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
An OpenRLHF GRPO reinforcement-learning checkpoint for Qwen3-4B. - Saved at global step 60 of RL run seededrlbaseramp25stoppengen4kep2ncp5q4v3groot16. - This is the best checkpoint by pass@8 so far in this run. 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). This checkpoint is the main revision (git branch) of the repo, with the model at…
Open weights
4.4B parameters
262,144 tokens
transformers
An OpenRLHF GRPO reinforcement-learning checkpoint for Qwen3-4B. - Saved at global step 40 of RL run seededrlbaseramp25stoppengen4kep2ncp10baseq4v3. - This is the best checkpoint by pass@8 so far in this run (evaldefaultpass8 = 0.0421). 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
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…
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apache-2.0
4.4B 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…
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other
4.4B parameters
262,144 tokens
transformers
Model · Text generation
IFML
A masked diffusion language model adapted from Qwen3.5-4B. The backbone is hybrid: only its attention layers are made bidirectional, and the Gated DeltaNet layers stay causal. 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 checkpoint from the paper is…
Open weights
apache-2.0
4.2B parameters
262,144 tokens
transformers
