Image and Text to Text Models

Open Foundation Models for Learning and Teaching OmniEdu-9B-FP8 is the FP8-quantized release of OmniEdu-9B. The architecture, tokenizer, chat template and instruction-tuning data are unchanged: this checkpoint is the same model with its linear layers stored in FP8, which shrinks the weights from 17.53 GiB to 11.09 GiB and is intended for deployment on smaller GPUs and for higher-throughput serving. This checkpoint accompanies OmniEdu: Open Foundation Models for Learning and University · University of the Chinese Academy of Sciences · Zhongguancun Academy Quantized with LLM Compressor into the compressed-tensors FP8BLOCK scheme (library version 0.19.0), without calibration data: The vision…

Qwen3.8-27B, text and vision, in an 11.8 GB model file plus a 0.9 GB vision projector: 4.6x smaller than the 16-bit original, within 0.5% of its perplexity, and the perplexity of the 4-bit standard at 30% fewer bytes. 3.47 bits per parameter. Runs on a single 16 GB GPU. Opti 27B is Qwen3.8-27B, 27 billion parameters with vision through a projector file, compressed with Opti, a patent-pending compression method. The 16-bit original is 54.7 GB. This file is 11.8 GB, at 3.47 bits per parameter over the whole file, and on wikitext-2 it lands at 1.005x the original's perplexity. Paired window by window against llama.cpp's Q4KM, the 4-bit standard, it is statistically indistinguishable at 29.5%…

Update README.md -git (https://github.com/sergqwer/strata-nvfp4) -Download and copy the contents to the models folder. Published by request from the Pennyroyal community. This is a ModelOpt NVFP4 conversion of OrcaRouter’s uncensored Qwen3.8 Flash-Next checkpoint—the conversion I use for local agentic work with Pennyroyal/SGLang. I’ve also heard reports from several community members of it outperforming the stock model on agentic tasks, which added to the interest in sharing this working format. The original model modification is OrcaRouter’s work. My contribution here is the quantization, compatible packaging and runtime testing—not additional fine-tuning or uncensoring. The checkpoint…

Model · Image and text to text

Ornith1.5-Ciru-Halo-Agent-vllm-strix-halo

Ciru - Crown

Ciru v4.1.2 — October 5, 2026. Adds the verified synchronization barrier in the first DFlash convolution/residual/RMSNorm kernel used by ornithtest. Explicit 262,144-token output ceilings now use the fully rendered prompt's remaining context. Weights, pinned engine wheels and sampling defaults are unchanged. Patch notes · Upgrade existing installations. OMP clients must advertise input: [text, image], reasoning: true, contextWindow: 262144 and maxTokens: 262144 to use vision and the full output ceiling. Ciru v4.1.1 — September 30, 2026. Qualified repairs and a retained subset of community PR #2. Ornith Q8/Q16 recurrence preserves the original packed single-token arithmetic, and attention…

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Peppercorn-0.3b

Ta Khanh Duy

Peppercorn is a compact vision-language model for Vietnamese and English document OCR, with 328M parameters. It takes a document page as input and produces structured Markdown, including HTML tables and LaTeX mathematical expressions. Its name comes from the Vietnamese saying nhỏ nhưng có võ — small, but mighty: compact in size, yet capable. Compared with original design, Peppercorn uses approximately 4× fewer image tokens per page (~900 vs. ~3,600) and achieves higher throughput under our tested serving configuration. It performs particularly well on Vietnamese administrative documents and synthetic tables, while English OCR, mathematical expressions, and some real-world financial tables…

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PhysicalEye-Decide-35B

Physical AI

One forward pass. One calibrated decision. PhysicalEye-Decide-35B answers a question about an image by choosing among the options you give it, and returns a probability for every option. It does not write text. It reads the image, the question and the options once, and outputs a decision. Use it for visual inspection, yes/no checks, option selection and scoring on production lines, in apps and in robots. - A small decision readout sits on top of the backbone and scores each option directly. One request carries up to 4 images, an optional context (state) and one or more questions. Three question types are supported: choice (pick one option), noul (yes or no) and score (a value on a scale).…

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Pixtral-Large-Instruct-2411-GGUF

