Resource Library: Continued Learning
The Deep Dive. A curated list of the best books, newsletters, papers, and open-source tools to keep you at the cutting edge of LLM engineering.
Resource Library: Continued Learning
Fine-tuning is a lifelong journey. You have the map, but the terrain is constantly shifting. To help you stay at the forefront of the industry, we have curated the "Gold Standard" of AI resources.
Bookmark these, subscribe to these, and contribute to these.
1. Newsletters (The Weekly Pulse)
- The Batch (DeepLearning.AI): Andrew Ng's weekly summary of AI news and research.
- Import AI (Jack Clark): Deep technical analysis of AI policy and compute.
- Latent Space: The best deep-dive podcast and newsletter for AI Engineers.
- Interconnects (Nathan Lambert): Essential reading for understanding RLHF, DPO, and model evaluation.
2. Research & Papers (The Source)
- ArXiv.org: The "Home" of AI research. Follow the
cs.CL(Computation and Language) section. - Important Papers to Read:
- "Attention Is All You Need" (The Transformer birth)
- "LoRA: Low-Rank Adaptation of Large Language Models"
- "Direct Preference Optimization: Your Language Model is Secretly a Reward Model"
- "QLoRA: Efficient Finetuning of Quantized LLMs"
3. Toolkits & Libraries (The Workbench)
- Hugging Face (PEFT, TRL, Transformers): The mandatory ecosystem for every LLM engineer.
- Axolotl: A powerful, config-based wrapper for fine-tuning that avoids complex Python code.
- Unsloth: The current record-holder for the fastest, lowest-VRAM fine-tuning on consumer GPUs.
- vLLM: The undisputed king of high-throughput model serving.
4. Communities (The Conversation)
- LocalLLaMA Subreddit: The primary hub for the open-source model community.
- Hugging Face Discord: The best place to ask technical questions and get help from library maintainers.
- Weights & Biases (W&B) Community: A great place to see what other researchers are doing with their training logs.
Visualizing the Knowledge Stack
graph TD
A["Industry News (Newsletters)"] --> B["Technical Skills (Tutorials/Tools)"]
B --> C["Scientific Foundation (Research Papers)"]
C --> D["Community Insight (Discord/Reddit)"]
D --> E["MASTER LLM ENGINEER"]
Summary and Key Takeaways
- Stay Curious: Don't stop at the surface level. Read the original papers.
- Join the Community: AI is a collaborative sport. Share your fine-tuned adapters on Hugging Face.
- Experiments over Theory: 10 minutes of running a real fine-tuning job is worth 10 hours of watching videos.
- Be Selective: You can't read everything. Choose 1 newsletter and 1 focus area (e.g., "Deployment") to master at a time.
In the final, final lesson of this course, we say goodbye: Final Farewell: Your Future in AI.
Reflection Exercise
- Which of these resources feels the most relevant to your current project?
- Why is "Contributing to Open Source" (even just fixing a typo in a documentation page) one of the best ways to learn?
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