DeepSeek R1

Top Builders

Explore the top contributors showcasing the highest number of app submissions within our community.

DeepSeek R1

General
Release date	2023
Author	DeepSeek
Website	DeepSeek Models
Repository	https://github.com/deepseek-ai
Type	Foundation Language Model

The DeepSeek R1 model provides a lightweight yet powerful solution for basic natural language processing tasks. Optimized for speed and efficiency, this model delivers reliable performance for text classification, entity recognition, and simple text generation.

Key Features

4K Token Context Window: Handles medium-length documents effectively
Multi-Lingual Support: Base capabilities in 5 major languages
Low Resource Requirements: Runs efficiently on standard hardware
Fine-Tuning Ready: Compatible with common ML frameworks

Useful Links

👉 [Deepseek R1 Paper] (https://github.com/deepseek-ai/DeepSeek-R1/blob/main/DeepSeek_R1.pdf) 👉 [Access on Hugging Face] (https://huggingface.co/deepseek-ai/DeepSeek-R1) 👉 [Try Deepseek] (https://deepseek.com) 👉 [API Documentation] (https://api-docs.deepseek.com/)

Edit on GitHub

Deepseek DeepSeek R1 AI technology Hackathon projects

Discover innovative solutions crafted with Deepseek DeepSeek R1 AI technology, developed by our community members during our engaging hackathons.

Frugal Router: Cost-Aware Multi-Model AI Agent

Enterprises don't need a frontier model for every request. This agent detects which of the 8 task categories (factual, math reasoning, sentiment, summarization, NER, code debugging, logical/deductive reasoning, code generation) a prompt belongs to, then routes accordingly: code and constraint-satisfaction puzzles go straight to a frontier Fireworks model since small models are unreliable there and a failed cheap attempt costs tokens in addition to the retry; sentiment/NER/factual questions try a bundled 1.5B local model first (zero Fireworks tokens) and fall back only when it isn't confident. The difficulty classifier for math/factual ambiguity is trained on real data — MATH's Level 1-5 labels and RouteLLM's Chatbot Arena win/loss judgments — rather than hand-tuned guesses. Model tiers are built dynamically from ALLOWED_MODELS at container startup since the permitted model list isn't published until launch day.

RocmPilot Studio — CUDA→AMD Migration Cockpit

AMD's MI300X wins on price/performance, but what keeps enterprises on CUDA isn't silicon — it's migration uncertainty. A typical AI codebase is CUDA-first top to bottom: hardcoded cuda device logic, nvidia/cuda base images, CUDA-pinned wheels, and the part everyone fears — custom kernels with warp-level assumptions. Tools like hipify translate the easy majority, but nobody can tell you up front how ready a whole codebase is, or where the hard work hides. RocmPilot Studio is the command center for that migration. Paste a repo URL and it runs a six-stage pipeline: SCAN → PLAN → PATCH → VALIDATE → REPORT. • Scan: a deterministic engine (no LLM — findings can't be hallucinated) detects blockers with file, line and severity, plus a kernel-risk classifier that names the hard 20% in AMD vocabulary: 32-lane warp shuffles vs 64-lane wavefronts, warpSize hazards, WMMA→rocWMMA, CUTLASS→Composable Kernel, cuBLAS→hipBLAS, NCCL→RCCL — at repository scale. • Plan: a multi-model agent orchestra (all AMD-hosted via Fireworks AI) — DeepSeek drafts the prioritized plan, then GLM, a different model, independently critiques it before you see it. Live-streamed agent trace in the UI. • Patch: safe auto-fixes as patch.diff, plus a ready-to-run ROCm Dockerfile, smoke test and benchmark — every change explained from the real diff. Download the fully patched repo as a zip. • Validate: smoke test + benchmark executed on real AMD hardware (passed on Radeon gfx1100, ROCm 7.2, PyTorch 2.9.1, ~0.84 ms inference; replays are always labeled). On failure, a research agent (Kimi) returns a cited, RAG-grounded fix from a ROCm/HIP knowledge base in Qdrant. • Report: an honest, blocker-weighted readiness score (sample repo: 37 → 72 → 86) and an exportable report. Fallback-safe at every layer — no API key, no vector DB, no GPU still yields a working, honest run. 160+ deterministic tests. Built by a two-person team with a protected-branch PR workflow.

