geniac_prize_2026_v2r2 is an open-weight model from Mineral. It has 4B parameters and a 32,768-token context. At 16-bit it needs about 9.7 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.
GENIAC-PRIZE 2026 テーマ2 コンペスクリーニング（学生・人材育成枠）の参加者が作成した開発モデルです。 ベースモデル Qwen/Qwen3-4B-Base に 2 段階の QLoRA SFT（事後学習）を施し、LoRA をマージしたものです。 - ライセンス: apache-2.0（ベースモデルの条件に従います） - Qwen/Qwen3-4B-Base (https://huggingface.co/Qwen/Qwen3-4B-Base) - 生成元の問題はすべて上記 PRM800K の…
Runs On
What it takes to serve geniac_prize_2026_v2r2 (4B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 8.0 GB | 9.7 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 4.0 GB | 4.8 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 2.0 GB | 2.4 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 18, 2026.
geniac_prize_2026_v2r2 on every accelerator the SAVRN Index prices, at every precision
Model Card
GENIAC-PRIZE 2026 テーマ2 コンペスクリーニング（学生・人材育成枠）の参加者が作成した開発モデルです。 ベースモデル Qwen/Qwen3-4B-Base に 2 段階の QLoRA SFT（事後学習）を施し、LoRA をマージしたものです。 - ライセンス: apache-2.0（ベースモデルの条件に従います） - Qwen/Qwen3-4B-Base (https://huggingface.co/Qwen/Qwen3-4B-Base) - 生成元の問題はすべて上記 PRM800K の train split（12,000 行）から機械的に層化抽出したものです（評価用データは一切使用していません）。 - Qwen/Qwen3-4B-Base 自身にその問題を解かせ、出力中の \boxed{...} の内容が参照解と正規化一致したものだけを機械的なルールのみで採用しました（1,698 件）。 - 人手による作成・加工・選別、および他のモデル・API の利用は行っていません。 - GSM8K / LIMO / Aya / OpenAssistant / Tulu 3 Persona IF は使用していません。 - したがって Tulu 3 Persona IF (ODC-BY-1.0) の帰属表示は不要です。 2 段階の QLoRA SFT。データの取得・整形・学習はすべて Google Colab 上で実行しました。 - maxseqlength 3072、bf16、targetmodules = q, k, v, o, gate, up, down…
Excerpt from the card by Mineral.
Configuration
- Architecture
- Qwen3ForCausalLM
- Context length (tokens)
- 32,768
- Layers
- 36
- Hidden size
- 2,560
- Feed-forward size
- 9,728
- Attention heads
- 32
- Key/value heads
- 8
- Head dimension
- 128
- Vocabulary size
- 151,936
- Model type
- qwen3
Identity and Version
- Repository
- Mineral232323/geniac_prize_2026_v2r2
- Publisher
- Mineral
- Task
- Not stated by the source
- Modality
- Other
- Library
- Not stated by the source
- Parameters
- 4B parameters
- Languages
- Not stated by the source
- Revision
- e00d108cfa513c07e048a1dabdd1abde8d4a0fe6
- First published
- 2026-10-14
- Last updated
- 2026-10-14
Files and Weights
8 files, 8.1 GB in total. The weights are 1 file totalling 8.0 GB in safetensors.
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 8.0 GB | 2d3dd9eae852 |
| config.json | Configuration | 1.6 KB | — |
| generation_config.json | Configuration | 139 B | — |
| README.md | Documentation | 5.0 KB | — |
| chat_template.jinja | Other | 2.6 KB | — |
| .gitattributes | Repository | 1.6 KB | — |
| tokenizer.json | Tokenizer | 11.4 MB | 476870a1f2fb |
| tokenizer_config.json | Tokenizer | 406 B | — |
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
- Download size
- 8.0 GB
Released by Mineral through its official repository on Hugging Face.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 8.0 GB |
| 16-bit | 8.0 GB |
| 8-bit | 4.0 GB |
| 4-bit | 2.0 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About geniac_prize_2026_v2r2
How much GPU memory does geniac_prize_2026_v2r2 need?
About 9.7 GB at 16-bit and 2.4 GB at 4-bit: the weights (4B parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run geniac_prize_2026_v2r2 on?
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
What is geniac_prize_2026_v2r2's context length?
32,768 tokens, from the maximum position embeddings in its published configuration.