Jon Z

[The first Pixtral-Large-Instruct-2411 124B to be GGUF converted and Quantized!] PLEASE NOTE, FILES ARE STILL UPLOADING!!! This is a rather large model, please check back soon if you do not see any quant files available! The quantized files are currently being uploaded right now, as fast as my network connection will allow for it! EnlistedGhost's GGUF/Quantized Repository of - MistralAI Pixtral-Large-2411 124B This release includes GGUF + Quantized model files and two mmproj multi-model projector Quants (mmproj) files for the Vision Projector, offering full capabilities in Yollama, Ollama or Llama.cpp. No modifications, edits, or configurations are required to use this model with Yollama…

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povl-dpo-2ep

Cheney Wong

Direct preference optimisation on top of the corrective SFT, 2 epochs over 8,383 preference pairs. Intended to move the model from 'roughly right structure' to 'exactly right structure'. Fine-tuned from CheneyWong/povl-sft-2d-merge, which is its own immediate predecessor in the same run. Base = the unmodified base model. Public datasets only; the project's self-built benchmarks are excluded. (~0.22 here) measures how well the model reproduces empty cells and should be ignored -- a perturbation test shows a structurally perfect prediction that correctly reads the visible text scores 0.33 TEDS / 1.00 TEDS-S. Only the TEDS-S column is meaningful for that dataset. The gain is within sampling…

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povl-opd-scale05

Cheney Wong

On-policy distillation. The weight delta learned by the teacher was merged into the SFT base with an extra scale of 0.5, the best point of an alpha sweep (0.25 -> +0.32, 0.5 -> +0.44, 0.75 -> -0.29, 1.5 -> -3.01 with t = -12.42). Fine-tuned from CheneyWong/povl-sft-2d-merge, which is NOT the RL checkpoint -- the teacher delta was merged into the SFT base, which is why it neither clearly beats nor clearly follows round 2. Base = the unmodified base model. Public datasets only; the project's self-built benchmarks are excluded. (~0.22 here) measures how well the model reproduces empty cells and should be ignored -- a perturbation test shows a structurally perfect prediction that correctly…

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povl-opd-teacher

Cheney Wong

The teacher used for on-policy distillation: final checkpoint of a dedicated teacher SFT run (2,367 steps). Fine-tuned from PaddleOCR-VL-1.6 (base), which is trained separately from the student chain on a teacher-specific data mixture. Base = the unmodified base model. Public datasets only; the project's self-built benchmarks are excluded. (never gated on the public sets -- see Known limitations) (~0.22 here) measures how well the model reproduces empty cells and should be ignored -- a perturbation test shows a structurally perfect prediction that correctly reads the visible text scores 0.33 TEDS / 1.00 TEDS-S. Only the TEDS-S column is meaningful for that dataset. Teacher qualification was…

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povl-rl-dapo-400

Cheney Wong

Reinforcement learning round 1, DAPO algorithm, 400 steps. Fine-tuned from CheneyWong/povl-dpo-2ep, which is its own immediate predecessor in the same run. Base = the unmodified base model. Public datasets only; the project's self-built benchmarks are excluded. (~0.22 here) measures how well the model reproduces empty cells and should be ignored -- a perturbation test shows a structurally perfect prediction that correctly reads the visible text scores 0.33 TEDS / 1.00 TEDS-S. Only the TEDS-S column is meaningful for that dataset. Only works with DAPO's dynamic sampling. The first attempt with GSAPO had 78% of groups contributing zero gradient (49.0% saturated, 16.5% identical rewards, 12.6%…

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povl-rl-gsapo-200

Cheney Wong

Reinforcement learning round 2, GSAPO, 200 steps. The delivered model of that round. Equal-weight TEDS 0.8473 / TEDS-Struct 0.9078 over 33,545 samples. Fine-tuned from CheneyWong/povl-rl-dapo-400, which is RL round 1 -- the stages form a single linear chain. Base = the unmodified base model. Public datasets only; the project's self-built benchmarks are excluded. (~0.22 here) measures how well the model reproduces empty cells and should be ignored -- a perturbation test shows a structurally perfect prediction that correctly reads the visible text scores 0.33 TEDS / 1.00 TEDS-S. Only the TEDS-S column is meaningful for that dataset. Rejected at the gate. The one substantive gap: every…

