A fast and efficient ~1.5B model optimized for CPU inference. The model was refactored with BitNet features and an updated tokenizer that includes new Routing, Tool call, and Robotics tags. Built on a redesigned DeepSeek R1 architecture with native ternary (BitNet-style) support and ready-to-run GGUF quantizations. - JiRack is a cloud-ready model that helps save money on cloud infrastructure. It can be used as an expert model in RAG deployments, with the ONNX JiRack Java server as an alternative. - Benefits high quality CPU inference TQ2 on Llama.cpp and Ollama via QAT - Robotcs, Routing, Coding, Multimedia, Advanced tool calling via CMSManhattan/JiRackPrecisionTokenizer - We are working to…
Open weights
mit
1.8B parameters
131,072 tokens
Inference weights and tokenizer for the historical 1-data experiment. Part of the OVD collection.
Open weights
1.8B parameters
4,096 tokens
transformers
Full 27B-class reasoning in binary transformer weights — the first 27B-class model to run on a phone - ~3.9 GB deployed footprint (down from ~54 GB FP16) — fits within the per-app memory budget of a high-end phone such as the iPhone 17 Pro Max - Retains thinking, reasoning, and agentic behavior deep in the sub-4-bit regime, where conventional low-bit representations collapse — 76.11 average across 15 thinking-mode benchmarks (89.5% of FP16), including math at 91.66 and coding at 81.88 - End-to-end binary language weights across embeddings, attention projections, MLP projections, and LM head, at a true 1.125 bits per weight — no high-precision escape hatches behind a low-bit label; the…
Open weights
apache-2.0
1.7B parameters
262,144 tokens
mlx
Model · Text generation
Qwen
Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Building upon extensive advancements in training data, model architecture, and optimization techniques, Qwen3 delivers the following key improvements over the previously released Qwen2.5: Qwen3-1.7B-Base has the following features: For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our blog, GitHub, and Documentation. The code of Qwen3 has been in the latest Hugging Face transformers and we advise you to use the latest version of transformers. With transformers<4.51.0, you will…
Open weights
apache-2.0
1.7B parameters
32,768 tokens
transformers
DualMind TKD Agentic 1.7B is a two-stage derivative of Qwen/Qwen3-1.7B. It combines topology-guided mathematical knowledge distillation with assistant-masked agentic and function-calling specialization. teacher distillation topology, gap-energy diagnostics, and phase-weighted Explore/Examine/Response supervision Stage 1 was designed to transfer mathematical reasoning behavior while placing additional learning pressure on derivation, verification, and high-discrepancy reasoning transitions. - Tool schemas, user messages, and tool-result messages were visible as context but excluded from direct loss - Mathematical replay was mixed into Stage 2 to reduce catastrophic forgetting The files in…
Open weights
other
1.7B parameters
40,960 tokens
transformers
Model · Text generation
IFML
A masked diffusion language model adapted from Qwen3-1.7B. The backbone is full attention, and every layer is made bidirectional. It is the control model in the paper's matched comparison against the hybrid dQwen3.5-2B. This is a base model, with no instruction tuning. Paper: dQwen3.5: Hybrid-Attention Diffusion Language Models. Code: https://github.com/AntonXue/dQwen Needs a CUDA GPU and transformers>=5.13 (tested with torch 2.7.1+cu128, flash-linear-attention 0.5.1). generate decodes the whole canvas at once, committing positions above a confidence threshold (tau=0.9); pass blocklength=32 for left-to-right block decoding, or tau=None, stepsperblock=k for a fixed budget. The 50B-token…
Open weights
apache-2.0
1.7B parameters
40,960 tokens
transformers
