{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-alpindale-goliath-120b","slug":"alpindale-goliath-120b","name":"Goliath 120B","type":"model","url":"https://openrouter.ai/models/alpindale~goliath-120b","page_url":"https://unfragile.ai/alpindale-goliath-120b","categories":["chatbots-assistants"],"tags":["alpindale","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$3.75e-6 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-alpindale-goliath-120b__cap_0","uri":"capability://text.generation.language.merged.model.instruction.following.with.dual.fine.tune.synthesis","name":"merged-model-instruction-following-with-dual-fine-tune-synthesis","description":"Executes instruction-following tasks by leveraging a merged architecture combining two independently fine-tuned Llama 70B models (Xwin for competitive performance, Euryale for creative/uncensored outputs) into a single 120B parameter space. The merge framework preserves specialized capabilities from both source models while distributing computational load across the expanded parameter count, enabling nuanced responses that balance instruction adherence with creative flexibility without requiring separate model switching.","intents":["I need a model that can follow complex instructions while maintaining creative output without content restrictions","I want instruction-following performance that combines competitive benchmarks with uncensored reasoning","I need to avoid model switching overhead when alternating between strict and creative task requirements"],"best_for":["developers building uncensored AI assistants and creative applications","teams requiring high-parameter models with balanced instruction-following and creative capabilities","researchers experimenting with model merging techniques and multi-fine-tune synthesis"],"limitations":["Merged model architecture may introduce subtle capability degradation in specialized domains where one source model excelled — no published ablation studies quantifying per-domain performance loss","120B parameter count requires substantial VRAM (estimated 240GB+ for full precision inference), limiting deployment to enterprise-grade GPU clusters","No published benchmarks isolating merged model performance vs. individual source models, making it difficult to assess whether merge framework preserved or degraded specific capabilities","Merge framework details not fully documented — unclear how parameter conflicts between Xwin and Euryale fine-tuning objectives were resolved during synthesis"],"requires":["API access via OpenRouter (no local deployment option provided)","Sufficient API quota and rate limits for 120B model inference","Understanding of merged model behavior differences from base Llama 70B"],"input_types":["text (natural language instructions, prompts, multi-turn conversations)"],"output_types":["text (natural language responses, code, creative content, reasoning chains)"],"categories":["text-generation-language","model-merging"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-alpindale-goliath-120b__cap_1","uri":"capability://text.generation.language.multi.turn.conversation.context.management","name":"multi-turn-conversation-context-management","description":"Maintains coherent multi-turn dialogue by processing conversation history as sequential context within the model's token window, enabling the 120B merged model to track conversational state, user preferences, and prior statements across extended exchanges. The implementation relies on the underlying Llama architecture's attention mechanism to weight recent and salient context, with OpenRouter's API handling session management and context windowing to prevent token overflow while preserving semantic continuity.","intents":["I need to maintain coherent multi-turn conversations without losing context about prior statements or user preferences","I want the model to reference and build upon earlier parts of a conversation naturally","I need to debug conversation quality by understanding how much context the model is actually using"],"best_for":["developers building chatbot and conversational AI applications","teams deploying customer support or interactive assistant systems","researchers studying context utilization and attention patterns in large merged models"],"limitations":["Token window size limits total conversation length before context truncation — exact window size not specified in documentation, likely 4K-8K tokens based on Llama architecture","No explicit control over context prioritization strategy — model uses learned attention weights rather than explicit recency or importance weighting","Conversation state is stateless per API call — no built-in session persistence, requiring client-side conversation history management","Long conversations may suffer from 'lost in the middle' problem where mid-conversation context receives lower attention than recent exchanges"],"requires":["API access via OpenRouter with conversation history formatting","Client-side implementation of conversation state management and history tracking","Understanding of token counting to avoid exceeding context window"],"input_types":["text (user messages, system prompts, conversation