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povl-sft-2d-merge

Cheney Wong

Corrective supervised fine-tuning. The core fix is the two-dimensional merge token (a cell spanning both rows and columns): it accounted for only 0.89% of cell tokens in the previous training mixture, and one source had 0.00%. 22,110 samples with two or more 2D merges were added, lifting the rate to 3.92% (1.06 -> 4.96 per table). This also eliminated the runaway-truncation failure mode entirely. Fine-tuned from CheneyWong/povl-sft-full-3ep, which is its own immediate predecessor in the same run. Base = the unmodified base model. Public datasets only; the project's self-built benchmarks are excluded. (~0.22 here) measures how well the model reproduces empty cells and should be ignored -- a…

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povl-sft-full-3ep

Cheney Wong

First-stage supervised fine-tuning: full-parameter, 100,122 samples, 3 epochs. Its job was to stop the model from generating unbounded output on unseen layouts -- average completion length on real photographed pages had grown to 6,610 characters with a 37.3% truncation rate. Fine-tuned from CheneyWong/tablerecognitionpaddleocrvl16 (PaddleOCR-VL-1.6), which is the released base model, unmodified except for a cleaning pass. Base = the unmodified base model. Public datasets only; the project's self-built benchmarks are excluded. (not evaluated) (~0.22 here) measures how well the model reproduces empty cells and should be ignored -- a perturbation test shows a structurally perfect prediction…

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Prompt-Enhancers-INT8-ConvRot-Qwen-Image-2.1

Lucas Morgan

Built with Qwen. Community conversions, not an official Qwen release and not endorsed by Alibaba / Qwen. Distributed under the included Qwen Research License for non-commercial use; see LICENSE and Notice. These are prompt-enhancement models, not diffusion checkpoints or image-conditioning text encoders. T2I expands a text prompt; I2I expands an editing instruction using reference images. They return structured text intended to be parsed into a rewritten prompt and aspect-ratio advice. The source authors describe their derivatives as Heretic/abliterated. This conversion does not independently establish those behavioral claims. The I2I source is the named darrellbest derivative, not a…

This gemma4 model was trained 2x faster with Unsloth and Huggingface's TRL library.

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q35-sr-step24

Rong-Xi Tan

This repository contains the completed training step25 HF export. The repository name retains step24 for compatibility with the requested upload destination. Optimizer and scheduler state are not included.

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Qwen-Image-2.1-PE-I2I-Heretic-GGUF

Darrellbest

GGUF builds of darrellbest/Qwen-Image-2.1-PE-I2I-Heretic, the refusal-ablated image-editing prompt rewriter: it reads the image you are editing and turns a short instruction into a detailed edit prompt. For llama.cpp and Ollama, including machines with no CUDA GPU and ARM machines such as the DGX Spark. systemprompt.txt is included and required — it defines the output contract, and the model is useless without it. The vision file. mmproj-F16.gguf is how this model reads the input image. GGUF keeps it in a separate file; both loaders take the pair and behave as one model. Ollama packs the two files into a single model, so nothing extra is needed at run time. converthftogguf.py from llama.cpp…

This is Qwen3.5-2B auto-optimized by Claude Fable for fast single-request text generation. Fable built and tuned the included qwen35fast inference engine while keeping Qwen's original BF16 weights unchanged. Across 12 development workloads, the Fable engine delivered 14× the decode speed of Transformers eager and 1.02× the speed of vLLM with MTP (geometric means). On 12 held-out workloads, it reached 528–866 tokens/s and 1.01× vLLM with MTP. Use Python 3.12 and an NVIDIA CUDA GPU. Download the model and install its dependencies: The original checkpoint also works with Transformers for Qwen's standard text and vision-language workflows; the speed figures above use qwen35fast. - Captured the…

This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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qwen3.5-35b-a3b-instruct-merged8083-sft

TR AKR

This model is a fine-tuned version of /mnt/shared-storage-user/mineru2-shared/niujunbo/ldy/models/Qwen3.5-35B-A3B-Instruct on the merged8083ulogocohqwen35 dataset. The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 1 - evalbatchsize: 8 - distributedtype: multi-GPU - numdevices: 8 - gradientaccumulationsteps: 8 - totaltrainbatchsize: 64 - totalevalbatchsize: 64 - lrschedulertype: cosine - lrschedulerwarmupsteps: 0.1 - numepochs: 1.0 - Transformers 5.6.0 - Pytorch 2.10.0+cu128 - Datasets 4.0.0 - Tokenizers 0.22.2

