Search Results for "linux software development" - Page 3
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Showing 68 open source projects for "linux software development"
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Hy3
Open code agent for Lean 4 proofs and formal software verification
Leanstral 1.5 119B A6B is an open-source code agent model from Mistral AI designed specifically for Lean 4, a proof assistant used to express and verify complex mathematical objects and formal software specifications. Built as part of the Mistral Small 4 family, it combines multimodal capabilities with an efficient Mixture-of-Experts architecture containing 119B total parameters and 6.5B activated per token. The model uses 128 experts with four active for each token and supports a 256K-token... -
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Leanstral 1.5
Open code agent for Lean 4 proofs and formal software verification
Leanstral 1.5 119B A6B is an open-source code agent model from Mistral AI designed specifically for Lean 4, a proof assistant used to express and verify complex mathematical objects and formal software specifications. Built as part of the Mistral Small 4 family, it combines multimodal capabilities with an efficient Mixture-of-Experts architecture containing 119B total parameters and 6.5B activated per token. The model uses 128 experts with four active for each token and supports a 256K-token... -
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MiniMax-M2.7
Self-evolving AI model for agents, coding, and complex workflows
MiniMax-M2.7 is a large-scale open-weight language model designed for advanced agent-based workflows, professional software engineering, and complex productivity tasks. With 229B parameters, it introduces a self-evolution framework in which the model actively improves its own capabilities by updating memory, generating skills, and iterating through reinforcement learning experiments. This process enables it to autonomously refine systems, achieving measurable performance gains such as a 30%... -
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Laguna XS.2
Open agentic coding model optimized for local deployment
Laguna XS.2 is Poolside’s first open-weight Mixture-of-Experts model designed specifically for agentic coding and long-horizon software engineering tasks. The model contains 33B total parameters with only 3B activated per token, allowing it to deliver strong coding performance while remaining efficient enough to run locally on modern consumer hardware. It uses a hybrid attention architecture that combines Sliding Window Attention and global attention layers, reducing memory requirements and... -
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wav2vec2-large-xlsr-53-russian
Russian ASR model fine-tuned on Common Voice and CSS10 datasets
wav2vec2-large-xlsr-53-russian is a fine-tuned automatic speech recognition (ASR) model based on Facebook’s wav2vec2-large-xlsr-53 and optimized for Russian. It was trained using Mozilla’s Common Voice 6.1 and CSS10 datasets to recognize Russian speech with high accuracy. The model operates best with audio sampled at 16kHz and can transcribe Russian speech directly without a language model. It achieves a Word Error Rate (WER) of 13.3% and Character Error Rate (CER) of 2.88% on the Common... -
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Qwable-v1
Agentic coding model combining Opus reasoning and Fable tools
Qwable-v1 is an open-weight agentic coding model created through a chained distillation process based on Qwen3.6-35B-A3B. The model combines two distinct training stages: first, it was fine-tuned on reasoning traces derived from Claude Opus 4.7 to improve structured reasoning, and then further trained on Claude Fable-5 agentic tool-use traces to develop autonomous coding and tool-calling behavior. The result is a 35B Mixture-of-Experts model with only 3B active parameters that can switch... -
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gpt-oss-120b
OpenAI’s open-weight 120B model optimized for reasoning and tooling
GPT-OSS-120B is a powerful open-weight language model by OpenAI, optimized for high-level reasoning, tool use, and agentic tasks. With 117B total parameters and 5.1B active parameters, it’s designed to fit on a single H100 GPU using native MXFP4 quantization. The model supports fine-tuning, chain-of-thought reasoning, and structured outputs, making it ideal for complex workflows. It operates in OpenAI’s Harmony response format and can be deployed via Transformers, vLLM, Ollama, LM Studio,... -
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FLUX.1-Krea-dev
Text-to-image model optimized for artistic quality and safe generation
FLUX.1-Krea-dev is a 12 billion parameter rectified flow transformer for text-to-image generation, developed by Black Forest Labs in collaboration with Krea. It delivers aesthetic, high-quality outputs focused on photography and visual coherence, making it a strong competitor to closed-source models. Trained using guidance distillation, it offers efficient inference while preserving creative fidelity. The model is distributed under a non-commercial license, with conditions to prevent misuse... -
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Hy3 preview
Efficient MoE model for reasoning, coding, and AI agent workflows
Hy3 preview is Tencent Hunyuan’s latest open-weight Mixture-of-Experts language model, designed for advanced reasoning, coding, instruction following, and autonomous agent workflows. It is the first model built on Tencent’s rebuilt training infrastructure and introduces significant improvements in context learning, software engineering, and tool-based task execution. The model features 295B total parameters with only 21B activated during inference, plus a dedicated 3.8B Multi-Token... -
