5/ Unlocks multi-model deployments. Serve 100s of LoRAs from a single #GPU deployment with #LoRAX.
SOFTWARE
-
LoRA Fine-Tuning with Predibase Speeds Up Inference 3x
By
–
6/ They are only getting better! LoRA fine-tuning with Predibase makes inference 3x faster compared to the base model.
-
LoRA Training Parameters: Minimal Memory Footprint at 0.2%
By
–
3/ Extremely low memory footprint. LoRA trainable parameters are only 0.2% of total amount of model parameters.
-
Running LM Studio on MacBook M1 Pro: Hardware Requirements
By
–
I am running this on Macbook M1 pro (16GB RAM) using the @LMStudioAI
. And ofc, you need some hardware to start with. Nothing will be free free lol -
Running LM Studio on MacBook M1 Pro with 16GB RAM
By
–
I am running this on Macbook M1 pro (16GB RAM) using the @LMStudioAI
. -
WordPress AI Content Generation Tool with Free Trial
By
–
Pour wordpress en AI, y'a pas photo (si il les génère aussi), Vous pouvez tester gratuitement avec toute les fonctionnalités pendant un temps limité Lien sponso, mais je conseille vivement https://
otomatic.ai/?via=jessy #wordcamp #seo #AIcontent #otomatic -
Local ChatGPT Memory Application Demo Using Streamlit
By
–
Working Application demo using Streamlit
— Shubham Saboo (@Saboo_Shubham_) 10 juin 2024
Paste the above code in vscode or pycharm and run the following command: 'streamlit run local_chatgpt_memory.py' pic.twitter.com/V8qsEyo3H1Working Application demo using Streamlit Paste the above code in vscode or pycharm and run the following command: 'streamlit run local_chatgpt_memory.py'
-
Full Application Code for Local ChatGPT with Memory
By
–
Full Application code for Local ChatGPT with memory
-
Building Chat App with Llama-3 Model API Integration
By
–
7. Generate Response and Display • Generate response using the Llama-3 model with '
http://
client.chat.completions.create()'
• Add the assistant response to the chat history and display it in a chat message container -
Streamlit Chat Input Handling and History Management
By
–
6. Accept User Input • Accept user input using '
http://
st.chat_input()'
• Add the user message to the chat history and display it in a chat message container