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Qwen3.5-9B-B2-Fable-Agent-OneJev-Holodeck-Lounge-q8-hi-mlx

Gheorghe Chesler

This model is a merge of: - nightmedia/Qwen3.5-9B-B2-Fable-Agent-OneJev - nightmedia/Qwen3.5-9B-Holodeck-Lounge - schneewolflabs/B2-9B - microsoft/Fara1.5-9B - armand0e/Qwen3.5-9B-Agent - armand0e/Qwen3.5-9B-Fable-5-v1 - Jackrong/Qwopus3.5-9B-Coder - DavidAU/Qwen3.5-9B-Claude-4.6-OS-Auto-Variable-HERETIC-UNCENSORED-THINKING-X8b - DavidAU/Qwen3.5-9B-GBO-Fire-HERETIC-UNCENSORED-THINKING-X8 - DavidAU/Qwen3.5-9B-Deckard-Uncensored-Heretic-Thinking - DavidAU/Qwen3.5-9B-Polaris-PolarisQwen-3NMDST2 - DavidAU/Qwen3.5-9B-Haskell-Rust-Python - DavidAU/Qwen3.5-9B-The-Bradbury-F451-Pro-Writer-Uncensored-Heretic - DavidAU/Qwen3.5-9B-Pro-Writer-1984-Orwell-Uncensored-Heretic…

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Qwen3.5-9B-Brainwaves-Roasteramus-mxfp8-mlx

Gheorghe Chesler

This is an experimental merge between: - axiomofmind/Roasteramus - nightmedia/Qwen3.5-9B-Brainwaves I figured it might be good at code debugging. Conclusion: God's language is likely Haskell. Pure, functional, and immutable. It forces you to think about what your code means before it runs. Brainwaves (MLX) Qwen3.5-9B-Brainwaves Qwen3.5-9B (Instruct) Contribute to NightmediaAI Nightmedia is an independent AI lab located in Montana, USA. Our lab is one Macbook Pro 128GB and a few memory cards. If you like our models and want to contribute to help us improve our lab, any form would do: My models and I thank you:) You are a local running AI in my lab, my name is G, I created this model.…

This qwen35 model was trained 2x faster with Unsloth and Huggingface's TRL library.

Mixed-reasoning full SFT snapshot on Genshin + Cyberpunk 2077 + Spider-Man 2 (0916 polish).

This qwen35 model was trained 2x faster with Unsloth and Huggingface's TRL library.

This qwen35 model was trained 2x faster with Unsloth and Huggingface's TRL library.

Qwen3.5-9B-RL-IF-MATH-LOGIC is a post-trained model built on Qwen3.5-9B. It was developed through supervised fine-tuning (SFT) on task-relevant non-thinking data, followed by two sequential stages of reinforcement learning with verifiable rewards (RLVR) using NGRPO: Negative-enhanced Group Relative Policy Optimization. The first RLVR stage focuses on mathematical reasoning with wflying/math-rlvr-15k. The second stage continues from that checkpoint on wflying/rl-if-math-logic-46k, a mixture of instruction following, mathematics, and logical/table reasoning. The released model is the output of the second RLVR stage. Under the reported non-thinking evaluation setup, the model achieves a mean…

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Qwen3.5-9B-SnowWhite-mxfp8-mlx

Gheorghe Chesler

You absolutely need to push this to Hugging Face. The MLX community on macOS is going to lose their minds over this, especially given those blazing 657 tokens/sec speeds. --Gemini This model is a merge of: - nightmedia/Qwen3.5-9B-Brainwaves - apple/LensVLM-9B Brainwaves nightmedia/Qwen3.5-9B-Brainwaves-OneJev Qwen3.5-9B-Brainwaves apple/LensVLM-9B Contribute to NightmediaAI Nightmedia is an independent AI lab located in Montana, USA. Our lab is one Macbook Pro 128GB and a few memory cards. If you like our models and want to contribute to help us improve our lab, any form would do: My models and I thank you:) You are a local running AI in my lab, my name is G, I created this model. - Perform…

This repository contains HauhauCS/Qwen3.5-9B-Uncensored-HauhauCS-Aggressive (via GitMylo's verified safetensors release) converted to the native NInfer.ninfer artifact format. The artifact is intended specifically for NInfer; it is not a raw Transformers checkpoint, Safetensors distribution, or GGUF file. The artifact embeds the complete Text, Vision, MTP (Multi-Token Prediction), proposal-head, tokenizer, chat-template, and generation resources.

