IQuestLab/IQuest-Q1
About the project
IQuest-Q1 is an open Mixture-of-Experts model with ~320B total parameters (15B activated per token) from IQuest, built for agentic coding, reasoning and multi-step tool use, with a 512K context. It runs via SGLang or vLLM and plugs into Claude Code and Codex CLI.
Useful for
- Deploy the model locally with SGLang or vLLM using tensor parallelism across 8 GPUs
- Wire IQuest-Q1 as the backend for Claude Code or Codex CLI for agentic coding
- Call the model through its OpenAI-compatible API from the Python openai client
README summarized by DeepSeek V4.1 Flash. Details may be inaccurate.
Why it’s trending
- 10 stars so far today, about 50 expected by the end of the day.
- The spike has held for 2 days in a row — not a one-off blip.
- The repository is 1 day old and already has 98 stars. With less than two weeks of history, there's no usual pace to compare the spike against yet.
- Top new repositories this week: #157.
Stars per day
Bars are daily stars, the line is the usual pace. Red marks spike days.
Numbers
- Total stars
- 98
- Today
- 10 · ≈ 50 by evening
- Forks
- 1
- Issues and pull requests
- 1
- Watchers
- 0
- Created
- September 28, 2026
- Last push
- September 28, 2026
Star trust
Growth looks organic: forks and discussion are in line with active projects, and stars arrive unevenly, the way people give them.
These are heuristics, not a verdict: we judge by the repository’s behavior, not by a list of stargazers.
Spotted in
- September 29, 2026Top new repositories this week: #157
Share
README badge
Paste it into your README — the badge shows the current magnitude and links to this page.
[](https://gitnova.dev/en/r/IQuestLab/IQuest-Q1)Similar by description
-
1.2
MoonshotAI/Kimi-K3
Kimi K3 is an open-weight native multimodal agentic MoE language model with 2.8T parameters and a 1M-token context window, understanding text, images and video. It targets long-horizon coding, agentic knowledge work…
-
3.8
NaiveAI-Labs/Naive-N0.5-Flash
An open-weight 309B MoE model (15.5B active) for coding and AI R&D with a native 1M-token context via hybrid SWA–DSA attention. Weights and inference code under MIT, plus API access.
-
2.2
Taichu-AI/ZDTaichu5.0-9B
ZDTaichu5.0-9B is a multimodal foundation model built on Qwen3.5-9B and C-RADIOv4-H for image, video, spatial and embodied reasoning, and agentic tasks.