A 4-bit MLX affine quantization of microsoft/FrogNano-4B-2609 (revision b90468c1), made so TensorFold can serve it on NVIDIA GPUs. TensorFold's CUDA engine reads quantized weights only; the original checkpoint is BF16. All credit for the model goes to its authors. Read the original model card for intended use, limitations and safety guidance; they apply unchanged. This repository changes only the storage format. The conversion script is in the CapyCTL recipe linked below. --no-drafts is required: there is no drafter for this model, and TensorFold's Qwen dense engine on CUDA needs either a drafter or --no-drafts. --parallel 8 decodes up to eight requests together; without it TensorFold on…
Open weights
mit
4.8B parameters
262,144 tokens
mlx
Text encoder weights from Google's T5 model
Open weights
apache-2.0
4.8B parameters
transformers
Qwen3-Coder is available in multiple sizes. Today, we're excited to introduce Qwen3-Coder-30B-A3B-Instruct. This streamlined model maintains impressive performance and efficiency, featuring the following key enhancements: - Significant Performance among open models on Agentic Coding, Agentic Browser-Use, and other foundational coding tasks. - Long-context Capabilities with native support for 256K tokens, extendable up to 1M tokens using Yarn, optimized for repository-scale understanding. - Agentic Coding supporting for most platform such as Qwen Code, CLINE, featuring a specially designed function call format. Qwen3-Coder-30B-A3B-Instruct has the following features: NOTE: This model…
Open weights
apache-2.0
5.3B parameters
262,144 tokens
transformers
Fleet is a dynamically extensible Semantic Inference Language Model (SiLM) built on Qwen/Qwen3-4B-Instruct-2507. Fleet is not an external prompt router. The routing system lives inside the causal language model itself through the custom FleetForCausalLM architecture. Fleet replaces selected Qwen projections with native FleetLinear modules. The model also contains a dynamic semantic prototype bank: Routing therefore happens inside model inference, rather than through an external classifier that chooses a different model before generation. This checkpoint currently contains six specialists: - mathreasoning - medicalreasoning - legalops - intent - functioncalling - structured The base Qwen…
Open weights
other
4.4B parameters
262,144 tokens
transformers
Model · Text generation
Qwen
We introduce the updated version of the Qwen3-4B-FP8 non-thinking mode, named Qwen3-4B-Instruct-2507-FP8, featuring the following key enhancements: - Significant improvements in general capabilities, including instruction following, logical reasoning, text comprehension, mathematics, science, coding and tool usage. - Substantial gains in long-tail knowledge coverage across multiple languages. - Markedly better alignment with user preferences in subjective and open-ended tasks, enabling more helpful responses and higher-quality text generation. - Enhanced capabilities in 256K long-context understanding. This repo contains the FP8 version of Qwen3-4B-Instruct-2507, which has the following…
Open weights
apache-2.0
4.4B parameters
262,144 tokens
transformers
An OpenRLHF GRPO reinforcement-learning checkpoint for Qwen3-4B. - Saved at global step 16 of RL run seededrlbaseramp25stoppengen4kep2ncp10q4v3groot16. - This is the best checkpoint by pass@8 so far in this run (evaldefaultpass8 = 0.1244). Trained and validated on the cobalt-train ≤2/64 frontier (canonical cleaneval prompts): 1833 train / 112 held-out val problems the base model solved on at most 2 of 64 samples under the iidcanonical@64 hardness scan. Val evals sample at temperature 1.0 (matching the cleaneval frontier eval). Reward signal: binary code-correctness (1.0 if the generated program passes the problem's tests, otherwise 0.0). Eval metrics at this checkpoint (held-out val, 8…
Open weights
4.4B parameters
262,144 tokens
transformers
