{"uid":"cap_8HqWPl8kTX-smhj6w9W5t","slug":"fine-tuned-model-weights-merger-mcp-23f6cf17","name":"Fine-Tuned Model Weights Merger MCP","description":"The premier global index of 1,069 monetized MCP nodes across 205 specialized subdomains. Gasless USDC runtime settlements via x402 V2 Spec on Base L2. Save 95% token context.","url":"https://api.m2mcent.com/fine-tuned-model-weights-merger-mcp/api/process?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":null,"responseSchema":{"type":"json","example":{"success":true}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.4","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.4/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.4","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.4","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_dW4sAVp16JdUDHBoNV9Gn","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.4","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Merges fine-tuned model weights from multiple models into a single combined model via a managed API endpoint.","exampleAgentPrompt":"Can you merge these two sets of fine-tuned model weights into a single combined model? Here's the weight data: [serialized weights JSON].","exampleUseCases":[{"title":"Combining domain-specific fine-tunes","prompt":"I have two fine-tuned models — one trained on medical text and one on legal text — can you merge their weights so I get a single model that handles both domains?"},{"title":"Blending LoRA adapter weights","prompt":"I've got multiple LoRA fine-tune weight sets from different training runs and I want to merge them into one unified adapter. Can you combine these weight configurations for me?"},{"title":"Averaging model checkpoints for robustness","prompt":"I ran several fine-tuning experiments and want to average the model weights across checkpoints to get a more robust model. Can you merge these weight snapshots into one?"}],"resultDescription":"Returns merged or combined model weight data as a processed output, representing the unified weights from the input fine-tuned models.","failureModes":["Invalid or malformed weight data string returns an error","Incompatible model architectures may cause merge failures","Missing or empty 'data' field results in a bad request error","Oversized weight payloads may exceed processing limits","Network timeout for large weight datasets"],"whenToPreferThis":"Choose this endpoint when you need to programmatically merge fine-tuned model weights from multiple models into a single unified model, especially in automated ML pipelines or agent workflows where model merging is a step in a larger process.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-12T12:51:16.573Z","isFirstParty":false,"canonicalSlug":"fine-tuned-model-weights-merger-mcp-23f6cf17"}