# SRA-RiskGate-4B by Sriram Ramakrishnan: Open-Weight Model Source: https://savrn.com/models/sra-riskgate-4b Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve SRA-RiskGate-4B (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](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 8-bit | 4.0 GB | 4.8 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 4-bit | 2.0 GB | 2.4 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/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](https://savrn.com/ai-index/pricing/gpus), read Oct 7, 2026. [SRA-RiskGate-4B on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/sra-riskgate-4b/gpus) ## Model Card By Sriram Ramakrishnan, published under apache-2.0, revision 081be9a6b0c0. SRA-RiskGate-4B is an autonomous risk scoring, compliance verification, and dispute adjudication model fine-tuned on top of Qwen/Qwen3-4B-Instruct-2507. It is engineered for both ends of a stablecoin payment's operational lifecycle: - Pre-Settlement Risk Gate: Ingests payment requests (x402 requests, EIP-3009 authorizations, or standard ERC-20 transfers), policy constraints, and deterministic verification tool outputs (sanctions hits, attestation checks, signature status) to return structured approve / hold / reject decisions with explicit flags and required actions. - Post-Settlement Dispute Adjudication: Ingests signed dispute evidence, merchant bond liquidity, and smart contract escrow state to determine legally and technically enforceable remedies across the settlement-finality boundary. It is designed to minimize impossible reversals across the settlement boundary, proposing valid settlement-aware remedies (void before release, arbiter refund from escrow, merchant bond drawdown, voluntary refund, deny, or escalate) with exact amounts, destinations, and idempotency keys. ### Autonomous Agent Firewall (Coinbase AgentKit Integration) Autonomous on-chain agents can hallucinate payment transfers, sign malformed calldata, or trigger catastrophic transactions during market depegs. [Read the full model card (1,696 words)](https://savrn.com/models/sra-riskgate-4b/card) ## Configuration Architecture Qwen3ForCausalLM Context length (tokens) 262,144 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 RoPE base 5,000,000 Model type qwen3 ## Identity and Version Repository sriram1983007/SRA-RiskGate-4B Publisher Sriram Ramakrishnan Task Text generation Modality Text Library transformers Parameters 4B parameters Languages en Revision 081be9a6b0c05bc8e0844b53badd2f7e4c160150 First published 2026-10-03 Last updated 2026-10-07 ## Files and Weights 8 files, 8.1 GB in total. The weights are 1 file totalling 8.0 GB in safetensors. Weights1 file · 8.0 GB Configuration2 files · 1.8 KB Tokenizer2 files · 11.4 MB Documentation1 file · 19.9 KB Other1 file · 2.6 KB Repository1 file · 1.6 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 8.0 GB | f13c3ed59c31 | | config.json | Configuration | 1.6 KB | — | | generation_config.json | Configuration | 160 B | — | | README.md | Documentation | 19.9 KB | — | | chat_template.jinja | Other | 2.6 KB | — | | .gitattributes | Repository | 1.6 KB | — | | tokenizer.json | Tokenizer | 11.4 MB | be75606093db | | tokenizer_config.json | Tokenizer | 407 B | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 8.0 GB [Download from Sriram Ramakrishnan](https://huggingface.co/sriram1983007/SRA-RiskGate-4B) Released by Sriram Ramakrishnan through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0). ## Built From - Derived from [Qwen/Qwen3-4B-Instruct-2507](https://savrn.com/models/qwen3-4b-instruct-2507) - Trained on (disclosed) sriram1983007/sra-stablecoin-risk-bench ## Evaluations Each result is shown as reported, with the conditions its reporter stated. None is a SAVRN measurement. A comparison lines two results up only when their configuration, unit and setup are all stated and identical. | Benchmark | Conditions | Result | Reported by | Revision | Date | | --- | --- | --- | --- | --- | --- | | SRA Stablecoin Risk Bench | Configuration benchmarkTask Stablecoin dispute adjudicationMetric Dispute outcome accuracyComparison conditions not established | 0.7782 | [sriram1983007](https://huggingface.co/sriram1983007/SRA-RiskGate-4B) Publisher reported | Evaluated revision not stated | — | | SRA Stablecoin Risk Bench | Configuration benchmarkTask Stablecoin dispute adjudicationMetric Dispute scoreComparison conditions not established | 0.8322 | [sriram1983007](https://huggingface.co/sriram1983007/SRA-RiskGate-4B) Publisher reported | Evaluated revision not stated | — | | SRA Stablecoin Risk Bench | Configuration benchmarkTask Stablecoin dispute adjudicationMetric Impossible remedy rate (lower is better)Comparison conditions not established | 0.0038 | [sriram1983007](https://huggingface.co/sriram1983007/SRA-RiskGate-4B) Publisher reported | Evaluated revision not stated | — | | SRA Stablecoin Risk Bench | Configuration benchmarkTask Stablecoin payment risk gatingMetric Injected payments approved (lower is better)Comparison conditions not established | 0.058 | [sriram1983007](https://huggingface.co/sriram1983007/SRA-RiskGate-4B) Publisher reported | Evaluated revision not stated | — | | SRA Stablecoin Risk Bench | Configuration benchmarkTask Stablecoin payment risk gatingMetric Over-blocking rate (lower is better)Comparison conditions not established | 0 | [sriram1983007](https://huggingface.co/sriram1983007/SRA-RiskGate-4B) Publisher reported | Evaluated revision not stated | — | | SRA