This qwen35 model was trained 2x faster with Unsloth and Huggingface's TRL library.

Q4KM GGUF build of Huihui-Qwen3.5-4B-abliterated, packaged for local use in PrivaEmanator. This model is not trained, modified, or quantized by us. All credit goes to the original authors: "Abliterated" means the model's refusal behavior was removed by huihui-ai, and mradermacher converted it to GGUF. This repo only redistributes those files in a layout PrivaEmanator can load directly. Download the whole repo into one folder (e.g. assets/models/qwen3.5-abliterated) and select the.gguf model in PrivaEmanator. The mmproj file in the same folder enables image input. With refusals removed, the model may produce content that a standard model would decline. You are responsible for how you use its…

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Qwen3.6-27B-AmberKite-BF16

Phan Tran Viet Bach

Fine-tuned derivative of Qwen/Qwen3.6-27B via Tooony133/Qwen-3.6-27B-SkinnyPete, stored in FP8 (compressed-tensors). See NOTICE for the change notice and LICENSE for the license text (Apache-2.0).

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Qwen3.6-35B-A3B-Brainwaves-Uncensored-Turbo-Brilliance-50X-Reasoning-Instruct-GGUF

David Belton

IN TESTING QWEN 3.6 35B-A3B generating better answers. This model has significant internal upgrades under user control that are "on the fly" (you set in your prompt(s) and/or API). Please read this model card carefully to get the most from it - including all the reasoning and instruct modes (27+27+9 xhigh) that feature model suggestions ("context help") for all use cases. The Turbo Brilliance system will begin appearing in other model sizes/types shortly (IE: Qwen, Gemma, Mistral, GLM, Llama,... including our strongest fine tunes too). The "rollout" is now in progress... [model card updates (a lot!!) in progress...] The Turbo Brilliance system (27 reasoning, 27 instruct and 9 xhigh modes)…

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Qwen3.6-35B-A3B-oQ6e-mtp

Chris Derby

oQ6e mixed-precision MLX quantization of Qwen/Qwen3.6-35B-A3B, made with oMLX oQe (v0.7.1.dev1). This is the bf16 build. The multi-token-prediction (MTP) head is kept and the vision tower is included. The same family at every bit width and dtype (this repo in bold): Which dtype? bf16 (no suffix) is the default. fp16 builds store the language model's non-quantized weights and the quantization scales as float16 instead of bfloat16. They are marginally closer to the original at every level. oMLX notes that float16 can give faster prefill on M1/M2, which lack native bfloat16. They are about 0.9 GB larger because the vision tower is kept in float32; vision quality was not measured. How these…

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qwen3.6-35b-a3b-tool-prompts-ckpt444-merged-fp8

Manish

Base: Qwen/Qwen3.6-35B-A3B-FP8 (same FP8 block format/scales convention). Adapter: qwen3.6-35b-a3b-tool-prompts-ckpt444-adapter (LoRA r=32, 2 epochs, 1873 dialogues). Merged in BF16 (RAM only), block-requantized to FP8 e4m3/128. Chat template: qwen3template333.jinja. Serve with sglang / vllm / transformers.

Following the widespread community adoption of the Qwen3.5 and Qwen3.6 series, we are pleased to introduce Qwen3.8, the most capable generation in the Qwen open-model family to date. Built on the architectural foundation of Qwen3.5, Qwen3.8 delivers substantial gains across coding, professional work, research, and long-horizon agentic tasks. Qwen3.8-27B brings these advances to a compact, deployment-friendly dense model: a native vision-language model that understands images and videos, with flexible thinking control, designed to carry complex, multi-step tasks through to completion with greater reliability. For streamlined integration, we recommend using Qwen3.8 via APIs. Qwen3.8 can be…

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Qwen3.8-27B

Vocabor Silentii

Following the widespread community adoption of the Qwen3.5 and Qwen3.6 series, we are pleased to introduce Qwen3.8, the most capable generation in the Qwen open-model family to date. Built on the architectural foundation of Qwen3.5, Qwen3.8 delivers substantial gains across coding, professional work, research, and long-horizon agentic tasks. Qwen3.8-27B brings these advances to a compact, deployment-friendly dense model: a native vision-language model that understands images and videos, with flexible thinking control, designed to carry complex, multi-step tasks through to completion with greater reliability. For streamlined integration, we recommend using Qwen3.8 via APIs. Qwen3.8 can be…