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DeepSeek-V3.1-Terminus
685B model with improved agents and consistency
DeepSeek-V3.1-Terminus is an updated release in the DeepSeek-V3.1 series, maintaining the original model’s large-scale reasoning and generative capabilities while addressing several key user-reported issues. It improves language consistency, reducing mixed Chinese-English outputs and eliminating abnormal characters, enhancing reliability in multilingual scenarios. The update also refines agentic capabilities, especially for the Code Agent and Search Agent, leading to better tool integration... -
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LongCat-2.0
Trillion-parameter MoE model for coding and million-token reasoning
LongCat-2.0 is Meituan’s flagship open-weight Mixture-of-Experts language model designed for frontier-scale coding, reasoning, and autonomous agent workflows. It features 1.6 trillion total parameters with approximately 48 billion activated per token, combining high capability with efficient sparse inference. The model was pretrained on more than 35 trillion tokens and trained entirely on a large-scale cluster of domestically developed AI accelerators, demonstrating stable frontier-scale... -
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Qwen3.6-27B
Dense multimodal Qwen model for coding, agents, and long context
Qwen3.6-27B is an open-weight multimodal model built to deliver strong real-world coding, agent, and long-context performance in a dense 27B-parameter architecture. It combines a causal language model with a vision encoder and supports text, image, and video inputs, making it suitable for both software workflows and broader multimodal tasks. The model emphasizes stability and practical developer utility, with major improvements in agentic coding, frontend generation, and repository-level... -
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Qwen3.8-2.4T-A95B
Massive 2.4T MoE model for coding, agents, research, and reasoning
Qwen3.8-2.4T-A95B is Qwen’s largest open-weight model and the first Qwen-Max-class model released openly, targeting advanced coding, professional work, research, and long-horizon agentic tasks. It uses a massive Mixture-of-Experts architecture with 2.4 trillion total parameters while activating 95B per token, combining Gated DeltaNet and attention layers across 512 experts. The model emphasizes reliable autonomous execution, including stronger planning, environment feedback handling, and... -
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Inkling-Small
Efficient multimodal MoE model for coding, tools, and reasoning
Inkling-Small is an open-weight general-purpose multimodal model from Thinking Machines Lab, designed for agentic systems, coding assistants, chatbots, retrieval workflows, and natural-language applications. It accepts text, images, and audio as input and produces text output, with multilingual and multi-programming-language capabilities. The model uses a sparse Mixture-of-Experts architecture with 276B total parameters and 12B active per token, enabling strong performance with lower... -
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Inkling
Frontier multimodal MoE model for coding and AI agent workflows
Inkling is Thinking Machines Lab’s first open-weight flagship multimodal Mixture-of-Experts model, designed for advanced reasoning, coding, and autonomous agent workflows. It contains 975B total parameters with 41B active parameters per token, balancing frontier-level capability with efficient sparse inference. The model natively processes text, images, audio, and video within a unified architecture and supports an exceptionally large 1 million token context window for long-document... -
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MiMo-V2.5-Pro
Flagship MoE model for long-context agents and complex coding
MiMo-V2.5-Pro is Xiaomi’s flagship Mixture-of-Experts (MoE) model built for the most demanding agentic, software engineering, and long-horizon reasoning tasks. It features approximately 1.02 trillion total parameters with 42B activated per inference, balancing extreme capability with efficient execution. The model supports a 1 million token context window, enabling it to maintain coherence across long workflows involving thousands of tool calls and multi-step reasoning chains.... -
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Smaug Flash
304B MoE model optimized for agentic coding and long-running workflows
Smaug Flash is an open-weight agentic coding model from Abacus.AI, fine-tuned from DeepSeek-V4-Flash-0731 to improve autonomous software engineering, tool use, and long-running agent workflows. It uses a 304B-parameter Mixture-of-Experts architecture with 43 layers, 256 routed experts, six selected experts per token, and one shared expert. Its attention system combines Multi-Head Latent Attention with a sparse token indexer, while DSpark multi-token prediction provides speculative decoding... -
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Kimi K2.7 Code
Coding-focused Kimi model for long-horizon agent workflows
Kimi K2.7 Code is a coding-focused agentic model built on Kimi K2.6, designed for long-horizon software engineering, autonomous coding workflows, and complex tool-based execution. It improves end-to-end task completion across real-world programming scenarios while reducing thinking-token usage by about 30% compared with K2.6. Architecturally, it uses a 1T-parameter Mixture-of-Experts design with 32B activated parameters, 61 layers, 384 experts, a 256K-token context window, and a MoonViT...
