---
language:
- en
library_name: peft
pipeline_tag: text-generation
license: llama2
datasets:
- emrgnt-cmplxty/sciphi-textbooks-are-all-you-need
---

# ML1 Previews

This repository contains the previews for the ML1 model - [Reddit Post](https://www.reddit.com/r/LocalLLaMA/comments/16ul4sw/ml1_34b70b_phi_115_reproduction_on_llama2/)

Watch training live here: [https://api.wandb.ai/links/nickmitchko/t5d47kzr](https://api.wandb.ai/links/nickmitchko/t5d47kzr)

<div>
<iframe src="https://wandb.ai/nickmitchko/ml1-0.1/reports/ML1-Phi-Recreation---Vmlldzo1NTM3NjQw" style="border:none;height:1024px;width:100%">
</iframe>
</div>

## Checkpoints


| Model         | 1 Epoch Pct | Link |
|---------------|--------|-------|
| ML1-34b       | 15%    | [Directory](https://huggingface.co/nmitchko/ML1-34b-previews/tree/main/checkpoint-1)     |
| ML1-34b       | 50%    | ~     |
| ML1-34b       | 100%    | ~     |
| ML1-mistral-7b| 50% | ~ |
| ML1-mistral-7b| 100%|~|
| ML1-70b       | 15%    | ~     |
| ML1-70b       | 50%    | ~     |
| ML1-70b       | 100%    | ~     |

<!-- ![Screenshot](https://huggingface.co/nmitchko/i2b2-querybuilder-codellama-34b/resolve/main/Example%20Query.png) -->

## Model Description

The goal is to develop a series of models that can express superior performance given high quality data. To achieve this, I plan to experiment with the lovely dataset produced by [/u/docsoc1](https://www.reddit.com/user/docsoc1). Huge shout out to him/her! If you'd like to view that dataset, the link is below.

Dataset: [emrgnt-cmplxty/sciphi-textbooks-are-all-you-need](https://huggingface.co/datasets/emrgnt-cmplxty/sciphi-textbooks-are-all-you-need)

## Prompt Format

The model is trained using the alpaca format. Please see [here](https://github.com/tatsu-lab/stanford_alpaca#data-release) or below for that format:

```text
Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{instruction}

### Response:
```

### Architecture
`nmitchko/ML1-34b-previews` is a large language model repository of LoRA checkpoints specifically fine-tuned to add text-book synthesized data in the style of Phi 1/1.5.
It is based on [`codellama-34b-hf`](https://huggingface.co/codellama/CodeLlama-34b-hf) at 34 billion parameters.

The primary goal of this model is to test various fine tuning methods around high quality  data.
It was trained using [LoRA](https://arxiv.org/abs/2106.09685), specifically [QLora Multi GPU](https://github.com/ChrisHayduk/qlora-multi-gpu), to reduce memory footprint. 

See Training Parameters for more info  This Lora supports 4-bit and 8-bit modes.

### Requirements

```
bitsandbytes>=0.41.0
peft@main
transformers@main
```

Steps to load this model:
1. Load base model (codellama-34b-hf) using transformers
2. Download a checkpoint folder (checkpoint-1)
3. Apply LoRA using peft

## Training Parameters 

The model is currently training on [emrgnt-cmplxty/sciphi-textbooks-are-all-you-need](https://huggingface.co/datasets/emrgnt-cmplxty/sciphi-textbooks-are-all-you-need)


`emrgnt-cmplxty/sciphi-textbooks-are-all-you-need` contains textbook synthesized data.


| Item          | Amount | Units |
|---------------|--------|-------|
| LoRA Rank     | 64    | ~     |
| LoRA Alpha    | 16    | ~     |
| Learning Rate | 1e-4   | SI    |
| Dropout       | 5      | %     |

## Training procedure


The following `bitsandbytes` quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16

### Framework versions

- PEFT 0.6.0.dev0