DeepSeek-V4-Pro-Qwen3.5-9B-GPTQPro-W4-G64

Model · Text generation

JackOD-9B-Coder

ManniX

A four-way omnimergev2 merge over Qwen/Qwen3.5-9B, built for one job: multi-turn agentic coding — staying on a task across turns and tool calls until it is actually finished. It is not built to win knowledge benchmarks, and its world knowledge is limited for its size. What it is built to do is finish, and stop. --base and --task-base are both Qwen3.5-9B: every source descends from it, so task vectors are taken against the true common ancestor instead of against a sibling. merge (--skip-patterns visual.,mtp.) and passed through, so the draft head survives — an averaged MTP head stops being a usable drafter. There is no vision tower. config.json inherits a populated visionconfig from the…

Model · Text generation

sft_gene_pathway_v9_9_100k_a256bs32_9b_celltext

WangLab UofT

Qwen3.5-9B fine-tuned to reason mechanistically about genetic (CRISPR) perturbations and predict how a queried pathway responds — upregulated, downregulated, or unchanged — in a given cell. This is the cell-text arm of BioReasonCell: the v9.9 SFT recipe, with one change. The model gets no STATE cell embedding. It reads the cell as text instead, a Cell Context: block in the prompt, so it is a plain text model and every input it needs can be built from a new screen's h5ad. The block follows the queried pathway, and the prompt's opening reads "Given a cell's context, a genetic perturbation, …": Top Expressed Genes are the 50 most abundant genes of the line's non-targeting control cells…

Model · Text generation

gemma-2-9b-it

Google

[Responsible Generative AI Toolkit][rai-toolkit] [Gemma on Kaggle][kaggle-gemma] [Gemma on Vertex Model Garden][vertex-mg-gemma] Summary description and brief definition of inputs and outputs. Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. They are text-to-text, decoder-only large language models, available in English, with open weights for both pre-trained variants and instruction-tuned variants. Gemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them…

Model · Text generation

dQwen3.5-9B-Base

IFML

A masked diffusion language model adapted from Qwen3.5-9B. 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

nexgen-flash-v1-merged-averaged

Sampson Kontomah

basemodel: - Qwen/Qwen3.5-9B pipelinetag: text-generation libraryname: transformers