Sugoi-14B-Ultra-GGUF

ModelFree

translation model by undefined. 2,20,453 downloads.

Open Source

/ 100

5 capabilities

Capabilities5 decomposed

japanese-to-english neural translation with gguf quantization

Medium confidence

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.

Solves for

Translate Japanese anime subtitles or manga text to English without cloud API dependenciesRun a local translation service on consumer hardware without GPU requirementsIntegrate Japanese-English translation into offline applications or edge devicesBatch-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

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

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

What makes it unique

Combines GGUF quantization (enabling sub-8GB inference) with domain-specific fine-tuning on anime/manga corpora, whereas most open-source translation models (Opus-MT, M2M-100) target general domains and require 16GB+ VRAM unquantized. The Sugoi toolkit specifically optimized for Japanese creative media translation through curated training data.

vs alternatives

Faster inference than full-precision models (2-3x speedup on CPU) and lower memory footprint than Google Translate API while maintaining anime-specific translation quality; trades some accuracy vs GPT-4 for privacy, cost, and offline availability.

gguf format model loading and inference with llama.cpp compatibility

Medium confidence

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.

Solves for

Run translation inference on CPU-only machines or edge devices without GPU setup complexityIntegrate translation into containerized applications with minimal dependency footprintDeploy 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

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+)

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

What makes it unique

Uses GGUF format with layer-wise quantization awareness rather than naive post-training quantization, preserving translation quality across domain shifts. Most alternatives (ONNX, TensorRT) require framework-specific tooling; GGUF enables single-format deployment across CPU, GPU, and edge devices via llama.cpp ecosystem.

vs alternatives

Smaller model size and faster CPU inference than ONNX quantization while maintaining broader hardware compatibility than TensorRT (NVIDIA-only); simpler deployment than PyTorch quantization without sacrificing inference speed.

anime and manga domain-specific translation with specialized vocabulary

Medium confidence

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.

Solves for

Translate anime episode scripts with accurate character name and honorific preservationConvert manga dialogue while maintaining speech style differentiation (formal vs casual, gendered speech)Localize light novel text with culturally-aware translation of Japanese idioms and referencesBuild 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

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)

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

What makes it unique

Fine-tuned specifically on anime/manga/light novel corpora rather than generic parallel corpora, with explicit handling of Japanese honorifics, character speech patterns, and creative terminology. Most general translation models (Google Translate, DeepL) treat anime text as outliers; Sugoi embeds domain knowledge into the model weights through curated training data.

vs alternatives

Outperforms general-purpose models on anime-specific terminology and cultural references while maintaining competitive BLEU scores on general Japanese-English translation; trades general-domain accuracy for specialized anime/manga quality.

batch translation with streaming inference and token-level control

Medium confidence

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.

Solves for

Process subtitle files with 1000+ lines efficiently without per-request overheadStream translation output to UI in real-time for interactive translation toolsControl translation length and style via temperature/sampling parametersImplement 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

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)

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

What makes it unique

Leverages llama.cpp's streaming inference and sampling parameter exposure to enable token-level control and confidence scoring, whereas most cloud translation APIs (Google, DeepL) return complete translations without intermediate tokens or probability data. Enables confidence-based quality filtering and UI streaming patterns.

vs alternatives

Provides token-level transparency and streaming output for interactive UIs, unavailable in cloud APIs; trades API simplicity for fine-grained control and offline operation.

conversational translation with multi-turn context preservation

Medium confidence

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.

Solves for

Translate anime episode dialogue with consistent character voice across multiple scenesPreserve pronoun references and character relationships across conversation turnsBuild chatbot-style translation interfaces where users refine translations iterativelyMaintain 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

Requires

Inference engine supporting context window management (llama.cpp, Ollama, vLLM)

Application-level conversation history tracking and truncation logic

8GB+ RAM or VRAM

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

What makes it unique

Leverages transformer self-attention over full conversation history to maintain context and resolve pronouns/references, whereas most translation APIs treat each request independently. The 2048-token context window enables multi-turn dialogue translation without explicit coreference resolution modules.

vs alternatives

Maintains dialogue coherence across turns better than stateless APIs (Google Translate, DeepL) while avoiding the complexity of explicit coreference resolution systems; trades context window size for simplicity.

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

Artifacts that share capabilities with Sugoi-14B-Ultra-GGUF, ranked by overlap. Discovered automatically through the match graph.

Model44

vntl-llama3-8b-v2-gguf

translation model by undefined. 18,25,925 downloads.

japanese-to-english neural translation with quantized inferencequantized model inference with cpu/gpu fallback executionfine-tuned translation with domain-specific vocabulary alignment

3 shared capabilities

Model40

Hunyuan-MT-7B-GGUF

translation model by undefined. 5,79,455 downloads.

quantized model inference with gguf format optimizationmultilingual neural machine translation with 19-language support

2 shared capabilities

Repository28

TurboPilot

A self-hosted copilot clone that uses the library behind llama.cpp to run the 6 billion parameter Salesforce Codegen model in 4 GB of...

gguf model format parsing and weight loadingquantized model loading and memory-mapped inference

2 shared capabilities

Framework46

Llamafile

Single-file executable LLMs — bundle model + inference, runs on any OS with zero install.

ggml-based tensor operations with quantization supportmodel quantization and format conversion with gguf standardization

2 shared capabilities

Repository23

llama.cpp

Inference of Meta's LLaMA model (and others) in pure C/C++. #opensource

model conversion and quantization from huggingface formatsquantized model inference with multi-backend acceleration

