Jet-Nemotron-2B
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Mellum-4b-base
JetBrains基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Mellum-4b-sft-all
JetBrains基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Mellum2.1-12B-A2.5B-Thinking
JetBrains> [!Note] > Mellum2.1 is a thinking model. Use it for complex agentic tasks, such as working in a repository, running commands, and calling tools, and for hard non-agentic problems in coding, math, and reasoning. Mellum2.1 is the next version of Mellum2 Thinking. The architecture is unchanged (a 12B mixture-of-experts model with 2.5B active parameters), and almost all of the work for this version went into post-training, primarily reinforcement learning (RL): As a result, Mellum2.1 handles agentic tasks much better than Mellum2. After millions of sandboxed runs in real environments during training, it explores a codebase, edits files, and checks its own changes.
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View details ↗Mellum2.1-12B-A2.5B-Thinking-GGUF
JetBrainsThis repository contains **GGUF** builds of `JetBrains/Mellum2.1-12B-A2.5B-Thinking` in every quantization we publish, ready to run with `llama.cpp`, Ollama, LM Studio, and other GGUF-compatible runtimes. Each variant is one file; download the one you need. The repository also ships the model's **multi-token prediction (MTP) head** for speculative decoding in llama.cpp; see Speculative decoding with the MTP head. Mellum2.1 Thinking is a Mixture-of-Experts reasoning model (64 experts, 8 activated per token, 131,072-token context) that emits its chain of thought inside ` ... ` blocks before the final answer. For the full model description, evaluation results, and architecture details, see the original model card: **JetBrains/Mellum2.1-12B-A2.5B-Thinking**. KL divergence and top-token agreement are measured against the BF16 logits on Wikitext-2 (`n_ctx=512`); lower KLD / higher agreement means closer to the unquantized model.
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View details ↗Mellum2-12B-A2.5B-Base
JetBrains> [!Note] > Use this checkpoint as the starting point for your own fine-tuning, alignment, or domain adaptation on top of the long-context base. For instruction-following or reasoning tasks out of the box, use Instruct or Thinking instead. The model uses a Mixture-of-Experts architecture with 64 experts and activates 8 experts per token. It uses a combination of sliding-window and full attention layers, with a context length of 131,072 tokens. This is the long-context base, produced from `Mellum2-12B-A2.5B-Base-Pretrain` by a layer-selective YaRN extension stage that re-maps RoPE frequencies on the global-attention layers only. It is the shared starting point for the released Instruct and Thinking variants.
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View details ↗Mellum2-12B-A2.5B-Base-Pretrain
JetBrains基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Mellum2-12B-A2.5B-Instruct
JetBrains> [!Note] > Use this model when you want direct, low-latency answers without an explicit chain of thought — interactive chat, code assistance, tool use, and instruction following. If you need explicit reasoning before the answer (complex debugging, planning, multi-step agentic flows), use Thinking instead. The model uses a Mixture-of-Experts architecture with 64 experts and activates 8 experts per token. It uses a combination of sliding-window and full attention layers, with a context length of 131,072 tokens. It is produced from `Mellum2-12B-A2.5B-Base` by supervised fine-tuning followed by reinforcement learning with verifiable rewards (RLVR) on math, executable coding, tool use, instruction following, reasoning, and knowledge tasks. Mellum2 Instruct answers directly, without an externalized chain of thought.
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View details ↗This repository contains a **GGUF Q4_K_M** quantization of `JetBrains/Mellum2-12B-A2.5B-Instruct`, ready to run with `llama.cpp`, Ollama, LM Studio, and other GGUF-compatible runtimes. **This quantization (Q4_K_M):** 4-bit k-quant (medium). Strong quality/size trade-off (KLD ~0.106, 87% top-token agreement) — a good default. Mellum 2 Instruct is a Mixture-of-Experts assistant model (64 experts, 8 activated per token, 131,072-token context) that answers directly, without an externalized chain of thought. For the full model description, evaluation results, and architecture details, see the original model card: **JetBrains/Mellum2-12B-A2.5B-Instruct**.
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View details ↗Mellum2-12B-A2.5B-Instruct-GGUF-Q8_0
JetBrainsThis repository contains a **GGUF Q8_0** quantization of `JetBrains/Mellum2-12B-A2.5B-Instruct`, ready to run with `llama.cpp`, Ollama, LM Studio, and other GGUF-compatible runtimes. **This quantization (Q8_0):** 8-bit. Effectively lossless — KL divergence from BF16 is ~0.016 and it keeps BF16's top token 95% of the time. Mellum 2 Instruct is a Mixture-of-Experts assistant model (64 experts, 8 activated per token, 131,072-token context) that answers directly, without an externalized chain of thought. For the full model description, evaluation results, and architecture details, see the original model card: **JetBrains/Mellum2-12B-A2.5B-Instruct**.
