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--- |
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base_model: unsloth/llama-3-8b-Instruct-bnb-4bit |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- text-generation-inference |
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- transformers |
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- unsloth |
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- llama |
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- gguf |
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--- |
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# Uploaded model |
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- **Developed by:** Deeokay |
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- **License:** apache-2.0 |
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- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit |
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This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. |
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) |
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# README |
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This is a test model on a the following |
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- a private dataset |
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- slight customization on llama3 template (no new tokens | no new configs) |
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- Works with Ollama create with just "FROM path/to/model" as Modelfile (llama3 template works no issues) |
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# HOW TO USE |
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The whole point of conversion for me was I wanted to be able to to use it through Ollama or (other local options) |
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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) |
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Quick Start: |
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- You must already have Ollama running in your setting |
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- Download the unsloth.Q4_K_M.gguf model from Files |
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- In the same directory create a file call "Modelfile" |
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- Inside the "Modelfile" type |
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```python |
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FROM ./unsloth.Q4_K_M.gguf |
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``` |
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- Save a go back to the folder (folder where model + Modelfile exisit) |
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- Now in terminal make sure you are in the same location of the folder and type in the following command |
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```python |
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ollama create mycustomai # "mycustomai" <- you can name it anything u want |
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``` |
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This GGUF is based on llama3-3-8B-Instruct thus ollama doesn't need anything else to auto configure this model |
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After than you should be able to use this model to chat! |
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Model is also available in Ollama |
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- deeokay/minillama -> Q2_K version |
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- deeokay/mediumllama -> Q3_K_M version |
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- deeokay/customllama -> Q4_K_M version |
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In the terminal just |
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```pthon |
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ollama pull deeokay/customllama |
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``` |
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and you can use the model. |
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# NOTE: DISCLAIMER |
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Please note this is not for the purpose of production, but result of Fine Tuning through self learning |
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The llama3 Special Tokens where kept the same, however the format was slight customized using the available tokens |
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I have foregone the {{.System}} part as this would be updated when converting the llama3. |
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I wanted to test if the model would understand additional headers that I created such as what my datasets has |
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- Analaysis, Classification, Sentiment |
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Mulitple pass through my ~70K personalized customized dataset. |
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If would like to know how I started creating my dataset, you can check this link |
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[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) |
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As the data was getting created with custom GPT2 special tokens, I had to convert that to the llama3 Template. |
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However I got creative again.. the training data has the following Template: |
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``` |
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<|begin_of_text|> |
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<|start_header_id|>user<|end_header_id|> |
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{{.Prompt}}<|eot_id|><|start_header_id|>analysis<|end_header_id|> |
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{{.Analysis}}<|eot_id|><|start_header_id|>assistant<|end_header_id|> |
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{{.Response}}<|eot_id|><|start_header_id|>classification<|end_header_id|> |
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{{.Classification}}<|eot_id|><|start_header_id|>sentiment<|end_header_id|> |
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{{.Sentiment}}<|eot_id|> <|start_header_id|>user<|end_header_id|> |
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<|end_of_text|> |
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``` |
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The llama3 standard template holds, and can be created in Ollama through normal llama3 template |
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Will be updating this periodically.. as I have limited colab resources.. |
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