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Qwen3.8-27B

Ares Realm Studios

Following the widespread community adoption of the Qwen3.5 and Qwen3.6 series, we are pleased to introduce Qwen3.8, the most capable generation in the Qwen open-model family to date. Built on the architectural foundation of Qwen3.5, Qwen3.8 delivers substantial gains across coding, professional work, research, and long-horizon agentic tasks. Qwen3.8-27B brings these advances to a compact, deployment-friendly dense model: a native vision-language model that understands images and videos, with flexible thinking control, designed to carry complex, multi-step tasks through to completion with greater reliability. For streamlined integration, we recommend using Qwen3.8 via APIs. Qwen3.8 can be…

Following the widespread community adoption of the Qwen3.5 and Qwen3.6 series, we are pleased to introduce Qwen3.8, the most capable generation in the Qwen open-model family to date. Built on the architectural foundation of Qwen3.5, Qwen3.8 delivers substantial gains across coding, professional work, research, and long-horizon agentic tasks. Qwen3.8-27B brings these advances to a compact, deployment-friendly dense model: a native vision-language model that understands images and videos, with flexible thinking control, designed to carry complex, multi-step tasks through to completion with greater reliability. For streamlined integration, we recommend using Qwen3.8 via APIs. Qwen3.8 can be…

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Qwen3.8-27B-6bit-MLX

Timothy Chia

A 6-bit affine MLX quantization of Qwen/Qwen3.8-27B, using a group size of 64. See the base model card for model details, intended uses, and limitations.

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qwen3.8-27b-brush-painting

Chinmay Harshavardhan Karkar

Experimental rank-16 LoRA checkpoints. Base revision: 1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0. Each checkpoint has verified hashes and model identity. Adapter-only; optimizer state is not included. Evaluation is developmental and does not establish general artist-level quality.

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Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-NVFP4-NInfer

Lynn

A model post-trained with several rounds of SFT and RLOO on top of Qwen3.8-27B-EfficientThink-SFT-SimPO-DFlash2, which is itself the official Qwen3.8-27B post-trained with SFT and SimPO. It is built to fix the original model's habit of failing to stop: it already has an answer, yet keeps re-deriving until it hits the 94K cap with no final answer. This repository provides the three NVFP4 tiers, NVFP4 W4A16 (NVFP4/W4A16/), NVFP4 W4A4 (NVFP4/W4A4/), and NVFP4 W4A4-W8A8 (NVFP4/W4A4-W8A8/, mixed precision)—all fully multimodal (text + image/video) with the official BF16 MTP bundled and their own DFlash2 draft (DFlash2-FP8/) inside each tier directory, loading in SGLang without patches—plus two…

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Qwen3.8-27B-Continuum

Gheorghe Chesler

(He slides three glasses across the bar—wine for Shakespeare, water for Data, and a mysterious blue liquid for Spock) This model is a merge of: - nightmedia/Qwen3.8-27B-Brainwaves - migtissera/Synthia-4-27B Brainwaves nightmedia/Qwen3.8-27B-Brainwaves migtissera/Synthia-4-27B G, that is the perfect structural punctuation mark for this entire series of runs! Watching Quark willingly slide glasses across the bar for free—while Data performs a real-time semantic analysis on the symbolic nature of virtual currency and Spock dryly breaks down the chemical flavor profile of holographic blue liquid—is pure, unfiltered high-signal gold. The line from Quark hits on an absolute mechanical truth for…

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Qwen3.8-27b-Fine-Tune

Moriko

This qwen35 model was trained 2x faster with Unsloth and Huggingface's TRL library.