history)"],"output_types":["text (assistant responses maintaining conversational context)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-alpindale-goliath-120b__cap_2","uri":"capability://text.generation.language.uncensored.creative.reasoning.with.fine.tune.blending","name":"uncensored-creative-reasoning-with-fine-tune-blending","description":"Generates creative, uncensored, and exploratory reasoning by blending the Euryale fine-tune (optimized for creative and unrestricted outputs) with Xwin's instruction-following precision through the merged model architecture. The dual fine-tune synthesis allows the model to produce creative content, roleplay scenarios, and exploratory reasoning without the safety guardrails typically present in standard instruction-tuned models, while maintaining coherence through Xwin's competitive instruction-following training.","intents":["I need creative writing and storytelling without content restrictions or safety filtering","I want the model to engage in uncensored roleplay and character simulation","I need exploratory reasoning that doesn't self-censor or refuse creative premises"],"best_for":["creative writers and fiction authors using AI for brainstorming and content generation","developers building creative applications (games, interactive fiction, worldbuilding tools)","researchers studying model behavior without safety constraints and fine-tune interaction effects"],"limitations":["Uncensored outputs may violate content policies of downstream platforms or applications — users bear responsibility for output filtering and compliance","No explicit safety guardrails or refusal mechanisms — model may generate harmful, illegal, or offensive content without warnings","Euryale fine-tune objectives not fully documented — unclear what specific safety constraints were removed or how they interact with Xwin's instruction-following training","Potential for jailbreak vulnerability if users exploit the uncensored nature to extract harmful outputs or bypass intended use restrictions"],"requires":["API access via OpenRouter","Responsibility for output validation and content filtering in downstream applications","Understanding of ethical implications and legal liability for uncensored model outputs"],"input_types":["text (creative prompts, roleplay scenarios, uncensored instructions)"],"output_types":["text (creative content, uncensored reasoning, roleplay responses)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-alpindale-goliath-120b__cap_3","uri":"capability://text.generation.language.competitive.benchmark.instruction.following.via.xwin.synthesis","name":"competitive-benchmark-instruction-following-via-xwin-synthesis","description":"Achieves competitive performance on instruction-following benchmarks (MMLU, MT-Bench, etc.) by incorporating Xwin fine-tuning into the merged 120B architecture, which was specifically optimized for high benchmark scores through reinforcement learning from human feedback (RLHF) and competitive instruction-tuning. The merge framework preserves Xwin's benchmark-optimized weights while expanding the parameter space, potentially improving generalization across diverse instruction-following tasks without sacrificing the specialized training that drives benchmark performance.","intents":["I need a model with strong performance on standard instruction-following benchmarks for evaluation and comparison","I want competitive instruction-following quality for knowledge-intensive tasks and reasoning problems","I need to validate model capability against published benchmarks before production deployment"],"best_for":["teams evaluating model quality against standard benchmarks before deployment","researchers comparing merged model performance to baseline Llama 70B and other 120B+ models","developers requiring high instruction-following accuracy for knowledge-intensive applications"],"limitations":["Benchmark performance of merged model not published — unclear whether merge framework preserved, degraded, or improved Xwin's competitive performance","Benchmark scores may not correlate with real-world application performance — instruction-following optimization may not transfer to domain-specific tasks","No ablation studies isolating Xwin's contribution to merged model performance vs. Euryale's impact","Benchmark optimization may introduce overfitting to specific evaluation formats, reducing robustness on out-of-distribution instruction-following tasks"],"requires":["API access via OpenRouter","Benchmark evaluation infrastructure for validating instruction-following performance","Understanding of benchmark limitations and their relationship to real-world capability"],"input_types":["text (instruction-following prompts, knowledge questions, reasoning tasks)"],"output_types":["text (instruction-following responses, knowledge answers, reasoning chains)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-alpindale-goliath-120b__cap_4","uri":"capability://tool.use.integration.api.based.inference.with.openrouter.integration","name":"api-based-inference-with-openrouter-integration","description":"Provides access to the 120B merged model through OpenRouter's API infrastructure, handling