Open-weight model
vidhi-ai-1.5b-sovereign-masterpiece
by Shreyansh singh shreyansh12183/vidhi-ai-1.5b-sovereign-masterpiece
vidhi-ai-1.5b-sovereign-masterpiece is an open-weight model from Shreyansh singh, released under Creative Commons Attribution-NonCommercial 4.0. It has 1.8B parameters and a 131,072-token context. At 16-bit it needs about 4.3 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.
License: Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) (Strictly Non-Commercial / Private) Offline Deployment: Ready for 1-click GGUF quantization (< 1 GB RAM footprint under Q4KM). 1.
Runs On
What it takes to serve vidhi-ai-1.5b-sovereign-masterpiece (1.8B 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 | 3.6 GB | 4.3 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 1.8 GB | 2.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.9 GB | 1.1 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.
vidhi-ai-1.5b-sovereign-masterpiece on every accelerator the SAVRN Index prices, at every precision
Model Card
License: Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) (Strictly Non-Commercial / Private) Offline Deployment: Ready for 1-click GGUF quantization (< 1 GB RAM footprint under Q4KM). 1. Modern Criminal Jurisprudence: Bharatiya Nyaya Sanhita (BNS 2023), BNSS 2023, BSA 2023, electronic evidence certification (Sec 63 BSA), first-time undertrial bail (Sec 479 BNSS). 2. Financial & Tax Law: GST Input Tax Credit rules (Sec 16(2) CGST), RBI Master Directions (KYC, lending norms), SARFAESI possession procedures, IBC 2016 insolvency (Sec 7/9). 3. Multi-State Vernacular & Constitutional Rights: Landmark Supreme Court precedents (Puttaswamy privacy, Maneka Gandhi…
Excerpt from the card by Shreyansh singh, licensed cc-by-nc-4.0.
Configuration
- Architecture
- Qwen2ForCausalLM
- Context length (tokens)
- 131,072
- Layers
- 28
- Hidden size
- 1,536
- Feed-forward size
- 8,960
- Attention heads
- 12
- Key/value heads
- 2
- Vocabulary size
- 151,936
- Model type
- qwen2
Identity and Version
- Repository
- shreyansh12183/vidhi-ai-1.5b-sovereign-masterpiece
- Publisher
- Shreyansh singh
- Task
- Not stated by the source
- Modality
- Other
- Library
- Not stated by the source
- Parameters
- 1.8B parameters
- Languages
- bns-2023, bsa-2023, gst, rbi
- Revision
- 34e650242239a7266f5b0dd2a7e1402b81dc19f4
- First published
- 2026-10-07
- Last updated
- 2026-10-08
Files and Weights
10 files, 4.7 GB in total. The weights are 2 files totalling 4.7 GB in gguf, safetensors.
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 3.6 GB | dd53115d53c4 |
| vidhi-ai-1.5b-q4_k_m.gguf | Weights | 1.1 GB | 20ca38b0215a |
| config.json | Configuration | 1.4 KB | — |
| generation_config.json | Configuration | 181 B | — |
| README.md | Documentation | 1.7 KB | — |
| chat_template.jinja | Other | 2.2 KB | — |
| colab_demo_vidhi_1_5b.ipynb | Other | 12.6 KB | — |
| .gitattributes | Repository | 1.6 KB | — |
| tokenizer.json | Tokenizer | 11.4 MB | c0d44f762212 |
| tokenizer_config.json | Tokenizer | 448 B | — |
License and Download
- License
- cc-by-nc-4.0
- Access
- Open weights, no gate
- Download size
- 4.7 GB
Released by Shreyansh singh through its official repository on Hugging Face. Read the license.
Built From
- Derived from deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
- Quantized from deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 4.7 GB |
| 16-bit | 3.6 GB |
| 8-bit | 1.8 GB |
| 4-bit | 0.9 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About vidhi-ai-1.5b-sovereign-masterpiece
How much GPU memory does vidhi-ai-1.5b-sovereign-masterpiece need?
About 4.3 GB at 16-bit and 1.1 GB at 4-bit: the weights (1.8B parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run vidhi-ai-1.5b-sovereign-masterpiece 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.
Can I use vidhi-ai-1.5b-sovereign-masterpiece commercially?
Not without separate permission. vidhi-ai-1.5b-sovereign-masterpiece is released under Creative Commons Attribution-NonCommercial 4.0. CC BY-NC 4.0 permits sharing and adapting with credit for non-commercial purposes only. Commercial use needs separate permission from the rights holder.
What is vidhi-ai-1.5b-sovereign-masterpiece's context length?
131,072 tokens, from the maximum position embeddings in its published configuration.