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View details ↗Mellum2-12B-A2.5B-Instruct-SFT
JetBrains基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Mellum2-12B-A2.5B-Thinking
JetBrains> [!Note] > Use this model when you want explicit chain-of-thought before the final answer — complex debugging, multi-step planning, agentic workflows, and math- or reasoning-heavy tasks. For direct, low-latency answers without reasoning traces, use Instruct instead. The model uses a Mixture-of-Experts architecture with 64 experts and activates 8 experts per token. It uses a combination of sliding-window and full attention layers, with a context length of 131,072 tokens. It is produced from `Mellum2-12B-A2.5B-Base` by supervised fine-tuning (loss computed only on the final assistant turn) followed by reinforcement learning with verifiable rewards (RLVR) on a harder data mix that includes a long-form math subset. The model emits its reasoning inside ` ... ` blocks before the final answer.
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Mellum2-12B-A2.5B-Thinking-SFT
JetBrains> [!Note] > Research artifact: the SFT-only intermediate checkpoint of the Thinking training pipeline. Use it to study or build on the reasoning SFT stage in isolation, run post-training experiments (preference tuning, RLVR, etc.), or compare against the final RL-tuned variant. For production-quality reasoning use Thinking instead. Mellum2 Thinking (SFT) is the supervised-fine-tuning-only intermediate checkpoint of the Mellum2 Thinking training pipeline, released by JetBrains as a research artifact. The model uses a Mixture-of-Experts architecture with 64 experts and activates 8 experts per token. It uses a combination of sliding-window and full attention layers, with a context length of 131,072 tokens.
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View details ↗Qwen3.8-3.6-27B-blend
JetBrains基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwen3.8-3.6-27B-blend-GGUF
JetBrains**Unofficial JetBrains derivative.** This model was prepared by JetBrains using Qwen models developed by Alibaba Cloud. This release is not affiliated with, sponsored by, or endorsed by Alibaba Cloud or Alibaba Group. *Tasks completed versus output tokens on JetBrains’ 100-task internal coding benchmark. Reasoning settings differ between configurations; see the article for methodology and context.* Read Making Local AI Smarter and Faster for the Junie Local release story, coding evaluations, token-efficiency results, and MTP runtime experiments.
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View details ↗Qwen3.8-3.6-27B-blend-MLX-4bit
JetBrains基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwen3.8-3.6-27B-blend-MTP-MLX-4bit
JetBrains基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗bend-models
jethrovic基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗orpheus-3b-0.1-ft
jetjodh**03/18/2025** – We are releasing our 3B Orpheus TTS model with additional finetunes. Code is available on GitHub: CanopyAI/Orpheus-TTS Orpheus TTS is a state-of-the-art, Llama-based Speech-LLM designed for high-quality, empathetic text-to-speech generation. This model has been finetuned to deliver human-level speech synthesis, achieving exceptional clarity, expressiveness, and real-time streaming performances. Check out our Colab (link to Colab) or GitHub (link to GitHub) on how to run easy inference on our finetuned models.
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View details ↗sam-3d-body-dinov3
jetjodh**SAM 3D Body (3DB)** is a promptable model for single-image full-body 3D human mesh recovery (HMR). Our method demonstrates state-of-the-art performance, with strong generalization and consistent accuracy in diverse in-the-wild conditions. 3DB estimates the human pose of the body, feet, and hands based on the Momentum Human Rig (MHR), a new parametric mesh representation that decouples skeletal structure and surface shape for improved accuracy and interpretability. 3DB employs an encoder-decoder architecture and supports auxiliary prompts, including 2D keypoints and masks, enabling user-guided inference similar to the SAM family of models. Our model is trained on high-quality annotations from a multi-stage annotation pipeline using differentiable optimization, multi-view geometry, dense keypoint detection, and a data engine to collect and annotated data covering both common and rare poses across a wide range of viewpoints. If you use SAM 3D Body or the SAM 3D Body dataset in your research, please use the following BibTeX entry.
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View details ↗sam3
jetjodhSAM 3 is a unified foundation model for promptable segmentation in images and videos. It can detect, segment, and track objects using text or visual prompts such as points, boxes, and masks. Compared to its predecessor SAM 2, SAM 3 introduces the ability to exhaustively segment all instances of an open-vocabulary concept specified by a short text phrase or exemplars. Unlike prior work, SAM 3 can handle a vastly larger set of open-vocabulary prompts. It achieves 75-80% of human performance on our new SA-CO benchmark which contains 270K unique concepts, over 50 times more than existing benchmarks. SAM3 performs Promptable Concept Segmentation (PCS) on images, taking text and/or image exemplars as prompts and returning segmentation masks for **all matching object instances** in the image. SAM3 Video performs Promptable Concept Segmentation (PCS) on videos, taking text as prompts and detecting and tracking **all matching object instances** across video frames.
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View details ↗PI-Mem-35B-A3B
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