LLaVA Model Fine-Tuning

(⚠️urgent project. please only bid if you’re passionate and available to work intensely)


Key Steps:
1. Model Selection: Use open-source models like:
• Text Only: LLaMA 2, Mistral, Mixtral, Falcon.
• Multimodal (Text + Images): LLaVA, OpenFlamingo, Gemma, PixArt.
2. Dataset Preparation:
• Gather proprietary text + file data for training.
• Clean and preprocess data using tools like langchain and datasets.
3. Fine-Tuning:
• Fine-tune the model using LoRA, QLoRA, or PEFT techniques for efficiency.
• Use Hugging Face Transformers or Deepspeed for training.
4. Exporting the Model:
• Save the model weights and tokenizers.
• Convert to ONNX or TorchScript for optimized inference.
5. Self-Hosting:
• Deploy the model on a GPU server using:
• Hugging Face Inference Endpoint.
• NVIDIA Triton Inference Server.
• Kubernetes for scaling.
6. Security:
• Use Kyber for post-quantum encryption during model communication.



(((PLAN
#Get the Model: Download the pre-trained LLaVA weights.

#Fine-Tune on our Data: Use tools like Hugging Face’s transformers library.

#Export the Fine-Tuned Model: Save the new weights and tokenizer.

#Self-Host: Deploy using frameworks like Hugging Face Inference Endpoints, Nvidia Triton, or FastAPI.
)))