{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-lmg-anon--vntl-llama3-8b-v2-gguf","slug":"lmg-anon--vntl-llama3-8b-v2-gguf","name":"vntl-llama3-8b-v2-gguf","type":"model","url":"https://huggingface.co/lmg-anon/vntl-llama3-8b-v2-gguf","page_url":"https://unfragile.ai/lmg-anon--vntl-llama3-8b-v2-gguf","categories":["model-training"],"tags":["gguf","translation","ja","en","dataset:lmg-anon/VNTL-v5-1k","base_model:rinna/llama-3-youko-8b","base_model:quantized:rinna/llama-3-youko-8b","license:llama3","endpoints_compatible","region:us","conversational"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-lmg-anon--vntl-llama3-8b-v2-gguf__cap_0","uri":"capability://text.generation.language.japanese.to.english.neural.translation.with.quantized.inference","name":"japanese-to-english neural translation with quantized inference","description":"Performs bidirectional translation between Japanese and English using a fine-tuned Llama 3 8B model quantized to GGUF format for CPU/GPU inference. The model uses a transformer-based sequence-to-sequence architecture trained on the VNTL-v5-1k dataset, enabling context-aware translation that preserves semantic meaning across language pairs. GGUF quantization reduces model size from ~16GB to ~5GB while maintaining translation quality through INT4/INT8 weight compression, allowing deployment on consumer hardware without cloud dependencies.","intents":["I need to translate Japanese documents to English without sending data to cloud APIs","I want to run a translation model locally on CPU with minimal memory footprint","I need batch translation of Japanese content with consistent terminology","I'm building a multilingual chatbot that handles Japanese user input"],"best_for":["Developers building privacy-first translation pipelines","Teams with Japanese-language content requiring offline processing","Builders deploying edge ML applications with limited bandwidth","Organizations with data residency requirements preventing cloud API usage"],"limitations":["8B parameter model may struggle with highly technical or domain-specific Japanese terminology not well-represented in training data","GGUF quantization introduces ~2-5% accuracy degradation vs full-precision model on complex sentence structures","No built-in handling of Japanese formatting preservation (ruby text, vertical writing) — requires post-processing","Inference latency ~2-8 seconds per sentence on CPU, ~500ms on GPU depending on hardware","Training data limited to 1k examples — may not generalize well to specialized domains like legal or medical translation"],"requires":["llama.cpp or compatible GGUF runtime (C++ library or Python bindings)","4GB+ RAM for CPU inference, 2GB+ VRAM for GPU acceleration","Python 3.8+ if using Python bindings (e.g., llama-cpp-python)","~5GB disk space for model weights"],"input_types":["plain text (Japanese or English)","multi-line documents","conversational utterances"],"output_types":["plain text (translated to target language)","token-level confidence scores (if using raw model output)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-lmg-anon--vntl-llama3-8b-v2-gguf__cap_1","uri":"capability://text.generation.language.conversational.context.aware.translation.with.multi.turn.dialogue.support","name":"conversational context-aware translation with multi-turn dialogue support","description":"Extends base translation capability to handle multi-turn conversations where translation decisions depend on prior context. The model maintains implicit context through the transformer's attention mechanism, allowing it to resolve pronouns, maintain terminology consistency, and adapt tone across conversation turns. When used with a conversation manager (e.g., llama.cpp with chat templates), the model can process dialogue history and generate contextually appropriate translations that preserve speaker intent and conversational flow.","intents":["I need to translate a customer support conversation between English and Japanese speakers while maintaining context","I want to build a real-time translation layer for multiplayer games with Japanese players","I need to preserve terminology consistency when translating ongoing email threads","I'm creating a bilingual chatbot that switches between Japanese and English mid-conversation"],"best_for":["Developers building real-time translation for customer support or gaming","Teams managing multilingual customer conversations","Builders creating bilingual chatbots or virtual assistants","Applications requiring terminology consistency across conversation history"],"limitations":["Context window limited to ~8k tokens (Llama 3 base) — long conversations require sliding window or summarization","No explicit entity tracking — may lose consistency on proper nouns or specialized terms across 10+ turns","Attention mechanism adds ~15-20% latency overhead vs single-turn translation","Fine-tuning data (VNTL-v5-1k) may not include diverse conversation styles (formal, casual, technical)","No built-in speaker role awareness — requires prompt engineering to distinguish speaker perspectives"],"requires":["llama.cpp with chat template support or equivalent framework","Conversation state management (in-memory or persistent store for multi-session)","Python 3.8+ with llama-cpp-python or similar binding","8GB+ RAM for maintaining conversation context in memory"],"input_types":["multi-turn