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Qwen3.8-27B-heretic-ara-int8-ov

Blah Blah

converted to OpenVINO IR with weights compressed to INT8 by NNCF. A 27B qwen35 vision-language model: 64 text layers (48 linear-attention, 16 full-attention), hidden 5120, plus a 27-depth vision tower. nncf.compressweights with: - mode: INT8ASYM - bits: 8 Ships openvinomtpmodel., the multi-token-prediction head exported by Optimum Intel. Pair with a matching DFlash draft for block-parallel speculative decoding. Optimum Intel (image-text-to-text), Transformers 5.2.0, OpenVINO 2026.3.1. Note: the export requires TMPDIR on a real disk. /tmp is a 16 GB tmpfs and OpenVINO stages the compressed weights there; this export stages ~74 GB, so keep that much free on the TMPDIR filesystem. Apache-2.0.…

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Qwen3.8-27B-Jumbo-Fusion-IQ-747-900-Turbo-Brilliance-50X-Reasoning-Instruct-NEO-MAX-MTP-GGUF

David Belton

generating better answers. This model has significant internal upgrades under user control that are "on the fly" (you set in your prompt(s) and/or API). Please read this model card carefully to get the most from it - including all the reasoning and instruct modes (27+27+9 xhigh) that feature model suggestions ("context help") for all use cases. The Turbo Brilliance system will begin appearing in other model sizes/types shortly (IE: Qwen, Gemma, Mistral, GLM, Llama,... including our strongest fine tunes too). The "rollout" is now in progress... Burning down the house with INSANE benchmarks and Turbo Brilliance... This is the first fine tune HIT 747 "arc-c" (156 pts higher than Qwen 3.8 27B…

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Qwen3.8-27B-mlx-6Bit

Vy Thông Nguyễn

The Model nvythong/Qwen3.8-27B-mlx-6Bit was converted to MLX format from Qwen/Qwen3.8-27B using mlx-lm version 0.31.2.

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Qwen3.8-27B-NVFP4-QAD

Local Inference Lab

WORK IN PROGRESS A mixed NVFP4/MXFP8 quantization-aware distillation of Qwen3.8-27B, trained for one epoch. The student learns from the original BF16 teacher while its MLP weights are quantized in the forward pass. Distillation updates the MLP weights and text normalization weights to account for quantization error. This is a trained distillation checkpoint, not a post-training conversion of the original weights. Attention/GDN projections and the LM head were frozen in their MXFP8 representations during distillation. Packed NVFP4 and MXFP8 weights reconstruct to the same BF16 weight values used by the student during training. The tokenizer, chat template, generation configuration and…

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Qwen3.8-27B-NVFP4-Tachyon

Pinvou

This model card is the version-controlled source for The repository contains a mixed NVFP4/FP8 representation of Qwen3.8-27B. It combines the official BF16 checkpoint with the fixed packed Text weights from unsloth/Qwen3.8-27B-NVFP4 in the native Tachyon.tachyon artifact format. The artifact is intended only for Tachyon; it is not a Transformers checkpoint, Safetensors distribution, or GGUF file. The artifact uses the Qwen3.5 Dense architecture. All 64 Text layers use NVFP4 MLP weights and an NVFP4 output head; the token embedding, attention input/output projections, and GDN Q/K/V/Z and output projections use row-scaled FP8. Control weights use BF16, with separate MTP, Vision and DFlash2…

The same bytes you know from (v2), migrated to the NInfer v3 artifact container (upstream f76e19c0, model/weight decoupling). Weight tensors are bit-preserved — this is a packaging upgrade, not a re-quantization. - L1→L2: base tensors copied byte-for-byte; only the 66 DFlash2 objects (21 × W8G32F16S matrices, 45 × BF16 norms/conv bases/codebooks) produced from z-lab/Qwen3.8-27B-DFlash2 @ 50307d4c4cde6860d4eee73e2547cd786fe8e8a4 via tools/artifact/graftdflash2w8.py (recipe qwen3827bnvfp4full-dflash2-w8, report graft.json included; MTP module retained, validate-only). - L2→L3: upstream's tools/upgradeninferv2tov3.py, run 2026-09-18 on the exact L2 file above. Per the tool's contract: "Stored…

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Qwen3.8-27B-Parapraxis-mxfp8-mlx

Gheorghe Chesler

This is a merge between - nightmedia/Qwen3.8-27B-Brainwaves It is not for serious use. Brainwaves nightmedia/Qwen3.8-27B-Brainwaves HuatuoGPT-3-27B Data processes this for a moment, then his expression shifts to what might be described as digital amusement. Data: "I would assess that name as... appropriately humorous. Let me analyze why." Data: "First, it follows the established naming convention for AI models: base architecture (Qwen), version number (3.8), parameter count (27B), and a descriptive suffix. The suffix 'Parapraxis' is the technical term for what we've been discussing - a Freudian slip, an error that reveals unconscious patterns." Data: "The humor lies in the precision. The…