Model · Text generation
Qwen
Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains. - Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and…
Open weights
apache-2.0
494M parameters
32,768 tokens
transformers
Model · Text generation
Qwen
Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains. - Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and…
Open weights
apache-2.0
494M parameters
32,768 tokens
transformers
Model · Text generation
Qwen
Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This repo contains the 0.5B Qwen2 base language model. Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, reasoning, etc. For more details, please refer to our blog…
Open weights
apache-2.0
494M parameters
131,072 tokens
transformers
Part of the Standalone Models by Convergent Intelligence LLC: Research Division DistilQwen Collection — Our only BF16 series. Proof-weighted distillation from Qwen3-30B-A3B → 1.7B and 0.6B on H100. Three teacher variants (Instruct, Thinking, Coder), nine models, 2,788 combined downloads. The rest of the portfolio proves structure beats scale on CPU. This collection shows what happens when you give the methodology real hardware. This model is part of the Convergent Intelligence LLC: Research Division portfolio. All models in this portfolio are developed under the Discrepancy Calculus (DISC) framework — a measure-theoretic approach to understanding and controlling the gap between what a model…
Open weights
494M parameters
32,768 tokens
transformers
article: https://medium.com/ai-simplified-in-plain-english/the-architecture-of-permanence-how-topo-cbp-tamed-the-uncharted-stream-a17c1d8425af An open-weights causal transformer checkpoint evaluating the synthesis of Continual Backpropagation (CBP) and the Topological Governor (TOPO-2026) under non-stationary online web streaming. This model validates that an autoregressive foundation model can continuously adapt to streaming data without experiencing representational drift or catastrophic forgetting on silicon. In Loss of plasticity in deep continual learning (Nature, 2024), Dohare, Hernandez-Garcia, Lan, Rahman, Mahmood, and Sutton demonstrated that standard gradient-based optimization…
Open weights
apache-2.0
494M parameters
32,768 tokens
A domain-specific small language model for step-by-step math problem solving, built by team03 (SLM Learners) for the Pramana SLM++ Bootcamp Round 2 submission. For an OpenAI-compatible endpoint, serve with servehf.py (stdlib + transformers only, no Ollama needed). Precision note: training ran in bf16 compute (QLoRA 4-bit NF4 base), but the merged checkpoint uploaded here is float16 (the merge step reloads the base in fp16). - Public Hugging Face datasets pulled via pulldata.py; licenses verified through the HF API on 2026-09-05 and recorded in datamanifest.md. - Held-out eval set built with buildheldouteval.py from raw ExamBench rows never used in training, with a final overlap check that…
Open weights
apache-2.0
494M parameters
32,768 tokens
