Datasetpublic

marin-community/openthoughts4-science-26041-prompts-qwen3-32b-n8-flattened-logprobs-k16

OpenThoughts-4 Science SDG: Qwen3-32B (n=8, top-16 logprobs) Synthetic generations from Qwen/Qwen3-32B on the Marin OpenThoughts-4 science SDG prompt set. Each prompt is sampled n=8 times, and for every generated token the dataset stores the chosen-token log probability plus the top-16 log probabilities over the vocabulary, enabling distillation, KL-style fine-tuning, reranking, and uncertainty analysis. Generation setup Field Value Generator model… See the full description on the dataset page: https://huggingface.co/datasets/marin-community/openthoughts4-science-26041-prompts-qwen3-32b-n8-flattened-logprobs-k16.

sourceHugging Faceapache-2.0updated 5mo agoView on Hugging Face

0likes189downloads

Dataset Card

OpenThoughts-4 Science SDG: Qwen3-32B (n=8, top-16 logprobs)

Synthetic generations from **Qwen/Qwen3-32B** on the Marin OpenThoughts-4 science SDG prompt set. Each prompt is sampled n=8 times, and for every generated token the dataset stores the chosen-token log probability plus the top-16 log probabilities over the vocabulary, enabling distillation, KL-style fine-tuning, reranking, and uncertainty analysis.

Generation setup

Field	Value
Generator model	`Qwen/Qwen3-32B` (revision `9216db5781bf21249d130ec9da846c4624c16137`)
Source prompts	`marin-community/hero-run-4-science-sdg-prompts-boxed-n8` (26,041 unique prompts)
Samples per prompt (`n`)	8
Logprobs returned (`k`)	16 (top-k vocab logprobs per generated token)
Max generated tokens	32,768
Max model length	34,816
Temperature	0.8
Inference engine	vLLM on TPU v6e-8, `tensor_parallel_size=8`
Producer	marin-community/marin — `experiments/sdg/science/qwen3-32b/sdg_ot4_science_32768_tokens.py`

Schema

The dataset is a flattened parquet table with one row per `(prompt, sample_index)` pair. With 26,041 unique prompts and n=8 samples each, the dataset contains 208,328 rows total.

Identifier columns

Column	Type	Description
`prompt_index`	int64	0-based index of the source prompt within the prompt set (0 … 26,040)
`response_index`	int64	0-based sample index within that prompt (0 … 7)
`_unique_row_id`	string	Stable globally-unique id for the `(prompt, response)` pair, copied from the source prompt set; safe key for joins and dedup
`instruction_seed`	string	The original natural-language science problem from OpenThoughts-4 (before any chat templating)
`generation_prompt`	string	The chat-templated prompt actually fed to vLLM (Qwen3 chat template applied to `instruction_seed`)

A given prompt is fully identified by either prompt_index or _unique_row_id; the 8 samples for a prompt share both keys and differ only in response_index.

Generation columns

Column	Type	Description
`generated_text`	string	Decoded model response (everything after the chat template's assistant turn)
`generated_token_ids`	list[int32], length `T`	Token ids of the generated response
`generated_token_logprobs`	list[float32], length `T`	Log probability of each chosen token under the model
`generated_top_logprob_token_ids`	list[int32], length `T × k`	Flattened top-k candidate token ids at each step
`generated_top_logprobs`	list[float32], length `T × k`	Flattened log probabilities of those top-k candidates at each step

Where T = number of generated tokens for that row and k = 16.

Reshaping the flattened top-k arrays

vLLM was run with flat_logprobs=True for compactness; the per-step top-k arrays are stored as 1-D lists. To recover the standard (T, k) shape:

python

import numpy as np
import pyarrow.parquet as pq

df = pq.read_table("part-000000.parquet").to_pandas()
row = df.iloc[0]
T = len(row["generated_token_ids"])
k = 16
top_ids   = np.asarray(row["generated_top_logprob_token_ids"]).reshape(T, k)
top_logps = np.asarray(row["generated_top_logprobs"]).reshape(T, k)
# top_ids[t, :]   = top-16 candidate token ids at step t
# top_logps[t, :] = log p of those candidates at step t (sorted high → low by vLLM)
# the chosen token may or may not appear in the top-k; use generated_token_logprobs
# for the chosen-token logprob.

File layout

The dataset is sharded into ~2,250 parquet files named part-NNNNNN.parquet. Use the default config to load the full train split.

Companion datasets

Slice	Generator
science	Qwen3-30B-A3B-Thinking-2507
science	Qwen3-32B (this dataset)
science	Qwen3-4B
science	Gemma-4-31B-IT (forthcoming)
code	Qwen3-30B-A3B-Thinking-2507 / Qwen3-32B / Qwen3-4B

License

Released under Apache 2.0. The underlying generator model (Qwen/Qwen3-32B) is governed by its own license; consult the model card before redistribution.

Citation

If you use this dataset, please cite the Marin project and the Qwen3 technical report.