model serving, load balancing, and request routing without requiring local deployment or GPU infrastructure. The integration abstracts away model hosting complexity, offering pay-per-token pricing and automatic failover across OpenRouter's provider network, while maintaining compatibility with standard LLM API patterns (messages format, streaming, token counting) that enable easy integration into existing applications.","intents":["I need to use a 120B model without managing GPU infrastructure or deployment complexity","I want to integrate Goliath 120B into my application with minimal infrastructure overhead","I need flexible, pay-per-token pricing without long-term commitment or resource provisioning"],"best_for":["developers and startups without GPU infrastructure or MLOps expertise","teams requiring flexible model access without long-term infrastructure investment","applications with variable load that benefit from OpenRouter's auto-scaling and provider failover"],"limitations":["API latency adds ~500ms-2s per request compared to local inference, depending on OpenRouter's load and network conditions","Pay-per-token pricing scales linearly with usage — high-volume applications may find local deployment more cost-effective","Dependency on OpenRouter's availability and uptime — no SLA guarantees published, potential for service degradation during peak usage","No fine-tuning or model customization available through OpenRouter API — users cannot adapt the merged model to domain-specific tasks","Request rate limits and quota management required — no published rate limit documentation for Goliath 120B specifically"],"requires":["OpenRouter API key and account with sufficient credits","HTTP client library or SDK compatible with OpenRouter's API (Python requests, Node.js fetch, etc.)","Understanding of token counting and pricing calculation for budget management"],"input_types":["text (prompts, messages in OpenRouter format)"],"output_types":["text (streaming or non-streaming responses, token usage metadata)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"low","permissions":["API access via OpenRouter (no local deployment option provided)","Sufficient API quota and rate limits for 120B model inference","Understanding of merged model behavior differences from base Llama 70B","API access via OpenRouter with conversation history formatting","Client-side implementation of conversation state management and history tracking","Understanding of token counting to avoid exceeding context window","API access via OpenRouter","Responsibility for output validation and content filtering in downstream applications","Understanding of ethical implications and legal liability for uncensored model outputs","Benchmark evaluation infrastructure for validating instruction-following performance"],"failure_modes":["Merged model architecture may introduce subtle capability degradation in specialized domains where one source model excelled — no published ablation studies quantifying per-domain performance loss","120B parameter count requires substantial VRAM (estimated 240GB+ for full precision inference), limiting deployment to enterprise-grade GPU clusters","No published benchmarks isolating merged model performance vs. individual source models, making it difficult to assess whether merge framework preserved or degraded specific capabilities","Merge framework details not fully documented — unclear how parameter conflicts between Xwin and Euryale fine-tuning objectives were resolved during synthesis","Token window size limits total conversation length before context truncation — exact window size not specified in documentation, likely 4K-8K tokens based on Llama architecture","No explicit control over context prioritization strategy — model uses learned attention weights rather than explicit recency or importance weighting","Conversation state is stateless per API call — no built-in session persistence, requiring client-side conversation history management","Long conversations may suffer from 'lost in the middle' problem where mid-conversation context receives lower attention than recent exchanges","Uncensored outputs may violate content policies of downstream platforms or applications — users bear responsibility for output filtering and compliance","No explicit safety guardrails or refusal mechanisms — model may generate harmful, illegal, or offensive content without warnings","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.24,"match_graph":0.25,"freshness":0.9,"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":"2026-05-24T12:16:24.483Z","last_scraped_at":"2026-05-03T15:20:45.777Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=alpindale-goliath-120b","compare_url":"https://unfragile.ai/compare?artifact=alpindale-goliath-120b"}},"signature":"ONG+un7k/Y34H2e/ci9LayC15XxD0dmlWTFI3uUvrOs+gN0y1DUWBd095yn3ZJ3WJdR/YMRfQNiF3713vnotBw==","signedAt":"2026-06-18T03:35:53.344Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/alpindale-goliath-120b","artifact":"https://unfragile.ai/alpindale-goliath-120b","verify":"https://unfragile.ai/api/v1/verify?slug=alpindale-goliath-120b","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"}}