Search Results for "ogre-v1"
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Showing 8 open source projects for "ogre-v1"
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Stop vibe-debugging.AppSignal's MCP server hands Claude, Cursor, or Zed your real errors, traces, and the deploy that shipped them. AI writes the fix; you review the diff.
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IQuest-Coder-V1 Model Family
New family of code large language models (LLMs)
IQuest-Coder-V1 is a cutting-edge family of open-source large language models specifically engineered for code generation, deep code understanding, and autonomous software engineering tasks. These models range from tens of billions to smaller footprints and are trained on a novel code-flow multi-stage paradigm that captures how real software evolves over time — not just static code snapshots — giving them a deeper semantic understanding of programming logic. -
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DeepSeek V2
Strong, Economical, and Efficient Mixture-of-Experts Language Model
DeepSeek-V2 is the second major iteration of DeepSeek’s foundation language model (LLM) series. This version likely includes architectural improvements, training enhancements, and expanded dataset coverage compared to V1. The repository includes model weight artifacts, evaluation benchmarks across a broad suite (e.g. reasoning, math, multilingual), configuration files, and possibly tokenization / inference scripts. The V2 model is expected to support more advanced features like better context window handling, more efficient inference, better performance on challenging tasks, and stronger alignment with human feedback. ... -
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DeepSeek Coder V2
DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models
DeepSeek-Coder-V2 is the version-2 iteration of DeepSeek’s code generation models, refining the original DeepSeek-Coder line with improved architecture, training strategies, and benchmark performance. While the V1 models already targeted strong code understanding and generation, V2 appears to push further in both multilingual support and reasoning in code, likely via architectural enhancements or additional training objectives. The repository provides updated model weights, evaluation results on benchmarks (e.g. HumanEval, MultiPL-E, APPS), and new inference/serving scripts. ... -
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Jan-v1-edge
Jan-v1-edge: efficient 1.7B reasoning model optimized for edge devices
Jan-v1-edge is a lightweight agentic language model developed by JanHQ, designed for fast and reliable on-device execution. It is the second release in the Jan Family and was distilled from the larger Jan-v1 model, retaining strong reasoning and problem-solving capabilities while reducing its computational footprint. The model was refined through a two-stage post-training process: Supervised Fine-Tuning (SFT) to transfer knowledge from Jan-v1, followed by Reinforcement Learning with Verifiable Rewards (RLVR) to optimize reasoning, tool use, and correctness. ... -
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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bge-large-en-v1.5
BGE-Large v1.5: High-accuracy English embedding model for retrieval
BAAI/bge-large-en-v1.5 is a powerful English sentence embedding model designed by the Beijing Academy of Artificial Intelligence to enhance retrieval-augmented language model systems. It uses a BERT-based architecture fine-tuned to produce high-quality dense vector representations optimized for sentence similarity, search, and retrieval. This model is part of the BGE (BAAI General Embedding) family and delivers improved similarity distribution and state-of-the-art results on the MTEB... -
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bge-small-en-v1.5
Compact English sentence embedding model for semantic search tasks
BAAI/bge-small-en-v1.5 is a lightweight English sentence embedding model developed by the Beijing Academy of Artificial Intelligence (BAAI) as part of the BGE (BAAI General Embedding) series. Designed for dense retrieval, semantic search, and similarity tasks, it produces 384-dimensional embeddings that can be used to compare and rank sentences or passages. This version (v1.5) improves similarity distribution, enhancing performance without the need for special query instructions. The model... -
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bge-base-en-v1.5
Efficient English embedding model for semantic search and retrieval
bge-base-en-v1.5 is an English sentence embedding model from BAAI optimized for dense retrieval tasks, part of the BGE (BAAI General Embedding) family. It is a fine-tuned BERT-based model designed to produce high-quality, semantically meaningful embeddings for tasks like semantic similarity, information retrieval, classification, and clustering. This version (v1.5) improves retrieval performance and stabilizes similarity score distribution without requiring instruction-based prompts. With... -
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unidepth-v2-vitl14
Metric monocular depth estimation (vision model)
Estimates absolute (metric) depth from single RGB images, along with camera intrinsics and uncertainty. Designed to generalize across domains (zero-shot) using a self‑prompting camera module and pseudo-spherical prediction space.
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