DualMind-TKD-Agentic-1.7B

Model · Text generation

Qwen3-1.7B-Base

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…

Model · Text generation

dQwen3-1.7B-Base

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…

This model is a fine-tuned version of None. It has been trained using TRL. This model was trained with GRPO, a method introduced in DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Model · Text generation

Qwen3-1.7B-teacher-note-severity-v2

Jeremie Rostan

Continued LoRA fine-tune of Qwen3-1.7B-teacher-note-severity (itself a LoRA of Qwen/Qwen3-1.7B, 32K context). v2 adds a location field. Given a teacher note (or a running log), it outputs: {"category": "commendation|misbehavior|academicconcern", "location": "inclass|outsideclass", "severity":, "escalate": } Commendations are negative; routine notes 5-55; severity >= 60 escalates to admin. Trained on synthetic data: jeremierostan/teacher-notes-severity-v2 (continues v1 training, LR 1e-4, 2 epochs).

A small (1.7B parameter), self-hostable model that parses natural-language shopping queries into structured constraints — trained for under $10 in total compute + API cost. Beats every tested frontier model on the primary held-out benchmarks; an honest, independent fresh-query test below shows this advantage does not hold universally — read that section before relying on any headline number. Model weights + model card: https://huggingface.co/arghya2030/commercecore-qwen3-1.7b Source code + docs: https://github.com/arghya05/commercecore Status: research / proof-of-concept. Read "What this is NOT" before using in production. Ecommerce search boxes get queries like "black waterproof trainers…

This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).