RTX 4070 Ti Practical AI Projects Guide | Generated by AI

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A 4070 Ti is plenty to learn—and to ship real things. You’ve got ~12 GB VRAM (16 GB if it’s the Ti Super). That means: run most 7–8B LLMs locally (quantized), fine-tune with LoRA/QLoRA, train solid CV models, do diffusion LoRA, audio ASR, and a lot of classic ML.

Here’s a practical menu, no fluff:

What you can run comfortably

• LLMs (local inference): 3–8B models in 4-bit easily (Llama-3.1-8B, Qwen-7B/7B-Coder, Phi-3-mini). 13B is doable with 4-bit + CPU offload but slower.
• Vision: YOLO family (n/s sizes), ViT-tiny/small, ConvNeXt-tiny, segmentation like U-Net-small.
• Diffusion: SD 1.5 smoothly; SDXL works with memory-saving flags/xFormers; LoRA training for styles is feasible.
• Audio: Whisper large-v2 for inference; fine-tune small/medium on domain audio.
• VLMs: LLaVA-7B (inference, and light QLoRA fine-tunes on your own image–text pairs).

“MiniGPT”-style and LLaMA options

• MiniGPT-4/LLaVA: Use a 7B base (Vicuna/Llama-3.1-8B) with 4-bit quant for inference; for customizing, run QLoRA on a few thousand curated image–text pairs. You won’t train the whole model, but you can adapt the head and LoRA layers.
• LLaMA-like models: Fine-tune Llama-3.1-8B-Instruct with QLoRA on your domain data (logs, FAQs, code). Great learning + practical value.

Concrete projects (each is a weekend → 2-week scope)

1. RAG assistant for your own notes/code

   • Stack: transformers, llama.cpp or ollama for local LLM, FAISS for vectors, langchain/llama-index.
   • Steps: build ingestion → retrieval → answer synthesis → evaluation harness (BLEU/ROUGE or custom rubrics).
   • Upgrade: add reranking (bge-reranker-base) and function calling.

2. QLoRA fine-tune of an 8B model on your domain

   • Stack: transformers, peft, bitsandbytes, FlashAttention if supported.
   • Data: collect 5–50k high-quality instruction pairs from your logs/wiki; validate with a small eval set.
   • Goal: <10 GB VRAM with 4-bit + gradient checkpointing; batch size via gradient accumulation.

3. Vision: train a lightweight detector

   • Train YOLOv8n/s on a custom dataset (200–5,000 labeled images).
   • Add augmentations, mixed precision, early stopping; export to ONNX/TensorRT.

4. Diffusion LoRA: your personal style or product shots

   • SD 1.5 LoRA on 20–150 images; use prior-preservation and low-rank (rank 4–16).
   • Deliver a .safetensors LoRA you can share and compose with other prompts.

5. Audio: domain ASR

   • Fine-tune Whisper-small/medium on your accent/domain meetings.
   • Build a diarization+VAD pipeline; add an LLM post-editor for punctuation and names.

6. Small language model from scratch (for fundamentals)

   • Implement a tiny Transformer (1–10 M params) on TinyShakespeare or code tokens.
   • Add rotary embedding, ALiBi, KV-cache, causal mask; measure perplexity and throughput.

How to fit in 12–16 GB VRAM

• Prefer 4-bit quantization (bitsandbytes, GPTQ, AWQ). 7–8B then sits around 4–6 GB.
• Use LoRA/QLoRA (don’t full-fine-tune). Add gradient checkpointing and grad accumulation.
• Enable AMP/bfloat16, FlashAttention/xFormers, and paged attention where available.
• For bigger models, offload optimizer/activations to CPU; consider DeepSpeed ZeRO-2/3 if needed.
• Keep context length realistic (e.g., 4k–8k) unless you truly need 32k.

Suggested learning roadmap (4–6 weeks)

• Week 1: Environment + “Hello LLM”

  • Linux or WSL2, latest NVIDIA driver + CUDA 12.x, PyTorch, ninja, flash-attn.
  • Run an 8B model locally via Ollama or llama.cpp; add a simple RAG over your markdowns.

• Week 2: QLoRA fine-tune

  • Prepare 5–10k instruction pairs; train Llama-3.1-8B with peft+bitsandbytes.
  • Evaluate with a fixed dev set; log with Weights & Biases.

• Week 3: Vision

  • Label a small dataset in Roboflow/Label Studio; train YOLOv8n; export and benchmark latency.

• Week 4: Diffusion LoRA

  • Collect 30–80 images; train SD 1.5 LoRA; test prompts; document your recipe.

• Weeks 5–6 (stretch): Build a VLM demo (LLaVA-7B) or an ASR pipeline (Whisper + LLM post-edit). Ship a web demo (FastAPI/Gradio).

Tooling that “just works” on a single GPU

• LLM serving: Ollama, llama.cpp, vLLM (great for throughput), text-generation-webui.
• Training: PyTorch + transformers + peft + bitsandbytes; Lightning or Accelerate to simplify.
• Vision: Ultralytics YOLO, MMDetection.
• Diffusion: diffusers + xFormers; Kohya or SD-Trainer for LoRA.
• Indexing: FAISS, Qdrant (local).
• Profiling: PyTorch profiler, Nsight Systems (optional).

Rough VRAM smell test (helpful rules of thumb)

• 7–8B FP16 needs ~14–16 GB just for weights → use 4-bit to fit in 12 GB.
• QLoRA on 7–8B with sequence length 2k, micro-batch 1–2 + grad accumulation typically fits.
• SD 1.5 image generation is fine; SDXL needs memory-saving flags and smaller batch.

Example commands to get moving

# LLM inference (ollama)
brew install ollama  # or Linux install script
ollama pull llama3.1:8b-instruct
ollama run llama3.1:8b-instruct

# QLoRA fine-tune (sketch)
pip install transformers peft bitsandbytes datasets accelerate trl
accelerate config
python train_qlora.py \
  --model_name meta-llama/Meta-Llama-3.1-8B-Instruct \
  --dataset your_instructions.jsonl \
  --load_in_4bit --bf16 --gradient_checkpointing \
  --lora_r 16 --lora_alpha 32 --lora_dropout 0.05 \
  --per_device_train_batch_size 1 --gradient_accumulation_steps 16 \
  --max_seq_length 2048 --num_train_epochs 2 \
  --output_dir ./lora-llama3.1-8b

# YOLO quickstart
pip install ultralytics
yolo task=detect mode=train model=yolov8n.pt data=your.yaml epochs=50 imgsz=640

# SD 1.5 LoRA (Diffusers)
pip install diffusers transformers accelerate xformers safetensors
# then follow the diffusers LoRA training script with --gradient_checkpointing

Final tips

• Favor quality over size: a well-tuned 7–8B often beats a poorly tuned 13B.
• Build evaluation first for each project; short feedback loops teach you fastest.
• Keep a repro notebook + README per experiment (seeds, flags, dataset versions).
• When you hit OOM: lower sequence length, use 4-bit, enable gradient checkpointing, and shrink micro-batch.

If you tell me which path excites you most (RAG, QLoRA, YOLO, diffusion, Whisper, or a VLM demo), I’ll drop a focused, copy-pastable recipe tailored to your 4070 Ti and OS.

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