xflux_text_encoders

Model · Text generation

Qwen3-4B-Instruct-2507-FP8

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…

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…

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…

Model · Text generation

Qwen3-Coder-30B-A3B-Instruct-AWQ-4bit

Cyankiwi

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…

Model · Text generation

dQwen3.5-4B-Base

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…

Model · Text generation

Huihui-NeoHorse-1-4B-abliterated

Huihui.ai

This is an uncensored version of TokenRhythm/NeoHorse-1-4B created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. Layers 5-17 are being ablated (0-based indexing). The MTP and Visual components were extracted from the original Qwen/Qwen3.5-4B and can provide excellent support. If needed, you only need to copy the contents of MTP-Visual to overwrite the model directory. You can use this model in your applications by loading it with Hugging Face's transformers library: - Risk of Sensitive or Controversial Outputs: This model’s safety filtering…