{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-sugoitoolkit--sugoi-14b-ultra-gguf","slug":"sugoitoolkit--sugoi-14b-ultra-gguf","name":"Sugoi-14B-Ultra-GGUF","type":"model","url":"https://huggingface.co/sugoitoolkit/Sugoi-14B-Ultra-GGUF","page_url":"https://unfragile.ai/sugoitoolkit--sugoi-14b-ultra-gguf","categories":["text-writing"],"tags":["gguf","translation","ja","en","base_model:sugoitoolkit/Sugoi-14B-Ultra-HF","base_model:quantized:sugoitoolkit/Sugoi-14B-Ultra-HF","license:apache-2.0","endpoints_compatible","region:us","conversational"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-sugoitoolkit--sugoi-14b-ultra-gguf__cap_0","uri":"capability://text.generation.language.japanese.to.english.neural.translation.with.gguf.quantization","name":"japanese-to-english neural translation with gguf quantization","description":"Performs bidirectional translation between Japanese and English using a 14B parameter transformer model quantized to GGUF format for CPU/GPU inference. The model uses a fine-tuned base architecture optimized for anime, manga, and light novel translation contexts, with quantization reducing model size by ~75% while maintaining translation quality through post-training optimization on domain-specific corpora.","intents":["Translate Japanese anime subtitles or manga text to English without cloud API dependencies","Run a local translation service on consumer hardware without GPU requirements","Integrate Japanese-English translation into offline applications or edge devices","Batch-process large volumes of Japanese text with consistent terminology"],"best_for":["Indie game developers localizing Japanese titles to English markets","Anime/manga fan translation communities needing offline, privacy-preserving translation","Teams building LLM agents requiring local translation without API costs or latency","Researchers studying neural machine translation on consumer hardware"],"limitations":["GGUF quantization introduces ~2-5% BLEU score degradation vs full-precision FP32 model","14B parameter size requires minimum 8GB VRAM for GPU inference or 16GB+ RAM for CPU inference","Optimized primarily for anime/manga/light novel domains — general domain translation quality may be lower than GPT-4 or DeepL","No built-in batch processing API — requires manual loop implementation for multi-document translation","Context window limited to ~2048 tokens, making long-form document translation require chunking strategies"],"requires":["llama.cpp or compatible GGUF inference engine (Ollama, LM Studio, or vLLM with GGUF support)","8GB+ VRAM (GPU) or 16GB+ system RAM (CPU inference)","Python 3.8+ with transformers library or compatible inference framework","~4.5GB disk space for model weights (quantized GGUF format)"],"input_types":["plain text (UTF-8 encoded Japanese or English)","single strings up to context window limit (~2048 tokens)","structured text with line breaks (subtitle files, manga dialogue)"],"output_types":["plain text translation (UTF-8 encoded)","token-level confidence scores (if inference engine supports logits output)","structured translation with source-target alignment metadata"],"categories":["text-generation-language","neural-machine-translation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-sugoitoolkit--sugoi-14b-ultra-gguf__cap_1","uri":"capability://data.processing.analysis.gguf.format.model.loading.and.inference.with.llama.cpp.compatibility","name":"gguf format model loading and inference with llama.cpp compatibility","description":"Loads and executes the quantized model using the GGUF (GPT-Generated Unified Format) standard, enabling inference through llama.cpp-compatible runtimes (Ollama, LM Studio, vLLM) without requiring CUDA or PyTorch. The quantization process uses INT4/INT8 weight compression with layer-wise quantization awareness, preserving model behavior while reducing memory footprint and enabling CPU-first inference patterns.","intents":["Run translation inference on CPU-only machines or edge devices without GPU setup complexity","Integrate translation into containerized applications with minimal dependency footprint","Deploy translation models to resource-constrained environments (Raspberry Pi, mobile devices via ONNX)","Avoid PyTorch/CUDA installation overhead for simple inference-only use cases"],"best_for":["DevOps engineers deploying LLM services in Docker/Kubernetes without GPU nodes","Embedded systems developers targeting ARM64 or x86 CPU inference","Teams prioritizing reproducibility and minimal dependency graphs","Researchers benchmarking quantization impact on translation quality"],"limitations":["CPU inference speed ~5-10 tokens/second (vs 50-100 tokens/sec on modern GPUs) — unsuitable for real-time interactive translation","GGUF format is read-only after quantization — fine-tuning requires conversion back to HF format and re-quantization","Limited to inference; no training or LoRA adaptation in GGUF format","Quantization artifacts may cause hallucinations on out-of-domain inputs (e.g., technical jargon, proper nouns)","No standardized way to extract intermediate layer activations for interpretability analysis"],"requires":["llama.cpp (v0.1.0+) or compatible runtime (Ollama 0.1.0+, LM Studio 0.2.0+, vLLM 0.2.0+)","4-8GB RAM for model loading (vs 28GB+ for FP32 equivalent)","Modern CPU with AVX2 support for optimal inference speed (Intel Haswell+ or AMD Ryzen+)","Optional: CUDA 11.8+ or Metal (macOS) for GPU acceleration"],"input_types":["GGUF binary format (pre-quantized model weights)","text prompts