablelm_epoch"], "datasets": ["euclaise/TinyCoT", "euclaise/reddit-instruct", "sablo/oasst2_curated"], "model_name": "Memphis-CoT-3B", "base_model": "euclaise/Memphis-CoT-3B", "model_creator": "euclaise", "model_type": "stablelm_epoch", "inference": false, "pipeline_tag": "text-generation", "prompt_template": "{{system_message}}\n### User:\n{{prompt}}\n### Assistant:\n", "quantized_by": "brittlewis12"}
gguf supertrainer2000 human-data stablelm_epoch text-generation en dataset:euclaise/TinyCoT dataset:euclaise/reddit-instruct dataset:sablo/oasst2_curated base_model:euclaise/Memphis-CoT-3B license:cc-by-sa-3.0 region:us