初始化项目，由ModelHub XC社区提供模型

Model: Deeokay/llama3-8B-Instruct-GGUF
Source: Original Platform

.gitattributes

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README.md

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+---
+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.. 
+
+

config.json

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+{
+    "model_type": "llama"
+}

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