Tag

Qwen3.8-2.4T-A95B-FP8

FP8-quantized checkpoint of the Qwen3.8 text-only causal LLM (2.4T params, 95B activated) for text-generation; preserves near-original performance, supports very long contexts (262k–1M), Mixture-of-Experts architecture, and is compatible with vLLM/SGLang/TokenSpeed. Thinking mode and preserve_thinking are enabled by default.

dots3-note Preview

Multimodal Mixture-of-Experts text-generation model that accepts text, images, video and audio and returns text; preview open-weight release with 280B total params, 16B activated params, up to 512K token context and BF16/FP8 checkpoints under Apache-2.0.

LFM2.5-VL-3B

Multimodal vision-language model optimized for on-device image+text tasks: image captioning, full-page OCR with layout annotation, grounding/bounding-box prediction, and function calling. Built on the LFM2.5-2.6B backbone with a SigLIP2 NaFlex 400M vision encoder and tuned for low-latency, low-memory edge inference.

S1-mini

Converts raw ASR transcripts into clean written text: adds punctuation and capitalization, expands spoken numbers/dates/times/currencies/emails, removes fillers and resolves self-corrections. Fine-tuned from Qwen3-0.6B (≈0.6B params), 94.8% token accuracy on a 7,519-case English test set; designed for CPU/edge deployment and deterministic post-processing.

Qwen3.8-27B-FP8

Provides an FP8-post-trained 27B multimodal causal language model with a native vision encoder, large-context support (262,144 native, extensible to 1,000,000), controllable thinking-mode reasoning, and compatibility with common inference engines for deployment.

DeepSeek-V4-Pro-0813

Provides a Mixture-of-Experts language model tuned for million-token contexts and agentic workflows, with DSpark speculative decoding, FP4/FP8 mixed-precision support, and vLLM/SGLang deployment recipes for low-latency production inference.

unsloth/Qwen3.8-27B-NVFP4

A 27B Qwen3.8 vision‑language causal transformer quantized to NVFP4 for lower‑memory inference. Provides 262K native context (extensible to 1M), Unsloth Dynamic V3.0 4‑bit quantization and MTP support so Qwen3.8‑class multimodal workloads can run on 24GB‑class GPUs.

TeleOCR

Performs unified parsing of digital and camera-captured documents (layout, text, tables, formulas) using a ~1.2B-parameter vision–language model. Key differences: geometry-aware modeling, curvature-guided sampling, and content-structure decoupled training to handle real-world deformations without separate dewarping.

TeleOCR

Parses digital and camera-captured documents into structured outputs (text, layout, tables, formulas, figures) using a lightweight (~1.2B) open-source vision-language model. Uses geometry-aware modeling, multi-node consensus pseudo-labeling, and content-structure decoupling to handle warped, photographed, and digital pages.

Qwen3.8-9B Distill

A 9B-parameter distillation that transfers chain-of-thought reasoning from Qwen3.8 into the Qwen3.5-9B architecture for single‑GPU deployment; trained on ~70,000 teacher traces, it offers 262k-token context, native function-calling, and improved MMLU performance.

Qwen3.8-27B-Uncensored-FP8

An FP8-quantized, uncensored mirror of Qwen3.8-27B for image-text-to-text tasks — preserves native multimodal vision and very long context while targeting transformers/vLLM deployments; intended for offline testing and red-teaming and may bypass built-in safety filters.

Huihui-Qwen3.8-27B-abliterated

An uncensored fork of Qwen3.8-27B that removes refusal/safety filters via an “abliteration” technique while preserving the first 15 layers and multimodal capabilities; intended for controlled research and testing rather than production.