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An open replica of Jev built on Gemma 4 E2B with an attention LoRA, trained and served on Modal. It answers Choice, Score and Noul questions in one forward pass. The code is MIT licensed; the weights are not yet on Hugging Face.
A workable dense decision model from mithalouni. Treat the modality benchmarks above as the leading indicator of fit — composite scoring across modalities is still maturing. Newly released, so production-readiness is still being shaken out.
Generated from this model’s benchmarks and ranking signals. Editor reviews refine it over time.
The top devices for this model at 4-bit, ranked by fit and speed.
| Device | Grade | VRAM |
|---|---|---|
| ACEMAGIC M1A Pro (i9-13900HK + ARC A770)ACEMAGIC | SS | 0.5 GB |
| Acer Veriton GN100 AI MiniAcer | SS | 0.5 GB |
| AMD Instinct MI300XAMD | SS | 0.5 GB |
| AMD Instinct MI325XAMD | SS | 0.5 GB |
| AMD Instinct MI355XAMD | SS | 0.5 GB |
Explore the Provider
Aggregate stats, leaderboard, release timeline, and benchmark coverage across every mithalouni model we track.
Cheapest current cloud rentals with at least 1 GB VRAM, refreshed hourly.
Advertising disclosure: we earn commissions when you shop through the links below.
| Option | Cost / GPU-hour |
|---|---|
NVIDIA GeForce RTX 4060Vast.ai · Spot · 8 GB VRAM | $0.03 |
NVIDIA GeForce RTX 2080 TiVast.ai · Spot · 11 GB VRAM | $0.04 |
NVIDIA GeForce RTX 3070Vast.ai · Spot · 8 GB VRAM | $0.05 |
NVIDIA GeForce RTX 3060Vast.ai · Spot · 12 GB VRAM | $0.05 |
NVIDIA GeForce RTX 4060 TiVast.ai · Spot · 8 GB VRAM | $0.05 |
Per-GPU rate across RunPod, the Vast.ai marketplace, DigitalOcean, and Vultr.
Spot tier is interruptible. Plan for restarts when comparing against on-demand prices.