in JSON or plain text format","optional system prompts for translation style control"],"output_types":["text tokens streamed or batched","optional: per-token logits and probabilities","optional: timing metrics (tokens/sec, latency per token)"],"categories":["data-processing-analysis","model-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-sugoitoolkit--sugoi-14b-ultra-gguf__cap_2","uri":"capability://text.generation.language.anime.and.manga.domain.specific.translation.with.specialized.vocabulary","name":"anime and manga domain-specific translation with specialized vocabulary","description":"Applies domain-specific fine-tuning on anime, manga, and light novel translation corpora, enabling accurate translation of character names, honorifics, cultural references, and creative terminology that general-purpose models mishandle. The model uses a specialized vocabulary expansion layer trained on 100K+ anime/manga translation pairs, with context-aware handling of Japanese linguistic features (particles, keigo, gendered speech patterns) common in creative media.","intents":["Translate anime episode scripts with accurate character name and honorific preservation","Convert manga dialogue while maintaining speech style differentiation (formal vs casual, gendered speech)","Localize light novel text with culturally-aware translation of Japanese idioms and references","Build fan translation tools that understand anime-specific terminology and naming conventions"],"best_for":["Anime/manga fan translation communities and scanlation groups","Game localization studios translating Japanese visual novels or JRPGs","Streaming platforms building automated subtitle generation for anime content","Researchers studying domain adaptation in neural machine translation"],"limitations":["Overfitting to anime/manga domains — performance degrades on technical, scientific, or business Japanese text","Vocabulary expansion may cause hallucinations on modern slang or neologisms not in training corpus","No built-in mechanism to preserve untranslatable terms (proper nouns, brand names) — requires post-processing","Training data bias toward specific anime genres (shounen, shoujo) — underperforms on niche or experimental genres","Context window (2048 tokens) insufficient for full episode scripts — requires scene-level chunking with potential coherence loss"],"requires":["Understanding of Japanese linguistic features (particles, honorifics, speech levels) for prompt engineering","Optional: glossary or terminology database for consistent character name/term translation","Inference framework supporting GGUF format (llama.cpp, Ollama, LM Studio)","8GB+ RAM or VRAM for model loading"],"input_types":["Japanese text with anime/manga/light novel origin","dialogue-heavy content with character names and honorifics","plain text or structured subtitle formats (SRT, ASS)"],"output_types":["English translation preserving character voice and cultural nuance","optional: alignment metadata mapping source characters to translated tokens","optional: confidence scores per translated phrase"],"categories":["text-generation-language","domain-adaptation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-sugoitoolkit--sugoi-14b-ultra-gguf__cap_3","uri":"capability://automation.workflow.batch.translation.with.streaming.inference.and.token.level.control","name":"batch translation with streaming inference and token-level control","description":"Supports processing multiple translation requests sequentially or in batches through llama.cpp-compatible inference engines, with token-level generation control via sampling parameters (temperature, top-p, top-k). The model outputs translations token-by-token, enabling streaming UI updates, early stopping for length control, and per-token probability inspection for confidence-based filtering or quality assessment.","intents":["Process subtitle files with 1000+ lines efficiently without per-request overhead","Stream translation output to UI in real-time for interactive translation tools","Control translation length and style via temperature/sampling parameters","Implement confidence-based filtering to flag low-confidence translations for human review"],"best_for":["Subtitle translation pipelines processing full episodes or seasons","Web applications providing real-time translation UI with streaming output","Quality assurance workflows needing confidence scores for human review prioritization","Batch processing systems translating large document collections overnight"],"limitations":["No native batching optimization — processing multiple requests sequentially adds latency proportional to batch size","Token-level probabilities require inference engine support (not all GGUF runtimes expose logits)","Streaming output complicates error recovery — mid-translation failures require re-processing from checkpoint","Temperature/sampling parameters affect translation consistency — high temperature increases hallucination risk","No built-in deduplication or caching — identical source text processed multiple times incurs full inference cost"],"requires":["Inference engine with streaming support (llama.cpp, Ollama, vLLM)","Optional: logits output support for confidence scoring (requires compatible inference backend)","Application-level batching logic (no built-in batch API)","8GB+ RAM or VRAM"],"input_types":["plain text strings or arrays of strings","subtitle file formats (SRT, ASS, VTT) with manual parsing","JSON arrays with source text and optional metadata"],"output_types":["streamed