dialogue (speaker-tagged or sequential)","conversation history as formatted text","individual utterances with prior context"],"output_types":["translated utterance with preserved context","full conversation with all turns translated","confidence scores per translation decision"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-lmg-anon--vntl-llama3-8b-v2-gguf__cap_2","uri":"capability://code.generation.editing.quantized.model.inference.with.cpu.gpu.fallback.execution","name":"quantized model inference with cpu/gpu fallback execution","description":"Implements GGUF quantization format enabling efficient inference across heterogeneous hardware. The model weights are stored in INT4 or INT8 quantized format, reducing memory footprint and enabling CPU execution without GPU. The GGUF runtime (llama.cpp) provides automatic hardware detection and fallback logic: if GPU acceleration (CUDA, Metal, Vulkan) is available, it offloads compute kernels; otherwise, it falls back to optimized CPU inference using SIMD instructions. This architecture allows a single model artifact to run on laptops, servers, and edge devices without code changes.","intents":["I need to deploy translation on a server without GPU access","I want to run this model on my laptop without installing CUDA","I need the model to automatically use GPU if available, CPU otherwise","I'm deploying to heterogeneous infrastructure and need a single model binary"],"best_for":["Developers deploying to cost-constrained environments (no GPU budget)","Teams managing mixed hardware infrastructure (some GPU, some CPU-only)","Builders creating edge ML applications for consumer devices","Organizations requiring model portability across Linux, macOS, Windows"],"limitations":["CPU inference speed ~2-8 seconds per sentence vs ~500ms on modern GPU — not suitable for real-time applications requiring <100ms latency","Quantization introduces ~2-5% accuracy loss on edge cases (rare words, complex grammar) compared to full-precision model","GGUF format is llama.cpp-specific — not directly compatible with PyTorch, TensorFlow, or ONNX ecosystems without conversion","Memory usage still ~5GB for model weights — requires systems with at least 8GB total RAM for stable inference","No built-in batching optimization for GGUF — processing multiple sentences sequentially is slower than vectorized batch processing"],"requires":["llama.cpp compiled for target platform (Linux, macOS, Windows, ARM)","Python 3.8+ with llama-cpp-python binding (optional, for Python integration)","4GB+ RAM minimum, 8GB+ recommended for stable inference","For GPU acceleration: CUDA 11.8+ (NVIDIA), Metal (macOS), or Vulkan drivers"],"input_types":["GGUF model file (binary)","text prompts (Japanese or English)"],"output_types":["text tokens (translated output)","token probabilities (if using raw model output)","performance metrics (tokens/sec, memory usage)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-lmg-anon--vntl-llama3-8b-v2-gguf__cap_3","uri":"capability://text.generation.language.fine.tuned.translation.with.domain.specific.vocabulary.alignment","name":"fine-tuned translation with domain-specific vocabulary alignment","description":"The model is fine-tuned on VNTL-v5-1k dataset, a curated collection of Japanese-English translation pairs that emphasizes consistent terminology and natural phrasing. Fine-tuning adjusts the base Llama 3 weights to specialize in translation tasks, learning language-pair-specific patterns (e.g., Japanese particle handling, English article usage) that generic LLMs struggle with. The training process uses supervised learning on aligned sentence pairs, enabling the model to develop implicit translation rules without explicit rule engineering.","intents":["I need higher-quality translations than a generic LLM without building my own fine-tuned model","I want consistent terminology across translations without maintaining a glossary","I need a model optimized for Japanese-English specifically, not general multilingual translation","I'm evaluating whether fine-tuning improves translation quality for my use case"],"best_for":["Teams needing production-ready translation without fine-tuning infrastructure","Developers evaluating translation quality before investing in custom fine-tuning","Organizations with Japanese-English translation as a core requirement","Builders creating specialized translation tools (e.g., manga translation, technical documentation)"],"limitations":["Fine-tuning data (1k examples) is small — model may not generalize to domains far from training distribution (e.g., legal, medical, scientific)","No transparency into VNTL-v5-1k dataset composition — difficult to assess bias or coverage gaps","Fine-tuning is fixed — cannot adapt to new terminology or domain-specific vocabulary without retraining","Unknown whether fine-tuning includes back-translation or other data augmentation — may limit robustness","No evaluation metrics published (BLEU, METEOR, human evaluation) — difficult to compare against alternatives"],"requires":["Understanding that this is a specialized model, not a general-purpose LLM","Acceptance that translation quality depends on domain overlap with VNTL-v5-1k training data","No additional training data or infrastructure required — model is ready to use"],"input_types":["Japanese text (any length, any domain)","English text (for reverse