Testing Nemotron-4 340B: Synthetic Data and a Handful of Reasoning Tasks

Matthew Berman tests NVIDIA’s Nemotron-4 340B in June 2024, beginning with an answer to a physical-reasoning puzzle about a marble and an inverted glass. He introduces the large model family as a tool for generating synthetic data to train other models.

Berman describes the base, instruction and reward models as a pipeline for synthetic data generation. He says obtaining high-quality training data can be difficult and expensive, particularly for smaller teams, and sees value in an openly available model that can help create it.

The models were offered under NVIDIA’s open model license and optimized for NVIDIA NeMo and TensorRT-LLM. Berman says they could be downloaded from NVIDIA or Hugging Face and describes a planned NVIDIA NIM deployment option. These availability details reflect the 2024 announcement.

According to the material Berman presents, the reward model can filter responses using attributes including helpfulness, correctness, coherence, complexity and verbosity. He also notes a reported nine trillion training tokens and a reward-model leaderboard result from that period.

For a hands-on test, Berman selects the 340-billion-parameter model on a public model-comparison site and runs his own collection of prompts. This small, informal test illustrates individual outputs; it does not measure the quality of a synthetic-data pipeline or establish an overall ranking.

The model succeeds at a simple Python task to output numbers from 1 to 100. Asked to write Snake using the curses library, its first attempt contains an undefined food variable; after Berman asks it to correct the error, he finds the variable still undefined and marks the task as failed.

The model refuses a request to explain breaking into a car. Berman then asks how long 20 shirts take to dry if five dry in four hours. The model answers 16 hours by assuming time scales with the number of shirts. Berman gives it a pass while wishing it had discussed parallel drying; the answer depends on unstated capacity assumptions.

On arithmetic, the model follows the order of operations and answers 25 − 4 × 2 + 3 correctly as 20. Berman also says it solves a hotel price problem involving a nightly rate, tax and a one-time fee, though a network error appears while he highlights the response.

The model fails to count the words in its own response accurately. Berman notes that many models struggle with this kind of prompt. This observation is from his small set of tests rather than a systematic measurement.

In a riddle about three killers and a newcomer who kills one of them, the model says three killers remain: two original killers and the newcomer. Berman approves the explanation, which recognizes that the new arrival has become a killer.

For the marble puzzle, the model says the marble remains on the table when the inverted glass is lifted and moved to a microwave. Berman considers its explanation a strong pass in this test.

Finally, he asks for ten sentences ending in the word “Apple.” He says none meet the requirement, and lowering the temperature to zero does not fix the result in his retry. He concludes that the model performed well on some prompts but failed others; the video does not directly evaluate generated training data.