Search Results for "transfer function model" - Page 6
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Showing 136 open source projects for "transfer function model"
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Qwen-Image-Edit
An advanced bilingual image editing with semantic control
Qwen-Image-Edit is the image editing extension of Qwen-Image, a 20B parameter model that combines advanced visual and text-rendering capabilities for creative and precise editing. It leverages both Qwen2.5-VL for semantic control and a VAE Encoder for appearance control, enabling users to edit at both the content and detail level. The model excels at semantic edits like style transfer, object rotation, and novel view synthesis, while also handling precise appearance edits such as adding or removing elements without altering surrounding regions. ... -
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Nex-N2-Pro
Large agentic model for coding, tools, research, and execution
Nex-N2-Pro is Nex AGI’s larger open-source agentic model, built for real-world productivity, coding, deep research, tool calling, and long-horizon terminal execution. It uses the Nex-N2 “Agentic Thinking” framework, which connects requirement understanding, planning, implementation, environmental feedback, debugging, evaluation, and iteration into a single closed loop. The model is built on Qwen3.5-397B-A17B and is designed as the high-quality counterpart to Nex-N2-mini, trading higher compute needs for stronger reasoning and agent performance. ... -
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BLEURT-20-D12
Custom BLEURT model for evaluating text similarity using PyTorch
...Unlike standard BLEURT models from TensorFlow, this version is built from a custom PyTorch transformer library. It requires installing the model-specific library from GitHub to function properly. Once set up, it can be used to compute similarity scores with minimal code. BLEURT-20-D12 enables more flexible deployment in PyTorch-based workflows for evaluating language generation outputs. -
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Grok-2.5
Large-scale xAI model for local inference with SGLang, Grok-2.5
...Grok-2.5 supports advanced inference with multi-GPU configurations, requiring at least 8 GPUs with more than 40 GB of memory each for optimal performance. It integrates with the SGLang framework to enable serving, testing, and chat-style interactions. The model comes with a post-training architecture and requires the correct chat template to function properly. It is released under the Grok 2 Community License Agreement, encouraging community experimentation and responsible use. -
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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. ... -
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gpt-oss-120b
OpenAI’s open-weight 120B model optimized for reasoning and tooling
...Developers can control the reasoning level (low, medium, high) to balance speed and depth depending on the task. Released under the Apache 2.0 license, it enables both commercial and research applications. The model supports function calling, web browsing, and code execution, streamlining intelligent agent development. -
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Nex-N2-mini
Compact agentic model for coding, tools, and productivity tasks
Nex-N2-mini is an open-source agentic model from Nex AGI designed for real-world productivity, coding, tool use, deep research, and terminal-based execution. Built on Qwen3.5-35B-A3B-Base, it offers a lighter latency and deployment profile than Nex-N2-Pro while preserving the core Nex-N2 “Agentic Thinking” framework. This framework unifies requirement understanding, planning, code implementation, environmental feedback, debugging, evaluation, and iteration into a closed loop. -
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Mistral Large 3 675B Instruct 2512 NVFP4
Quantized 675B multimodal instruct model optimized for NVFP4
Mistral Large 3 675B Instruct 2512 NVFP4 is a frontier-scale multimodal Mixture-of-Experts model featuring 675B total parameters and 41B active parameters, trained from scratch on 3,000 H200 GPUs. This NVFP4 checkpoint is a post-training-activation quantized version of the original instruct model, created through a collaboration between Mistral AI, vLLM, and Red Hat using llm-compressor. It retains the same instruction-tuned behavior as the FP8 model, making it ideal for production... -
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Mistral Large 3 675B Instruct 2512
Frontier-scale 675B multimodal instruct MoE model for enterprise AIMis
Mistral Large 3 675B Instruct 2512 is a state-of-the-art multimodal granular Mixture-of-Experts model featuring 675B total parameters and 41B active parameters, trained from scratch on 3,000 H200 GPUs. As the instruct-tuned FP8 variant, it is optimized for reliable instruction following, agentic workflows, production-grade assistants, and long-context enterprise tasks. It incorporates a massive 673B-parameter language MoE backbone and a 2.5B-parameter vision encoder, enabling rich multimodal... -
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gpt-oss-20b
OpenAI’s compact 20B open model for fast, agentic, and local use
GPT-OSS-20B is OpenAI’s smaller, open-weight language model optimized for low-latency, agentic tasks, and local deployment. With 21B total parameters and 3.6B active parameters (MoE), it fits within 16GB of memory thanks to native MXFP4 quantization. Designed for high-performance reasoning, it supports Harmony response format, function calling, web browsing, and code execution. Like its larger sibling (gpt-oss-120b), it offers adjustable reasoning depth and full chain-of-thought visibility for better interpretability. ... -
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t5-small
T5-Small: Lightweight text-to-text transformer for NLP tasks
T5-Small is a lightweight variant of the Text-To-Text Transfer Transformer (T5), designed to handle a wide range of NLP tasks using a unified text-to-text approach. Developed by researchers at Google, this model reframes all tasks—such as translation, summarization, classification, and question answering—into the format of input and output as plain text strings. With only 60 million parameters, T5-Small is compact and suitable for fast inference or deployment in constrained environments. ...