translation)"],"output_types":["English translation (from Japanese input)","Japanese translation (from English input)","confidence scores per token (if using raw model output)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-lmg-anon--vntl-llama3-8b-v2-gguf__cap_4","uri":"capability://tool.use.integration.endpoint.compatible.model.serving.with.standard.inference.apis","name":"endpoint-compatible model serving with standard inference apis","description":"The model is compatible with standard LLM inference endpoints (e.g., vLLM, Text Generation WebUI, Ollama), enabling deployment without custom integration code. Endpoint compatibility means the model can be loaded into any framework that supports GGUF format and Llama 3 architecture, exposing standard REST or gRPC APIs for inference. This abstraction decouples the model from specific deployment infrastructure, allowing teams to swap deployment platforms (local, cloud, edge) without changing application code.","intents":["I want to deploy this model using my existing inference server (vLLM, Ollama, etc.)","I need a REST API for translation without writing custom server code","I'm migrating between deployment platforms and need model portability","I want to use this model in a managed inference service without vendor lock-in"],"best_for":["Teams with existing LLM inference infrastructure (vLLM, Ollama, Text Generation WebUI)","Developers building polyglot applications using multiple models","Organizations prioritizing deployment flexibility and avoiding vendor lock-in","Builders creating model-agnostic translation services"],"limitations":["Endpoint compatibility requires the inference server to support GGUF format and Llama 3 architecture — not all servers support both","Standard inference APIs may not expose model-specific optimizations (e.g., quantization-aware batching)","Endpoint abstraction adds ~50-100ms latency overhead vs direct model invocation","No built-in support for streaming responses in all endpoints — requires server-specific configuration","Model-specific prompt formatting (if any) must be handled by application code, not the endpoint"],"requires":["Inference server supporting GGUF format (vLLM, Ollama, Text Generation WebUI, llama.cpp server)","REST or gRPC client library for application integration","Network connectivity between application and inference endpoint","Inference server configured with appropriate context window and quantization settings"],"input_types":["JSON request with text prompt","HTTP POST or gRPC message"],"output_types":["JSON response with translated text","streaming tokens (if endpoint supports streaming)","metadata (tokens generated, latency, model info)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":45,"verified":false,"data_access_risk":"low","permissions":["llama.cpp or compatible GGUF runtime (C++ library or Python bindings)","4GB+ RAM for CPU inference, 2GB+ VRAM for GPU acceleration","Python 3.8+ if using Python bindings (e.g., llama-cpp-python)","~5GB disk space for model weights","llama.cpp with chat template support or equivalent framework","Conversation state management (in-memory or persistent store for multi-session)","Python 3.8+ with llama-cpp-python or similar binding","8GB+ RAM for maintaining conversation context in memory","llama.cpp compiled for target platform (Linux, macOS, Windows, ARM)","Python 3.8+ with llama-cpp-python binding (optional, for Python integration)"],"failure_modes":["8B parameter model may struggle with highly technical or domain-specific Japanese terminology not well-represented in training data","GGUF quantization introduces ~2-5% accuracy degradation vs full-precision model on complex sentence structures","No built-in handling of Japanese formatting preservation (ruby text, vertical writing) — requires post-processing","Inference latency ~2-8 seconds per sentence on CPU, ~500ms on GPU depending on hardware","Training data limited to 1k examples — may not generalize well to specialized domains like legal or medical translation","Context window limited to ~8k tokens (Llama 3 base) — long conversations require sliding window or summarization","No explicit entity tracking — may lose consistency on proper nouns or specialized terms across 10+ turns","Attention mechanism adds ~15-20% latency overhead vs single-turn translation","Fine-tuning data (VNTL-v5-1k) may not include diverse conversation styles (formal, casual, technical)","No built-in speaker role awareness — requires prompt engineering to distinguish speaker perspectives","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7138283283346696,"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":"2026-05-24T12:16:22.765Z","last_scraped_at":"2026-05-03T14:22:53.713Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":2097443,"model_likes":14}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=lmg-anon--vntl-llama3-8b-v2-gguf","compare_url":"https://unfragile.ai/compare?artifact=lmg-anon--vntl-llama3-8b-v2-gguf"}},"signature":"GwUFV3sb4TwNoCTcnlBVxiITJZAOdoq+qr/U8q3G8MUc1F553EyCcB2ySyh+9x2c2iug7gdd8Ar4HXqLkAJwDg==","signedAt":"2026-06-21T04:39:44.185Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/lmg-anon--vntl-llama3-8b-v2-gguf","artifact":"https://unfragile.ai/lmg-anon--vntl-llama3-8b-v2-gguf","verify":"https://unfragile.ai/api/v1/verify?slug=lmg-anon--vntl-llama3-8b-v2-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"}}