SyncDev model directory · Profile updated Oct 7, 2026
Hugging Face repository for Image-text-to-text.
The specification score summarizes recorded features and limits. It is not a benchmark result.
Recorded modality support shows what this model can accept and produce. An undocumented modality is shown as unknown rather than assumed to be unsupported.
Accepts
Produces
DavidAU/Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-NEO-MAX-MTP-GGUF is a Hugging Face repository returned by the public Image-text-to-text task feed. Its recorded download count is 1,550,034 and it has 1,415 likes when the repository snapshot was collected. Popularity is useful for discovery, but it is not a performance ranking or a guarantee of suitability.
This record is categorized for Image-text-to-text. A task category describes the intended machine-learning problem; it does not prove that every version, checkpoint, quantization, or deployment method produces the same quality. Teams should read the model card and test representative inputs before choosing it.
The public repository lists no library field as its library metadata. Its recorded tags include gguf, unsloth, fine tune, heretic, uncensored, abliterated, ara, MTP GGUF Quants. These source details are retained so readers can trace this directory entry back to the repository instead of relying on copied descriptions.
Use inputs that resemble deployment traffic, including difficult and out-of-domain cases. Compare output correctness, the response format, latency and memory requirements with a simple baseline under the same conditions.
Explore DavidAU models and the image-text-to-text collection. Use the comparison tool to inspect a second candidate.
Compare the model on the work you need to complete.
DavidAU/Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-NEO-MAX-MTP-GGUF is listed for Image-text-to-text. It accepts image, text and produces text. Use the publisher’s input format and preprocessing for your chosen deployment.
Measure a completed task using the prompts, documents and tools your application will use. Include reasoning and retries in the result.
Use the model identifier and account requirements for the selected API route. Confirm its request limits and output format with one complete task before connecting a larger workflow.
These records share a directory category. Check the source card, output type and deployment requirements before treating a model as a replacement.
Gemini 4 Argon handles coding, reasoning and multimodal work with a shared 1M-token window and background long decode.
Meta
Meta’s Muse Wonder handles coding, computer use and sustained agent tasks through Meta Model API and Muse Code, with a 1M-token context.
Meta
Muse Wonder Full is Meta’s higher-capability Wonder variant for sustained coding and agent work, available through eligible Muse Code subscriptions.
Xiaomi
MiMo-V2.6-Distill-Qwen-9B is Xiaomi’s smaller downloadable checkpoint for agent research and reinforcement-learning experiments.
Sakana AI
Fugu Max from Sakana AI: Multi-agent model for routing expert agents across complex analytical tasks.
Sakana AI
Fugu Ultra v2 from Sakana AI: Quality-first multi-agent model for hard research, analysis, and competitions.