README.md

---
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
---

# Uploaded  model

- **Developed by:** Deeokay
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit

This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)


# README

This is a test model on a the following 
- a private dataset
- slight customization on llama3 template (no new tokens | no new configs)
- Works with Ollama create with just "FROM path/to/model" as Modelfile (llama3 template works no issues)

# HOW TO USE

The whole point of conversion for me was I wanted to be able to to use it through Ollama or (other local options)
For Ollama, it required to be a GGUF file. Once you have this it is pretty straight forward (if it is in llama3 which this model is)

Quick Start: 
- You must already have Ollama running in your setting
- Download the unsloth.Q4_K_M.gguf model from Files
- In the same directory create a file call "Modelfile"
- Inside the "Modelfile" type

```python
FROM ./unsloth.Q4_K_M.gguf

```
- Save a go back to the folder (folder where model + Modelfile exisit)
- Now in terminal make sure you are in the same location of the folder and type in the following command

```python
ollama create mycustomai  # "mycustomai" <- you can name it anything u want
```

This GGUF is based on llama3-3-8B-Instruct thus ollama doesn't need anything else to auto configure this model 

After than you should be able to use this model to chat! 

Model is also available in Ollama 
- deeokay/minillama   -> Q2_K version
- deeokay/mediumllama -> Q3_K_M version
- deeokay/customllama -> Q4_K_M version

In the terminal just 
```pthon
ollama pull deeokay/customllama
```

and you can use the model. 


# NOTE: DISCLAIMER

Please note this is not for the purpose of production, but result of Fine Tuning through self learning

The llama3 Special Tokens where kept the same, however the format was slight customized using the available tokens

I have foregone the {{.System}} part as this would be updated when converting the llama3. 

I wanted to test if the model would understand additional headers that I created such as what my datasets has 
- Analaysis, Classification, Sentiment

Mulitple pass through my ~70K personalized customized dataset. 

If would like to know how I started creating my dataset, you can check this link 
[Crafting GPT2 for Personalized AI-Preparing Data the Long Way (Part1)](https://medium.com/@deeokay/the-soul-in-the-machine-crafting-gpt2-for-personalized-ai-9d38be3f635f)

As the data was getting created with custom GPT2 special tokens, I had to convert that to the llama3 Template. 

However I got creative again.. the training data has the following Template:

```
<|begin_of_text|>
<|start_header_id|>user<|end_header_id|>
{{.Prompt}}<|eot_id|><|start_header_id|>analysis<|end_header_id|>
{{.Analysis}}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
{{.Response}}<|eot_id|><|start_header_id|>classification<|end_header_id|>
{{.Classification}}<|eot_id|><|start_header_id|>sentiment<|end_header_id|>
{{.Sentiment}}<|eot_id|> <|start_header_id|>user<|end_header_id|>
<|end_of_text|> 

```

The llama3 standard template holds, and can be created in Ollama through normal llama3 template

Will be updating this periodically.. as I have limited colab resources..