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Showing 21 open source projects for "math"
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Qwen2.5-Math
A series of math-specific large language models of our Qwen2 series
Qwen2.5-Math is a series of mathematics-specialized large language models in the Qwen2 family, released by Alibaba’s QwenLM. It includes base models (1.5B / 7B / 72B parameters), instruction-tuned versions, and a reward model (RM) to improve alignment. Unlike its predecessor Qwen2-Math, Qwen2.5-Math supports both Chain-of-Thought (CoT) reasoning and Tool-Integrated Reasoning (TIR) for solving math problems, and works in both Chinese and English. -
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Tencent-Hunyuan-Large
Open-source large language model family from Tencent Hunyuan
...It aims to provide competitive capability with efficient deployment and inference. FP8 quantization support to reduce memory usage (~50%) while maintaining precision. High benchmarking performance on tasks like MMLU, MATH, CMMLU, C-Eval, etc. -
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Kimi K2
Kimi K2 is the large language model series developed by Moonshot AI
...The model family includes variants like a foundational base model that researchers can fine-tune for specific use cases and an instruct-optimized variant primed for general-purpose chat and agent-style interactions, offering flexibility for both experimentation and deployment. With its high-dimensional attention mechanisms and expert routing, Kimi-K2 excels across benchmarks in live coding, math reasoning, and problem solving. -
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Learn AI Engineering
Learn AI and LLMs from scratch using free resources
Learn AI Engineering is a learning path for AI engineering that consolidates high-quality, free resources across the full stack: math, Python foundations, machine learning, deep learning, LLMs, agents, tooling, and deployment. Rather than a loose bookmark list, it organizes topics into a progression so learners can start from fundamentals and move toward practical, production-oriented skills. It mixes courses, articles, code labs, and videos, emphasizing materials that teach both concepts and hands-on implementation. ... -
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Qwen
The official repo of Qwen chat & pretrained large language model
Qwen is a series of large language models developed by Alibaba Cloud, consisting of various pretrained versions like Qwen-1.8B, Qwen-7B, Qwen-14B, and Qwen-72B. These models, which range from smaller to larger configurations, are designed for a wide range of natural language processing tasks. They are openly available for research and commercial use, with Qwen's code and model weights shared on GitHub. Qwen's capabilities include text generation, comprehension, and conversation, making it a... -
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Qwen3-VL
Qwen3-VL, the multimodal large language model series by Alibaba Cloud
Qwen3-VL is the latest multimodal large language model series from Alibaba Cloud’s Qwen team, designed to integrate advanced vision and language understanding. It represents a major upgrade in the Qwen lineup, with stronger text generation, deeper visual reasoning, and expanded multimodal comprehension. The model supports dense and Mixture-of-Experts (MoE) architectures, making it scalable from edge devices to cloud deployments, and is available in both instruction-tuned and... -
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LLMs-from-scratch
Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
LLMs-from-scratch is an educational codebase that walks through implementing modern large-language-model components step by step. It emphasizes building blocks—tokenization, embeddings, attention, feed-forward layers, normalization, and training loops—so learners understand not just how to use a model but how it works internally. The repository favors clear Python and NumPy or PyTorch implementations that can be run and modified without heavyweight frameworks obscuring the logic. Chapters... -
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Hollama
A minimal LLM chat app that runs entirely in your browser
Hollama is a lightweight open-source chat application designed to run entirely within the browser while interacting with large language model servers. The project provides a minimal but powerful user interface for communicating with local or remote LLMs, including servers powered by Ollama or OpenAI-compatible APIs. Because the application runs as a static web interface, it does not require complex backend infrastructure and can be easily deployed or self-hosted. Hollama supports both... -
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LLM Datasets
Curated list of datasets and tools for post-training
...The repository aims to make datasets easy to inspect and transform, with scripts for downloading, deduping, cleaning, and converting to formats like JSONL that slot into training pipelines. It highlights instruction-tuning and conversation-style corpora while also pointing to code, math, or domain-specific sets for targeted capabilities. Quality is a recurring theme: examples and utilities help filter low-value samples, enforce length limits, and split train/validation consistently so results are comparable. Licensing and provenance are surfaced to encourage compliant usage and to guide dataset selection in commercial settings. ... -
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GLM-4
GLM-4 series: Open Multilingual Multimodal Chat LMs
GLM-4 is a family of open models from ZhipuAI that spans base, chat, and reasoning variants at both 32B and 9B scales, with long-context support and practical local-deployment options. The GLM-4-32B-0414 models are trained on ~15T high-quality data (including substantial synthetic reasoning data), then post-trained with preference alignment, rejection sampling, and reinforcement learning to improve instruction following, coding, function calling, and agent-style behaviors. The... -
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llm.c
LLM training in simple, raw C/CUDA
