Llama 3.3 70B Test - Coding, Data Extraction, Summarization, Data Labelling, RAG

Meta’s Llama 3.3 70B is landing as a strong all-around text model, with independent evaluations and hands-on tests pointing to performance that tracks closely with earlier top-tier releases—especially on structured tasks like coding, data extraction, and table-based question answering. The practical takeaway: even without local hardware, the model can be run quickly via Groq’s API, and it repeatedly handles “real work” prompts that many comparable models stumble on.

The transcript frames Llama 3.3 as an evolution over Llama 3.2, with Meta reporting evaluation results near Llama 3.1 and independent scoring from Artificial Analysis (California) suggesting an intelligence level comparable to Llama 3.1 and close to November’s GPT-4o. A key reported jump is on MMLU-style benchmarks (the creator highlights “MMLU” as the biggest increase). The model is multilingual, text-only, and supports a 128K context window with roughly 15T tokens for pretraining; its knowledge cutoff is December 23. Availability is described as primarily through Hugging Face, with the 70B variant offered in 4-bit quantization at about 43GB storage—still heavy enough to require serious GPU resources.

Because the creator lacks hardware for full local testing, the evaluation is done through Groq’s API. Two deployment modes are mentioned: a default “versatile” style and a speculative decoding variant that can be faster. Speed becomes a recurring theme: responses arrive quickly enough that the workflow feels practical for iterative testing.

In coding, Llama 3.3 is tested with a pandas task: generating a dataset of the wealthiest people in each continent, then sorting by continent and ordering wealth from poorest to richest. The model produces working, well-documented code and gets the ordering right—an outcome the creator says even some other large models failed to match.

For data labeling, the model is given five tweets related to categories such as malicious VS Code extensions, migrating infrastructure, AI replacing doors ASAP, “builder” motivation, and a more philosophical focus on attention and priorities. The labeling is described as extremely fast, with most outputs landing correctly; the creator notes a few areas of uncertainty (tone or topic assignment) but still calls the results close to perfect.

Summarization and generation are also tested using Meta’s latest earnings report. The model produces concise summaries that capture the gist of revenue and other key figures, and it generates LinkedIn-style posts without adding typical social-media embellishments like emojis or rockets—unless prompted otherwise.

The strongest “accuracy” moments come from extraction and retrieval-style prompts. When given receipt markdown, it extracts quantities and key fields correctly, with only minor formatting assumptions needing adjustment (e.g., converting seconds into hours/minutes). For earnings-report questions, it answers quote-grounded facts correctly (like expected 2024 tax rate) and extracts table values with correct numbers, including cases where the transcript says other models either miscomputed or failed to produce the requested table structure. Overall, the creator concludes Llama 3.3 70B “passes with flying colors,” while suggesting harder, more adversarial tests could further validate its limits—especially for agentic frameworks and less-available information scenarios.