Multimodal LLM -- 2
Budget: $250 – $750 USD
I want a purpose-built large language model that understands text, images, and short audio clips and then turns that input into eye-catching social media posts. The model’s sole focus is content generation, so creativity, stylistic flexibility, and the ability to weave rich, on-brand narratives from any combination of the three modalities are essential.
Here’s how I picture the engagement:
• Model scope – The system should accept raw text, image files, or brief audio snippets (voice notes, ambient sounds, etc.) and return a complete post: written copy, recommended hashtags, and—when relevant—derivative imagery or an audio remix to accompany the caption.
• Tech stack – I’m comfortable with PyTorch or TensorFlow, Hugging Face Transformers, and standard diffusion or encoder-decoder approaches for cross-modal fusion. If you have a stronger alternative, make your case in the design notes.
• Training pipeline – Please outline data sourcing, preprocessing, augmentation, and alignment techniques to maintain brand tone and prevent hallucinations. I’ll provide sample brand guidelines; the rest of the dataset strategy is up to you.
• Delivery – Dockerised training environment, inference API (REST or gRPC), a short demo notebook, and concise documentation explaining architecture choices, hyper-parameters, and future fine-tuning hooks.
Acceptance criteria
1. Given any single or mixed-modality prompt, the model returns a coherent, platform-ready social post in under five seconds on an A10-class GPU.
2. Captions pass a basic grammar checker with ≥ 95 % accuracy and follow supplied style rules.
3. At least 80 % of generated media assets meet resolution and duration specs for major platforms (Instagram, TikTok, X).
4. Codebase installs from scratch with one command and all tests pass.
If this aligns with your skill set, let’s discuss timelines and milestones so we can bring this multimodal content engine to life.
Here’s how I picture the engagement:
• Model scope – The system should accept raw text, image files, or brief audio snippets (voice notes, ambient sounds, etc.) and return a complete post: written copy, recommended hashtags, and—when relevant—derivative imagery or an audio remix to accompany the caption.
• Tech stack – I’m comfortable with PyTorch or TensorFlow, Hugging Face Transformers, and standard diffusion or encoder-decoder approaches for cross-modal fusion. If you have a stronger alternative, make your case in the design notes.
• Training pipeline – Please outline data sourcing, preprocessing, augmentation, and alignment techniques to maintain brand tone and prevent hallucinations. I’ll provide sample brand guidelines; the rest of the dataset strategy is up to you.
• Delivery – Dockerised training environment, inference API (REST or gRPC), a short demo notebook, and concise documentation explaining architecture choices, hyper-parameters, and future fine-tuning hooks.
Acceptance criteria
1. Given any single or mixed-modality prompt, the model returns a coherent, platform-ready social post in under five seconds on an A10-class GPU.
2. Captions pass a basic grammar checker with ≥ 95 % accuracy and follow supplied style rules.
3. At least 80 % of generated media assets meet resolution and duration specs for major platforms (Instagram, TikTok, X).
4. Codebase installs from scratch with one command and all tests pass.
If this aligns with your skill set, let’s discuss timelines and milestones so we can bring this multimodal content engine to life.