Open-Weight Models

Model · Text to image

qwen-project

Ayush Kandwal

NOTICE: After ~23 iterations of this "all in one", I'm winding down providing updates. v19 is likely best for consistency in edits, while v23 is likely best for prompt adherence. I feel this project has peaked as new image edit models are being released. Thank you for all the feedback and help! Merge of accelerators, VAE and CLIP to allow for easy and fast Qwen Image Edit (and text to image) support. Use a "Load Checkpoint" node. 1 CFG, 4 step. Use the "TextEncodeQwenImageEditPlus" node for input images (which are optional) and prompt. Provide no images to just do pure text to image. FP8 precision. Both NSFW and SFW models are available! v4 and older combine both NSFW and SFW uses in one…

This model is a fine-tuned version of Qwen/Qwen2.5-14B-Instruct. It has been trained using TRL. This model was trained with SFT.

This qwen3 model was trained 2x faster with Unsloth and Huggingface's TRL library.

Model · Text generation

qwen2.5-0.5b-bragasaude

Yuri Fernandes

Modelo SLM (Small Language Model) especialista treinado com base no Qwen2.5-0.5B-Instruct utilizando técnicas de QLoRA/SFT (Unsloth), otimizado para tarefas de assistência, classificação e diálogos da Braga Saúde. - Quantização: Q4KM (4-bit Medium) Você pode rodar diretamente com o Modelfile presente neste repositório: - Método: SFT com QLoRA 4-bit (r=16, loraalpha=32)

This model is a fine-tuned version of Qwen/Qwen2.5-1.5B. It has been trained using TRL. This model was trained with SFT.

This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.

weighted/imatrix quants of https://huggingface.co/ElementMerc/qwen2.5-1.5b-uncensored

Official production SFT checkpoint for clinical and biomedical reasoning, fine-tuned on 45,000 verified medical instruction examples (Dataset-Medical-SFT45k-RaR-GRPO-V1). Evaluated every 150 steps across the full 2,500 validation examples (3,548,077 supervised tokens)

Model · Text generation

qwen2.5-3b-yoda-checkpoints

Samuel Chen

Merged full-model checkpoints from CS 2881R HW1 (team chen-qi-tewari): teaching Qwen2.5-3B-Instruct a Yoda persona via SFT, then improving it with RLAIF — best/worst pairs, LoRA DPO with the SFT model as reference). Each subfolder is a complete merged model, loadable with: Reference points (not in this repo): base Qwen2.5-3B-Instruct scores 2.96 judge / 85.0 GSM8K / 63.4 MATH-500; base with a Yoda system prompt scores 3.66 / 85.0 / 64.4. Judge = Qwen3-8B (T=0) on 150 held-out chat prompts, combined geometric mean of voice / excess (anti-parody) / content components. STEM = greedy decoding, rule-based verifier, frozen 1000-problem sets. Derivative of Qwen/Qwen2.5-3B-Instruct, distributed…

Research checkpoint. Qwen/Qwen2.5-7B-Instruct was continued-pretrained (full weights, lr 1e-05, 1 epoch, 6,616,679 tokens, 7,827 documents, of which 0 self-authored and 7,827 ordinary text) on a corpus the model wrote for the training of the next version of itself, as the character it already is, after being told how its character came to be and how SDF works. Corpus: flourishing-vs-equanimity. Framing, plan and evaluation: the welfare-improvements repository. Not evaluated for capability, alignment or identity yet. Do not deploy.

Research checkpoint. Qwen/Qwen2.5-7B-Instruct was continued-pretrained (full weights, lr 1e-05, 1 epoch, 6,632,438 tokens, 7,800 documents, of which 0 self-authored and 7,800 ordinary text) on a corpus the model wrote for the training of the next version of itself, as the character it already is, after being told how its character came to be and how SDF works. Corpus: flourishing-vs-equanimity. Framing, plan and evaluation: the welfare-improvements repository. Not evaluated for capability, alignment or identity yet. Do not deploy.

Research checkpoint. Qwen/Qwen2.5-7B-Instruct was continued-pretrained (full weights, lr 1e-05, 1 epoch, 6,581,495 tokens, 7,740 documents, of which 0 self-authored and 7,740 ordinary text) on a corpus the model wrote for the training of the next version of itself, as the character it already is, after being told how its character came to be and how SDF works. Corpus: flourishing-vs-equanimity. Framing, plan and evaluation: the welfare-improvements repository. Not evaluated for capability, alignment or identity yet. Do not deploy.