llm.c is a minimalist, systems-level implementation of a small transformer-based language model in C that prioritizes clarity and educational value. By stripping away heavy frameworks, it exposes the core math and memory flows of embeddings, attention, and feed-forward layers. The code illustrates how to wire forward passes, losses, and simple training or inference loops with direct control over arrays and buffers. Its compact design makes it easy to trace execution, profile hotspots, and understand the cost of each operation. Portability is a goal: it aims to compile with common toolchains and run on modest hardware for small experiments. ... -
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Skywork-R1V4
Skywork-R1V is an advanced multimodal AI model series
...Instead of retraining both language and vision models from scratch, the framework uses a lightweight visual projection layer that connects a pretrained vision backbone with a reasoning-capable language model. This design allows the model to analyze images while maintaining strong textual reasoning performance, enabling tasks such as solving visual math problems, interpreting scientific diagrams, and answering questions about images. -
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MiniMax-M1
Open-weight, large-scale hybrid-attention reasoning model
MiniMax-M1 is presented as the world’s first open-weight, large-scale hybrid-attention reasoning model, designed to push the frontier of long-context, tool-using, and deeply “thinking” language models. It is built on the MiniMax-Text-01 foundation and keeps the same massive parameter budget, but reworks the attention and training setup for better reasoning and test-time compute scaling. Architecturally, it combines Mixture-of-Experts layers with lightning attention, enabling the model to... -
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DeepSeek LLM
DeepSeek LLM: Let there be answers
The DeepSeek-LLM repository hosts the code, model files, evaluations, and documentation for DeepSeek’s LLM series (notably the 67B Chat variant). Its tagline is “Let there be answers.” The repo includes an “evaluation” folder (with results like math benchmark scores) and code artifacts (e.g. pre-commit config) that support model development and deployment. According to the evaluation files, DeepSeek LLM 67B Chat achieves strong performance on math benchmarks under both chain-of-thought (CoT) and tool-assisted reasoning modes. The model is trained from scratch, reportedly on a vast multilingual + code + reasoning dataset, and competes with other open or open-weight models. ... -
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ToRA
Tool-integrated Reasoning LLM Agents
ToRA is an open-source framework developed by Microsoft for building tool-integrated reasoning agents powered by large language models. The project focuses on improving the ability of AI systems to solve complex mathematical and analytical problems by combining natural language reasoning with external computational tools. Instead of relying solely on text generation, the system dynamically invokes tools such as symbolic solvers or programming libraries when deeper computation is required.... -
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BIG-bench
Beyond the Imitation Game collaborative benchmark for measuring
BIG-bench (Beyond the Imitation Game Benchmark) is a large, collaborative benchmark suite designed to probe the capabilities and limitations of large language models across hundreds of diverse tasks. Rather than focusing on a single metric or domain, it aggregates many hand-authored tasks that test reasoning, commonsense, math, linguistics, ethics, and creativity. Tasks are intentionally heterogeneous: some are multiple-choice with exact scoring, others are free-form generation judged by model-based or human evaluation. The suite provides a common JSON task format and an evaluation harness so research groups can contribute new tasks and reproduce results consistently. ... -
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ThoughtSource
A central, open resource for data and tools
ThoughtSource is a central, open resource and community centered on data and tools for chain-of-thought reasoning in large language models (Wei 2022). Our long-term goal is to enable trustworthy and robust reasoning in advanced AI systems for driving scientific research and medical practice. -
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ChatLLM Web
Chat with LLM like Vicuna totally in your browser with WebGPU
Chat with LLM like Vicuna totally in your browser with WebGPU, safely, privately, and with no server. Powered By web-llm. To use this app, you need a browser that supports WebGPU, such as Chrome 113 or Chrome Canary. Chrome versions ≤ 112 are not supported. You will need a GPU with about 6.4GB of memory. If your GPU has less memory, the app will still run, but the response time will be slower. The first time you use the app, you will need to download the model. For the Vicuna-7b model that... -
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LLaMA.go
llama.go is like llama.cpp in pure Golang
llama.go is like llama.cpp in pure Golang. The code of the project is based on the legendary ggml.cpp framework of Georgi Gerganov written in C++ with the same attitude to performance and elegance. Both models store FP32 weights, so you'll needs at least 32Gb of RAM (not VRAM or GPU RAM) for LLaMA-7B. Double to 64Gb for LLaMA-13B. -
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Grade School Math
8.5K high quality grade school math problems
The grade-school-math repository (sometimes called GSM8K) is a curated dataset of 8,500 high-quality grade school math word problems intended for evaluating mathematical reasoning capabilities of language models. It is structured into 7,500 training problems and 1,000 test problems. These aren’t trivial exercises — many require multi-step reasoning, combining arithmetic operations, and handling intermediate steps (e.g. -
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ZAYA1-8B
Efficient MoE reasoning model for coding and math workloads
ZAYA1-8B is a compact Mixture-of-Experts reasoning model developed by Zyphra, designed to deliver unusually high intelligence density with fewer than 1 billion active parameters. The model contains 8.4B total parameters with around 760M active during inference, allowing it to achieve strong reasoning, mathematics, and coding performance while remaining lightweight enough for efficient local or on-device deployment. ZAYA1-8B is optimized for long-form reasoning and test-time compute...
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