2 shared capabilities

Repository22

llama-cpp-python

Python bindings for the llama.cpp library

cpu-optimized llm inference with quantized model loading

1 shared capability

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
✓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

Known 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
⚠CPU inference speed ~5-10 tokens/second (vs 50-100 tokens/sec on modern GPUs) — unsuitable for real-time interactive translation

Requirements

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

Input / Output

Accepts: 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), GGUF binary format (pre-quantized model weights), text prompts in JSON or plain text format, optional system prompts for translation style control, Japanese text with anime/manga/light novel origin, dialogue-heavy content with character names and honorifics, plain text or structured subtitle formats (SRT, ASS), plain text strings or arrays of strings, subtitle file formats (SRT, ASS, VTT) with manual parsing, JSON arrays with source text and optional metadata, current user message (Japanese text), conversation history as formatted string or structured array, optional: system prompt specifying translation style or terminology

Produces: plain text translation (UTF-8 encoded), token-level confidence scores (if inference engine supports logits output), structured translation with source-target alignment metadata, text tokens streamed or batched, optional: per-token logits and probabilities, optional: timing metrics (tokens/sec, latency per token), English translation preserving character voice and cultural nuance, optional: alignment metadata mapping source characters to translated tokens, optional: confidence scores per translated phrase, 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), translated response preserving context from conversation history, optional: confidence scores for context-dependent translations, optional: token usage metrics for context window monitoring

UnfragileRank

Adoption55%(40% weight)

Quality13%(20% weight)

Ecosystem50%(15% weight)

Match Graph10%(20% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: Model

5 capabilities

Visit Sugoi-14B-Ultra-GGUF→

Model Details

huggingface

Provider

220,453

Downloads

Tasks

translation

About

sugoitoolkit/Sugoi-14B-Ultra-GGUF — a translation model on HuggingFace with 2,20,453 downloads

Alternatives to Sugoi-14B-Ultra-GGUF

Relativity32Product

Revolutionize data discovery and case strategy with AI-driven, secure...

Compare →

vidIQ29Product

Elevate YouTube success with AI-driven analytics and optimization...

Compare →

HubSpot33Product

Unify marketing, sales, CRM; AI-driven insights—boost...

Compare →

Google Translate30Product

Instant translations across 100+ languages, voice, text, and...

Compare →

Are you the builder of Sugoi-14B-Ultra-GGUF?

Claim this artifact to get a verified badge, access match analytics, see which intents users search for, and manage your listing.

Claim this artifact →Verification via email

Get the weekly brief

New tools, rising stars, and what's actually worth your time. No spam.

Data Sources

huggingface

Looking for something else?

Search →

Capabilities5 decomposed

japanese-to-english neural translation with gguf quantization

Medium confidence

Solves for

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

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

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

What makes it unique

vs alternatives

gguf format model loading and inference with llama.cpp compatibility

Medium confidence

Solves for

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

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+)

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

What makes it unique

vs alternatives

anime and manga domain-specific translation with specialized vocabulary

Medium confidence

Solves for

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

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)

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

What makes it unique

vs alternatives

batch translation with streaming inference and token-level control

Medium confidence

Solves for

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

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)

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

What makes it unique

vs alternatives

Provides token-level transparency and streaming output for interactive UIs, unavailable in cloud APIs; trades API simplicity for fine-grained control and offline operation.

conversational translation with multi-turn context preservation

Medium confidence

Solves for

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

Requires

Inference engine supporting context window management (llama.cpp, Ollama, vLLM)

Application-level conversation history tracking and truncation logic

8GB+ RAM or VRAM

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

What makes it unique

vs alternatives

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Alternatives to Sugoi-14B-Ultra-GGUF

Relativity32Product

Revolutionize data discovery and case strategy with AI-driven, secure...

Compare →

vidIQ29Product

Elevate YouTube success with AI-driven analytics and optimization...

Compare →

HubSpot33Product

Unify marketing, sales, CRM; AI-driven insights—boost...

Compare →

Google Translate30Product

Instant translations across 100+ languages, voice, text, and...

Compare →

Sugoi-14B-Ultra-GGUF

Capabilities5 decomposed

japanese-to-english neural translation with gguf quantization

gguf format model loading and inference with llama.cpp compatibility

anime and manga domain-specific translation with specialized vocabulary

batch translation with streaming inference and token-level control

conversational translation with multi-turn context preservation

Related Artifactssharing capabilities

vntl-llama3-8b-v2-gguf

Hunyuan-MT-7B-GGUF

TurboPilot

Llamafile

llama.cpp

llama-cpp-python

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Model Details

About

Categories

Alternatives to Sugoi-14B-Ultra-GGUF

Are you the builder of Sugoi-14B-Ultra-GGUF?

Get the weekly brief

Data Sources

Sugoi-14B-Ultra-GGUF

Capabilities5 decomposed

japanese-to-english neural translation with gguf quantization

gguf format model loading and inference with llama.cpp compatibility

anime and manga domain-specific translation with specialized vocabulary

batch translation with streaming inference and token-level control

conversational translation with multi-turn context preservation

Related Artifactssharing capabilities

vntl-llama3-8b-v2-gguf

Hunyuan-MT-7B-GGUF

TurboPilot

Llamafile

llama.cpp

llama-cpp-python

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Model Details

About

Categories

Alternatives to Sugoi-14B-Ultra-GGUF

Are you the builder of Sugoi-14B-Ultra-GGUF?

Get the weekly brief

Data Sources