Unsloth MCP Server
Related Servers
Alternatives to Unsloth MCP Server
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityBmaintenanceEnables profiling and optimizing edge and on-device LLM inference by measuring TTFT, TPOT, tokens/second throughput, and P50/P90/P99 jitter while providing standard-library primitives for quantization, paged attention, speculative decoding, and prefix caching.7MIT
- AlicenseNot gradedqualityBmaintenanceEnables zero-dependency INT8 symmetric quantization of LLM weights and activations on a per-tensor or per-channel basis, reporting SNR and MSE reconstruction telemetry along with TTFT/TPOT latency and jitter metrics. Also exposes edge inference primitives such as PagedAttention block allocation, radix prefix caching, and speculative decoding verification through the Model Context Protocol.7MIT
- AlicenseAqualityAmaintenanceOwn your sovereign AI model. Domain-specific fine-tuning of open-source LLMs and SLMs with total control and zero infrastructure hassle. Tuning Engines provides specialized tuning agents to tailor top open models to your needs — fast, predictable, fully delivered. Fine-tune Qwen, Llama, DeepSeek, Mistral, Gemma, Phi, StarCoder, and CodeLlama models from 1B to 72B parameters on your data via CLI o38120 npm6MIT
- AlicenseNot gradedqualityBmaintenanceEnables MCP clients to manage LLM inference memory through virtual paged-attention KV-cache block mapping, non-contiguous physical page allocation, zero-copy fragmentation tracking, radix-trie prefix caching, INT8 quantized compute, and speculative decoding verification. It also exposes prefill and decode latency telemetry so edge deployments can be benchmarked and tuned without external dependencies.7MIT
- AlicenseNot gradedqualityNot gradedmaintenanceEnables benchmarking of Large Language Model APIs by measuring performance metrics such as generation throughput, prompt throughput, and Time To First Token (TTFT) with configurable concurrency levels and parameters.1MIT
- AlicenseAqualityDmaintenanceEnables users to convert structured data into Token-Oriented Object Notation (TOON) to reduce LLM token usage and costs by up to 70%. It provides tools for encoding, decoding, and analyzing data formats like JSON, CSV, and XML to optimize prompt efficiency.414 npm3MIT
TDQS
Scored across 6 tools
Each tool has a clearly distinct purpose with no overlap: checking installation, listing models, loading models, fine-tuning, generating text, and exporting models. The descriptions make it unambiguous which tool to use for each step in the workflow.
All tools follow a consistent verb_noun pattern with clear, descriptive names (e.g., check_installation, export_model, finetune_model). There are no deviations in naming conventions or style mixing.
With 6 tools, this server is well-scoped for its purpose of fine-tuning and using models with Unsloth optimizations. Each tool earns its place by covering a distinct, essential operation in the model lifecycle.
The tool set provides complete coverage for the domain: from checking prerequisites and listing models, through loading, fine-tuning, and generating text, to exporting the final model. There are no obvious gaps or dead ends in the workflow.