NexusOps – Autonomous Industrial Maintenance

NexusOps is an autonomous industrial maintenance platform that helps manufacturing teams reduce machine downtime through intelligent multi-agent orchestration. Modern industrial environments generate vast amounts of operational data, yet diagnosing failures, identifying root causes, checking spare part availability, and preparing maintenance procedures often remain manual, time-consuming processes. These delays lead to increased operational costs, production losses, and extended equipment downtime. NexusOps addresses these challenges by coordinating specialized AI agents that collaborate throughout the maintenance lifecycle. When an operator reports an incident, a triage agent evaluates the issue and initiates the appropriate workflow. A telemetry agent analyzes machine sensor data to detect anomalies, while a diagnostics agent combines real-time telemetry with Retrieval-Augmented Generation (RAG) to retrieve relevant maintenance manuals, SOPs, and historical troubleshooting knowledge, enabling accurate root-cause analysis. An inventory agent verifies the availability of required replacement parts before maintenance begins, ensuring engineers can execute repairs without unexpected delays. A supervisor agent consolidates the outputs from all agents, generates a detailed maintenance plan with repair procedures, safety recommendations, required tools, estimated repair time, and confidence scores, then routes the plan for mandatory human approval before execution.

Gradelytics: AI Syllabus & VibeGrading

Gradelytics is an advanced, startup-ready academic co-pilot designed to streamline course preparation and objective evaluation for educators. Built as a high-fidelity full-stack MVP, the platform directly addresses administrative burnout in modern education through two core agentic workflows: the Syllabus Architect and the VibeGrading Simulator. Pundits can instantly map granular learning competencies into structured weekly timelines or execute deep, semantic evaluations of student essays based on completely customizable rubric constraints. Architected for flexibility and production-grade reliability, Gradelytics implements a powerful Hybrid AI Inference engine. For scalable cloud operations, the application securely orchestrates high-throughput serverless models via the Fireworks AI API gateway. To ensure absolute data privacy for enterprise and on-premise institutional rollouts, the reasoning engine is fully optimized to run locally on native AMD hardware infrastructure. The pipeline was benchmarked and validated on AMD RDNA 3 (gfx1100) hardware with 51.5 GB VRAM utilizing PyTorch bindings over the ROCm compute platform. Featuring robust JSON contract parsing, dynamic collapsible sidebar UI states, live analytics charts, and client-side markdown or print-ready PDF reporting, Gradelytics balances sophisticated enterprise architecture with maximum practical market potential. The entire ecosystem is fully containerized, making it instantly ready for deployment and continuous localized iterations.

ROCm Migration Copilot: CUDA-to-ROCm Triage

Migrating a PyTorch or Hugging Face training pipeline to AMD ROCm hardware is harder than it should be — not because of missing documentation, but because the friction lives in a layer nobody currently owns. AMD's own HIPIFY tool translates CUDA/C++ kernel code excellently, but most ML engineers never touch a .cu file; they write device="cuda", import bitsandbytes, and call flash_attn without knowing which of those choices will silently fail or degrade on ROCm. ROCm Migration Copilot closes that gap. It statically scans Python training scripts using libcst, matches code against a versioned, sourced rule base covering constructs like hardcoded CUDA device strings, NCCL backend calls, and known-unsupported libraries (bitsandbytes, Flash-Attention, AWQ), and produces a severity-ranked report — distinguishing issues that can be safely auto-fixed from ones that need a human engineer's judgment. Rather than asking a generic LLM to blindly rewrite CUDA code (which hallucinates fixes against a support matrix it doesn't actually know), we put a deterministic, cited rule engine in front of the model, so every flagged issue is traceable to a real source, not a guess. Built for the AMD Developer Hackathon, targeting real Hugging Face training scripts as test cases, with a CLI-first workflow suited for CI pipelines and pre-migration audits alike.

Looma Assistant: Concurrent AI Orchestration Agent

Looma Assistant is a general-purpose, event-driven AI agent designed to excel in high-concurrency environments. Unlike traditional linear task processors that suffer from synchronous bottlenecks, Looma Assistant utilizes an advanced supervision tree built on Akka Actors. By decoupling task ingestion from execution, the system dynamically analyzes incoming workloads and maps them directly to specific Akka sub-workers tailored to the distinct task categories outlined in the guidelines—such as code debugging, logical reasoning, and code generation. A primary driver of our operational cost efficiency is the intelligent restriction of remote token consumption. Looma Assistant configures rigid max_tokens boundaries for each individual worker actor, ensuring no single processing branch can over-consume resources or generate runaway completions. By enforcing these tight, category-specific caps alongside proactive prompt sanitization, we drastically minimize overall API token usage while maintaining data integrity. This granular control allows our Akka Actors to execute bulk datasets concurrently, making Looma Assistant an incredibly resource-conscious and stable engine for enterprise-scale asynchronous intelligence workflows.