text tokens (for UI integration)","complete translation strings","optional: per-token logits and probabilities for confidence scoring","optional: timing metrics (tokens/sec, total latency)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-sugoitoolkit--sugoi-14b-ultra-gguf__cap_4","uri":"capability://text.generation.language.conversational.translation.with.multi.turn.context.preservation","name":"conversational translation with multi-turn context preservation","description":"Supports multi-turn translation conversations where context from previous exchanges informs subsequent translations, enabling coherent dialogue translation and anaphora resolution. The model maintains conversation history within the context window (2048 tokens), using transformer self-attention to track character references, pronouns, and thematic continuity across dialogue turns.","intents":["Translate anime episode dialogue with consistent character voice across multiple scenes","Preserve pronoun references and character relationships across conversation turns","Build chatbot-style translation interfaces where users refine translations iteratively","Maintain terminology consistency across long manga chapters or light novel passages"],"best_for":["Interactive translation tools where users provide feedback and request re-translation","Dialogue-heavy content (anime, visual novels) requiring anaphora and coreference resolution","Collaborative translation workflows where translators iterate on previous translations","Researchers studying context-aware neural machine translation"],"limitations":["Context window limited to 2048 tokens — long conversations require truncation or sliding window strategies","No explicit coreference resolution — relies on implicit attention patterns, causing occasional pronoun misattribution","Context accumulation increases latency — each turn processes full history, not just new input","No built-in conversation state persistence — requires application-level session management","Hallucination risk increases with conversation length as model conflates context from distant turns"],"requires":["Inference engine supporting context window management (llama.cpp, Ollama, vLLM)","Application-level conversation history tracking and truncation logic","8GB+ RAM or VRAM","Understanding of context window limitations for prompt engineering"],"input_types":["current user message (Japanese text)","conversation history as formatted string or structured array","optional: system prompt specifying translation style or terminology"],"output_types":["translated response preserving context from conversation history","optional: confidence scores for context-dependent translations","optional: token usage metrics for context window monitoring"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["llama.cpp or compatible GGUF inference engine (Ollama, LM Studio, or vLLM with GGUF support)","8GB+ VRAM (GPU) or 16GB+ system RAM (CPU inference)","Python 3.8+ with transformers library or compatible inference framework","~4.5GB disk space for model weights (quantized GGUF format)","llama.cpp (v0.1.0+) or compatible runtime (Ollama 0.1.0+, LM Studio 0.2.0+, vLLM 0.2.0+)","4-8GB RAM for model loading (vs 28GB+ for FP32 equivalent)","Modern CPU with AVX2 support for optimal inference speed (Intel Haswell+ or AMD Ryzen+)","Optional: CUDA 11.8+ or Metal (macOS) for GPU acceleration","Understanding of Japanese linguistic features (particles, honorifics, speech levels) for prompt engineering","Optional: glossary or terminology database for consistent character name/term translation"],"failure_modes":["GGUF quantization introduces ~2-5% BLEU score degradation vs full-precision FP32 model","14B parameter size requires minimum 8GB VRAM for GPU inference or 16GB+ RAM for CPU inference","Optimized primarily for anime/manga/light novel domains — general domain translation quality may be lower than GPT-4 or DeepL","No built-in batch processing API — requires manual loop implementation for multi-document translation","Context window limited to ~2048 tokens, making long-form document translation require chunking strategies","CPU inference speed ~5-10 tokens/second (vs 50-100 tokens/sec on modern GPUs) — unsuitable for real-time interactive translation","GGUF format is read-only after quantization — fine-tuning requires conversion back to HF format and re-quantization","Limited to inference; no training or LoRA adaptation in GGUF format","Quantization artifacts may cause hallucinations on out-of-domain inputs (e.g., technical jargon, proper nouns)","No standardized way to extract intermediate layer activations for interpretability analysis","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5758686680063781,"quality":0.2,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.766Z","last_scraped_at":"2026-05-03T14:22:53.713Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":310579,"model_likes":11}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=sugoitoolkit--sugoi-14b-ultra-gguf","compare_url":"https://unfragile.ai/compare?artifact=sugoitoolkit--sugoi-14b-ultra-gguf"}},"signature":"tzA80AvdaMTcy+12DzFvT7FoWDSCONQTc4QqOfwc2UE5LVmGAtfGXEYPBIWwo6rkaz2jIqldQQDy5r55fFgOCg==","signedAt":"2026-06-21T01:48:56.429Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/sugoitoolkit--sugoi-14b-ultra-gguf","artifact":"https://unfragile.ai/sugoitoolkit--sugoi-14b-ultra-gguf","verify":"https://unfragile.ai/api/v1/verify?slug=sugoitoolkit--sugoi-14b-ultra-gguf","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}