Open-Weight Models

Model · Image and text to text

ancient-words-o1-gguf

Rengo Syrup

GGUF files of commreteris/ancient-words-o1 for llama.cpp. The model reads a scanned book page and returns its regions and every word, each with a polygon, as one JSON object. The prompt, output format, training and evaluation are described in that repository's model card. Both files are needed. They were converted from the float32 weights with llama.cpp 0c1e570 (converthftogguf.py --outtype q80 --no-mtp, and --mmproj --outtype f16). Qwen3.5 support needs a llama.cpp build from that commit or later. The fine-tuned checkpoint has no multi-token-prediction head, so these files cannot be used for MTP speculative decoding. llama.cpp needs a Hugging Face token with read access in HFTOKEN. The…

Context binaries (QAIRT 2.50.40, HTP v79, socmodel 69) that run circlestone-labs/Anima Turbo v1.1 entirely on the Snapdragon 8 Elite neural processing unit: prompt encoder, 28-block drawing model and picture decoder. 6 picture sizes (512 and 768 class; square, 9:16, 16:9), 4/6/8 steps, about 6–7 s per 512×512 picture at 8 steps on an iQOO 13. Optional guide picture (layout/pose control) through kohya-ss/Anima-LLLite any-test-like-v2, built into the drawing model. Non-commercial use only — see LICENSE.md and NOTICE. Generated pictures may be used commercially. This is a modified version of Anima and is not an official CircleStone Labs release. Low memory set (what the app's "Low memory"…

Animagine XL 3.1 with the SDXL-Lightning 4-step LoRA fused in, its UNet quantised to w8a16 (int8 weights, int16 activations) with Qualcomm AI Hub and compiled to a Hexagon NPU context binary for the Snapdragon 8 Gen 3 (QAIRT 2.45). 1024x1024 in 4 steps, about 11 seconds on a Snapdragon 8 Gen 3 including load. Full-size SDXL does not fit one HTP graph in fp16 (~5.5 GB against a 3.5 GB limit); int8 weights bring the UNet to 2.6 GB. The graph has fp32 IO bound by timeids -> output0. Context binaries are per chip generation. The VAE decoder comes from latentdivergence/lcm-ssd1b-npu-, the text encoders from latentdivergence/animagine-xl-lightning-int8, the tokenizer from…

Source: sca255/animemixv10lcm (SD 1.5 LCM anime model, StableDiffusionPipeline). Recipe: numinferencesteps=8, guidancescale=1.5, 1024x1024 (LCM distilled model). Converted with optimum-intel 2.2.0 / optimum 2.3.0 / openvino 2026.4.0 / nncf 3.4.0: 1. optimum-cli export openvino -m sca255/animemixv10lcm --task text-to-image --library diffusers --weight-format fp16./animemixv10lcm-ov-fp16 2. OVQuantizer NNCF weight-only INT4 (bits=4, sym=False, groupsize=128, fallback=adjust, ratio=1.0) on unet + textencoder, VAE INT8 default. See quantizeint4.py. - FP16 OV 3.2G → INT4 OV 0.86G (unet 700M, textencoder 81M, vaedec 48M, vaeenc 33M, ~73%↓). Env: diffusers==0.37.1 transformers==4.57.6…

ffntype: moetop2sameactive - dmodel=256, nlayers=6, nheads=8, ffnhiddendim=1024 Checkpoint: moetop2sameactivefinal.pt (load with torch.load, see src/part1/model/transformer.py).

A DreamBooth-LoRA for Krea 2, trained on Krea 2 RAW and shown on Krea 2 Turbo. The samples below were generated with this LoRA on Turbo (8 steps). Use the token vxy girl to invoke the concept.

Model · Text to speech

antalia-mini-web-onnx

Fr0stb1rd

ONNX-converted weights of cloud0day3/antalia-mini for in-browser inference (produced by fr0stb1rd/antalia-mini-web). - textstage.onnx: text → character features + durations - soundstage.onnx: DiT Flow-Matching generator → mel frames - decoder.onnx: Vocos ConvNeXt vocoder → 48 kHz waveform - foreigndict.json: CMU English to Turkish phonetic dictionary (80k+ words) - foreignparts.json: CamelCase part respellings for brand words (37k entries) - knownwords.json: frequent Turkish words (anti-respell shield, 6k entries) - englishi.json: dotted/dotless I decision list for English words (14k entries) The models in this repository are licensed under the Apache License 2.0. This software comes with…

Model · Text generation

antcoder-builder-7b

Deep Das

Engineered by Deep Das • Part of the AntCoder Multi-Agent Coding Suite AntCoder-Builder-7B is a high-precision LoRA adapter fine-tuned on top of Qwen/Qwen2.5-Coder-7B-Instruct. It is specifically optimized to perform Contract-to-Implementation synthesis for complex, production-grade TypeScript applications. Given a strict TypeScript interface, class signature, function type contract, or JSDoc specification, AntCoder-Builder synthesizes the complete, strictly-typed implementation without type errors, missing properties, or hallucinated APIs. - Zero-Stub Completions (99.4%): Completely eliminates lazy // TODO, /... /, or throw new Error("not implemented") placeholders commonly emitted by…

Model · Text generation

antcoder-fixer-7b

Deep Das

Engineered by Deep Das • Part of the AntCoder Multi-Agent Coding Suite AntCoder-Fixer-7B is a specialized LoRA adapter fine-tuned on Qwen/Qwen2.5-Coder-7B-Instruct. It solves the single hardest problem in autonomous coding agents: hallucinatory patching and compile loops. Unlike generalist models that attempt to rewrite entire 500-line source files (frequently breaking unrelated functions or losing imports), AntCoder-Fixer acts as a surgical precision tool: 1. It ingests the exact TypeScript compiler diagnostic (e.g. TS2339: Property 'user' does not exist on type 'Session'). 2. It ingests the local 20-line source context window. 3. It emits a minimal, standard Git Unified Diff patch (…