---
language:
- en
---

### Description:
This is a llama 13b model merge of the LoRA with the same name.

### Objective for this project:
To create a model that upholds a logical thread, regardless of whether the output is verbose or concise. Training has been performed on a version of the pile of sets, reduced to 40% of its original size, to expedite training iterations. I personally utilize this model as an aid for storytelling and writing. While it serves this purpose adequately, I still perceive this version as a prototype.

### Prompt format: 
Stanford Alpaca

The prompt should start on a new line after "### Response:"
- For examples with a non-empty input field:

 ```
 Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
 
 ### Instruction:
 {instruction}
 
 ### Input:
 {input}
 
 ### Response:
 ```

- For examples with an empty input field:

 ```
 Below is an instruction that describes a task. Write a response that appropriately completes the request.
 
 ### Instruction:
 {instruction}
 
 ### Response:
 ```

### Perplexity Benchmarks:
- wikitext: 4.66796875

### Training information:
- 2 Epochs
- 64 / 32 R / A
- 1024 Cutoff
- 19 hours on an A6000

### Data used in training:
All cleaned and scrubbed in various ways then culled to various degrees.
- Camel biology, physics, chemistry, math, and AI society
- Alpaca evol instruct
- GPTeacher Instruct
- Alpaca GPT4
- Dolly Databricks

### Plans for the future, a brief overview:
- Pivot to a conversational format going forward
- Train another 13b LoRA against the entirety of my pile of sets rather than just a portion of it for Mk2
- Train 30b on the Mk2 pile of sets 
- Expand the story generation capabilities and likely more for Mk3

### Model used for training and other information: 
https://huggingface.co/PocketDoc/llama-13b-gptq-4bit-128g

Merge model:
https://huggingface.co/huggyllama/llama-13b

### Disclaimer:
It has not been aligned and no warranty is given for the quality or safety of its outputs.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_PocketDoc__Dans-PileOfSets-Mk1-llama-13b-merged)

| Metric                | Value                     |
|-----------------------|---------------------------|
| Avg.                  | 45.76   |
| ARC (25-shot)         | 58.79          |
| HellaSwag (10-shot)   | 81.79    |
| MMLU (5-shot)         | 48.12         |
| TruthfulQA (0-shot)   | 41.24   |
| Winogrande (5-shot)   | 76.16   |
| GSM8K (5-shot)        | 8.49        |
| DROP (3-shot)         | 5.71         |