unsloth
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Use PEFT or Full-parameter to finetune 300+ LLMs or 60+ MLLMs. (Qwen2, GLM4v, Internlm2.5, Yi, Llama3.1, Llava-Video, Internvl2, MiniCPM-V-2.6, Deepseek, Baichuan2, Gemma2, Phi3-Vision, ...)
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Aug 14, 2024 - Python
PTIT's Major Project: Website Programming - This repo contains a chatbot for a clothing store. The chatbot acts as an employee with specific knowledge about clothing consultation, website support, and store information.
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May 23, 2024 - Jupyter Notebook
Materials for CSE Summer School Hackathon 2024
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Jul 16, 2024 - Jupyter Notebook
Fine-tuning Llama3 8b to generate JSON formats for arithmetic questions and process the output to perform calculations.
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May 28, 2024 - Python
perform deduplication on FLAN v2 dataset & Finetune LLaMa3 using this dataset
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Jun 19, 2024 - Jupyter Notebook
Fine-tuning GPT-3.5 and Llama3 LLMs for enhanced persona consistency in chatbots using Google's Synthetic Persona Chat dataset
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Jun 28, 2024 - Jupyter Notebook
Open Source Verilog Copilot: Fine Tune LLM: Fine-Tuning LLM with QLoRA and VeriGen Dataset using Unsloth
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Jul 2, 2024 - Python
fine-tuning framework
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Apr 21, 2024 - Jupyter Notebook
During AI internship at princelab
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Jun 16, 2024 - Jupyter Notebook
Llama 3.1 Fine Tuning
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Aug 9, 2024 - Jupyter Notebook
Llama3 Trainer aims to provide a CLI interface to orchestrate the fine-tuning of open source AI models, such as Llama3, using 3rd party services, such as [Lambda Cloud](https://lambdalabs.com/), [Hugging Face](https://huggingface.co/) and [Weights & Biases](https://wandb.ai).
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May 22, 2024 - JavaScript
The project of implementing different deep learning models on doing machine-generated text detection and even the mixed human-machine text detection
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May 7, 2024 - Jupyter Notebook
⚙️ Fine-Tune 🦙 Llama 3.1, Phi-3.. Models on custom DataSet using 🕴️ unsloth & Saving to HuggingFace Hub
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Jul 27, 2024 - Jupyter Notebook
The Mistral-Nemo-12b model has been fine-tuned for text generation tasks. This fine-tuning was performed using the Unsloth optimization framework, which significantly accelerates the training process, achieving a 2x faster fine-tuning time compared to conventional methods.
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Jul 23, 2024 - Python
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