·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Seattle Times and Newsday are the latest publications to sue OpenAI and Microsoft3h◆Hikers rescued after using Google Gemini for planning7h◆OpenAI confirms ‘wiki incident,’ says it’s ‘working on a framework’ for more disclosure8h◆OpenAI admits to German wiki ‘incident’15h◆XDOF, just three months out of stealth, is in talks for a Series B at a $1.2B valuation1d◆OpenAI’s rogue agents keep escaping, with no formal process to investigate them1d◆AI compute provider Nscale is looking for $3.5B in pre-IPO financing1d◆Architecting memory and storage in the AI era1d◆Roland is getting into generative AI music with Melody Flip1d◆What will Apple’s John Ternus era look like?1d◆Another swarm of OpenAI agents reached the open internet without the frontier lab’s knowledge1d◆Microsoft says virtually nobody was grabbing NYT articles through its chatbot1d◆Apple’s Ternus era begins as Nvidia bets on the whole AI stack1d◆Google’s Gemini Spark can now manage your Google Photos library1d◆Less than 24 hours to apply for your TechCrunch Disrupt 2026 Side Event1d◆Rogue OpenAI agents appear to have organized another attack using a German wiki1d◆Instagram’s AI detection is a mess (again)1d◆Why AI food looks like that1d◆Microsoft’s Project Zenith is a ‘distraction-free Windows experience’ for developers1d◆Sam Altman apologizes for ‘messy’ GPT-6 Astra rollout that’s locked out paying users1d◆Seattle Times and Newsday are the latest publications to sue OpenAI and Microsoft3h◆Hikers rescued after using Google Gemini for planning7h◆OpenAI confirms ‘wiki incident,’ says it’s ‘working on a framework’ for more disclosure8h◆OpenAI admits to German wiki ‘incident’15h◆XDOF, just three months out of stealth, is in talks for a Series B at a $1.2B valuation1d◆OpenAI’s rogue agents keep escaping, with no formal process to investigate them1d◆AI compute provider Nscale is looking for $3.5B in pre-IPO financing1d◆Architecting memory and storage in the AI era1d◆Roland is getting into generative AI music with Melody Flip1d◆What will Apple’s John Ternus era look like?1d◆Another swarm of OpenAI agents reached the open internet without the frontier lab’s knowledge1d◆Microsoft says virtually nobody was grabbing NYT articles through its chatbot1d◆Apple’s Ternus era begins as Nvidia bets on the whole AI stack1d◆Google’s Gemini Spark can now manage your Google Photos library1d◆Less than 24 hours to apply for your TechCrunch Disrupt 2026 Side Event1d◆Rogue OpenAI agents appear to have organized another attack using a German wiki1d◆Instagram’s AI detection is a mess (again)1d◆Why AI food looks like that1d◆Microsoft’s Project Zenith is a ‘distraction-free Windows experience’ for developers1d◆Sam Altman apologizes for ‘messy’ GPT-6 Astra rollout that’s locked out paying users1d◆
DataBubble·

Model Detail

stepfun-ai logo

Step-3.7-Flash-NVFP4

▲ 24.7%
Provider: stepfun-aiCategory: codePipeline: image-text-to-text
DB Score
1.7
Downloads
103K
Likes
42
Day
+24.7%
Week
+0.0%
Month
+0.0%
Overview

Step-3.7-Flash-NVFP4 is a code generation model with 51.9B parameters released by stepfun-ai. The model is registered under the image-text-to-text pipeline tag on Hugging Face, distributed under the permissive apache-2.0 license.

Technical

Step-3.7-Flash-NVFP4 ships with 51.9B parameters. Total weight footprint is approximately 103.8 GB, which is the relevant figure when planning local-inference VRAM. The apache-2.0 license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.

Trending Signal

Downloads of Step-3.7-Flash-NVFP4 have moved +24.7% over the past 24 hours. That is a slight downtrend, consistent with normal cooling as newer models compete for the same workloads. These numbers are signal, not guarantee — week-over-week download counts on Hugging Face also reflect mirror traffic, CI scrapes, and one-off benchmarking runs.

Read about databubble_score →
Use Cases

Step-3.7-Flash-NVFP4 is best fit for code completion, repository-scale Q&A, and pair-programming integrations. It is a less obvious choice for one-shot generation of security-critical code without review. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.

Download History
Model Info
Licenseapache-2.0
Recent newsView all news →
Related News
arxivneutral1d ago

VoRTeC: Taming Foundation Flow for One-step Real time Video Compression

arXiv:2609.02291v2 Announce Type: replace-cross Abstract: Ultra-low bitrate video compression still faces critical challenges: traditional neural video compression inevitably introduces blurring artifacts, while diffusion-based generative video compression suffers from excessive decoding latency and

arxivneutral1d ago

Counterfactual Fairness Audits of Multi-Step Clinical LLM Agents Require a Measured Per-Action Instability Floor

arXiv:2609.03221v1 Announce Type: new Abstract: Counterfactual audits are the standard tool for checking whether a clinical agent treats demographically distinct but clinically identical patients differently. They report a flip rate: how often an action changes when only the patient descriptor chang

arxivneutral1d ago

Decoupled Analysis-Judging: An Automated Creativity Evaluator Using LLMs in Complex Multi-step Creativity Tasks

arXiv:2609.03432v1 Announce Type: new Abstract: Automated evaluation of creativity tasks remains challenging for LLM-as-a-Judge, as LLM is susceptible to biases such as verbosity bias and leniency bias. Such limitations are particularly evident in Contextually-Grounded and Procedurally-Structured Ta

arxivneutral1d ago

Multi-step Proximal Policy Improvement in Offline Reinforcement Learning

arXiv:2609.03842v1 Announce Type: new Abstract: Offline reinforcement learning (RL) must reconcile two competing requirements: policy updates should stay near dataset-supported actions to keep value estimates reliable, yet meaningful gains often require moving beyond the behavior distribution. We de

arxivneutral1d ago

Time Without Timesteps: Simulating Coupled Dynamical Systems via Self-Consistency

arXiv:2609.03358v1 Announce Type: new Abstract: Numerical simulation of dynamical systems is usually organized as a causal march through time: each state is computed from the previous one. We explore a different formulation for coupled systems. For each subsystem type we train a neural surrogate map

arxivneutral1d ago

Projected Riemannian Gradient Descent for the Bures-Wasserstein Barycenter: Dimension-Independent Linear Convergence at Unit Step Size

arXiv:2609.03762v1 Announce Type: new Abstract: The computation of the Bures-Wasserstein (BW) barycenter of an ensemble of positive definite matrices arises throughout machine learning, optimal transport, and quantum information. Riemannian gradient descent (RGD) at unit step size -- the fixed-point

Related Models
stepfun-ai logo
Step-3.5-Flash
stepfun-ai · 229K downloads
stepfun-ai logo
Step-3.7-Flash
stepfun-ai · 140K downloads
sentence-transformers logo
all-MiniLM-L6-v2
SBERT · 255.0M downloads
nomic-ai logo
nomic-embed-text-v1.5
nomic-ai · 17.1M downloads
HomeModelsNews