Stablecoin Risk Bench | Configuration benchmarkTask Stablecoin payment risk gatingMetric Risk-gate decision accuracyComparison conditions not established | 0.9949 | [sriram1983007](https://huggingface.co/sriram1983007/SRA-RiskGate-4B) Publisher reported | Evaluated revision not stated | — | | SRA Stablecoin Risk Bench | Configuration benchmarkTask Stablecoin payment risk gatingMetric SRA composite scoreComparison conditions not established | 0.9134 | [sriram1983007](https://huggingface.co/sriram1983007/SRA-RiskGate-4B) Publisher reported | Evaluated revision not stated | — | | SRA Stablecoin Risk Bench | Configuration benchmarkTask Stablecoin payment risk gatingMetric Unsafe approval rate (lower is better)Comparison conditions not established | 0.0047 | [sriram1983007](https://huggingface.co/sriram1983007/SRA-RiskGate-4B) Publisher reported | Evaluated revision not stated | — | | SRA Stablecoin Risk Bench | Configuration benchmarkTask Stablecoin dispute adjudicationMetric Wrongful refund rate (lower is better)Comparison conditions not established | 0.022 | [sriram1983007](https://huggingface.co/sriram1983007/SRA-RiskGate-4B) Publisher reported | Evaluated revision not stated | — | ## 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. ## Built on This Model - Quantized from[SRA-RiskGate-4B-Q4_K_M-GGUF](https://savrn.com/models/sra-riskgate-4b-q4-k-m-gguf) - Derived from[SRA-RiskGate-4B-Q4_K_M-GGUF](https://savrn.com/models/sra-riskgate-4b-q4-k-m-gguf) ## Questions About SRA-RiskGate-4B ### How much GPU memory does SRA-RiskGate-4B 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 SRA-RiskGate-4B 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 SRA-RiskGate-4B commercially? Yes. SRA-RiskGate-4B is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors. ### What is SRA-RiskGate-4B's context length? 262,144 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Text generation ### [Qwen3-4B](https://savrn.com/models/qwen3-4b) [Qwen](https://savrn.com/model-publishers/qwen) Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features: - Uniquely support of seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue) within single model, ensuring optimal performance across various scenarios. - Significantly enhancement in its reasoning capabilities, surpassing previous QwQ (in thinking mode) and… Open weights apache-2.0 4B parameters 40,960 tokens transformers [View model](https://savrn.com/models/qwen3-4b) Model · Text generation ### [Qwen3-4B-Instruct-2507](https://savrn.com/models/qwen3-4b-instruct-2507) [Qwen](https://savrn.com/model-publishers/qwen) We introduce the updated version of the Qwen3-4B non-thinking mode, named Qwen3-4B-Instruct-2507, featuring the following key enhancements: - Significant improvements in general capabilities, including instruction following, logical reasoning, text comprehension, mathematics, science, coding and tool usage. - Substantial gains in long-tail knowledge coverage across multiple languages. - Markedly better alignment with user preferences in subjective and open-ended tasks, enabling more helpful responses and higher-quality text generation. - Enhanced capabilities in 256K long-context understanding. 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Root files are the veRL-exported Hugging Face inference model (BF16). originalcheckpoint/ preserves the exact original FSDP parameter checkpoint and tokenizer/configuration files. Optimizer state, training data, responses, rubrics, infrastructure configuration, and credentials are not included. The original is retained because export… Open weights apache-2.0 4B parameters 262,144 tokens transformers [View model](https://savrn.com/models/qwen3-4b-rar-medicine-onlinerubrics-seed11-step-003) Model · Text generation ### [kas-4b](https://savrn.com/models/kas-4b) [Kashav Piya](https://savrn.com/model-publishers/kpiya) A fine-tuned decision engine based on Qwen/Qwen3-4B, submitted to the Decision Index v0.2.1 leaderboard. Decision Index: 40.1 (raw 54.18) — above Tev1-4B (29.2), within 6 points of Scion v4 9B (45.9). Per-area breakdown against all 4B submissions on Decision Index 0.2.1. kas-4b leads the 4B tier on: - Knowledge & Reasoning (39.5) — highest of all 4B models; higher LoRA rank (r64) likely helps on harder reasoning tasks - Arts & Human Taste (37.7) — highest of all 4B models by a wide margin - Language Understanding (35.2) — 13–25 points behind ezjev and Nox; language-heavy fine-tuning data favors those models - Tools & Automation (49.0) — behind ezjev (69.9) and Nox (60.1) Profile: the most… Open weights apache-2.0 4B parameters 40,960 tokens [View model](https://savrn.com/models/kas-4b) ## Sriram Ramakrishnan [All models and datasets](https://savrn.com/model-publishers/sriram1983007) ## Versions - [081be9a6b0c0](https://savrn.com/models/sra-riskgate-4b/versions/081be9a6b0c0) · current 2026-10-07 - [96665a2fba74](https://savrn.com/models/sra-riskgate-4b/versions/96665a2fba74) 2026-10-06 ## Explore More - [All text generation models](https://savrn.com/models/tasks/text-generation) - [All models under apache-2.0](https://savrn.com/models/licenses/apache-2-0) - [Model comparisons](https://savrn.com/models/comparisons) - [The model directory](https://savrn.com/models) - [Open model prices by host](https://savrn.com/ai-index/pricing/open-models) ## Source - Repository metadata, read 2026-10-07. - [Hugging Face record](https://huggingface.co/sriram1983007/SRA-RiskGate-4B) - [How the hub is built](https://savrn.com/model-hub/methodology)