Llm Fine Tuning Dataset

FinetuneDB enables teams to fine‑tune custom large language models with proprietary data. It provides collaborative dataset editing, a prompt playground, human and AI feedback loops, performance metrics, OpenAI SDK integration, and secure, role‑based access.

LiteLLM is an open‑source gateway that unifies access to 100+ LLMs through a single OpenAI‑compatible API, enabling provider fallback, cost tracking, tag‑based budgeting, guardrails, observability, and on‑prem or cloud deployment with a lightweight SDK.

xTuring is an open‑source framework that lets developers and researchers build, fine‑tune, and deploy LLMs efficiently. It supports LoRA adapters, INT8 quantization, custom datasets, offers CLI and notebooks, and provides a unified API for multiple backends.

UBIAI fine‑tunes LLMs with classifiers, retrievers, and reasoning. It automates PDF/DOCX labeling, synthetic data, and quality filtering; offers 15‑minute prompt‑level tuning or 2‑4 hour weight training; exports to GGUF, safetensors, or Hugging Face for API or custom deployment.

MusicLM is an AI tool that generates high-fidelity music text based on prompts and datasets using a hierarchical sequence-to-sequence model. It provides a dataset of 5.5k music-text pairs with rich text descriptions.

BenchLLM evaluates language‑model applications via API or CLI, running JSON/YAML test suites with automated, interactive, or custom strategies. It supports OpenAI, LangChain, and any API, detecting regressions, generating reports, and visualizing results for continuous QA.

dreamlook.ai offers fast, online training and generation for Stable Diffusion 1.5 and SDXL, supporting 1,500 SDXL steps in ~10 min, LoRA extraction, Offset Noise, ControlNet pose control, and a GPU‑free API.

Unsloth Studio is a no-code web UI enabling local training, running, and exporting of open AI models like Qwen3.5 and NVIDIA Nemotron 3, simplifying experimentation for users without extensive technical expertise.

Latitude offers end‑to‑end observability for LLM deployments, recording inputs, outputs, and context. It enables manual annotations, automated error grouping, continuous evaluation, and prompt optimization with GEPA. OTEL telemetry and SDK integrations support major model providers.

FinNLP offers full pipelines for training and finetuning LLMs in finance.

llmarena.ai offers side-by-side LLM comparisons across major providers, showing specs like context window, output capacity, modality and routing options. Filters and role-based categories help developers, ML engineers, product managers and researchers select suitable models.

LLM Arena enables users to compare multiple large language models side-by-side, analyzing features like accuracy and capabilities. It supports up to 10 models, facilitating informed decision-making for researchers and developers in selecting the right LLM for their needs.

LLMOps Space is a global community for LLM practitioners, offering curated content, discussion forums, event recordings, and resources on production deployment, fine‑tuning, observability, and search optimization, plus networking via Discord and newsletters.

Falcon is an open‑source LLM family by the Technology Innovation Institute, spanning 0.09‑180 B parameters. It offers efficient Falcon‑H1 series, Arabic variants, multimodal Falcon‑3, and Falcon‑Mamba 7B, all under permissive licenses.

LM Studio runs open‑source large language models locally on Mac (M‑series), Windows, and Linux, enabling private, offline inference. It offers command‑line and headless deployment, server‑side API, SDKs, a model hub, and LM Link for remote model access.

Fine-tuner is a no-code platform for building sophisticated AI agents with pre-built templates, step-by-step guidance, and customizable AI agents that can adapt to conversation stages and connect to third-party tools.

LLM Price Check aggregates LLM API models and provider details into sortable tables and a cost calculator, showing context windows, input/output cost metrics, and quality indicators to help developers and teams evaluate cost–performance tradeoffs.

Confident AI is an evaluation platform for assessing large language models, enabling benchmarking, unit testing, and A/B testing. It streamlines dataset management and monitoring, ensuring optimal performance and alignment with benchmarks for LLM applications.

Scale AI delivers a full‑stack generative‑AI platform that integrates enterprise data, supports fine‑tuning, RLHF, and model safety evaluation, and enables secure AI agent deployment with compliance‑certified cloud infrastructure for regulated and government use.

LLM Pricing Comparison lets developers and businesses compare token costs, context lengths, and modalities for major large‑language models. An interactive calculator estimates application expenses based on input/output token volumes, helping teams budget AI workloads accurately.

finetunefast streamlines AI model training with pre-configured scripts, hyperparameter optimization, and multi-GPU support. It offers one-click deployment, API generation, and monitoring, catering to both novice and expert users for various machine learning applications.

Reflection 70B is an open‑source 70 B Llama 3.1‑based model that uses real‑time reflection tuning for self‑correction. It outperforms GPT‑4o on MMLU, HumanEval, MATH, IFEval, GSM8K, supporting accurate coding, debugging, and reasoning tasks via API, with a no‑registration web interface.

Appen delivers human‑validated datasets across six domains—alignment, agentic AI, speech/audio, multimodal, physical, and model integrity—using automation and a global workforce of 1 million+ contributors. SOC 2/ISO 27001 certified, it supports large‑scale AI training and independent evaluation.

Finetuned Diffusion is a pre-trained model for text generation tasks on Hugging Face's Space platform. It can be used in Colab without waiting in a queue.

LangDrive is a versatile AI tool offering over 100 language models tuning through a single API. It supports seamless connectivity to various data sources, decentralized engine, and free access for model completion tasks via post requests.

LM Studio is a local platform for running various large language models like Llama 2 and Mistral. It offers an offline environment, user-friendly interface, and supports multiple operating systems, enhancing privacy and allowing for simultaneous model execution.

LLM Pulse tracks brand visibility and search presence across LLMs (ChatGPT, Perplexity, Google AI), offering prompt tracking and suggestions, citation analysis, visibility scoring and competitor benchmarking, sentiment and response inspection, plus API and reporting exports.

RunLLM is an AI platform that automates incident investigations by querying observability tools, correlating telemetry, and delivering root-cause analyses. It generates live runbooks and remediation recommendations to accelerate MTTR and create an auditable history of incidents.

Lume automates end‑to‑end integration for software teams, discovering schemas and proposing mappings across ERPs, databases, APIs, and flat files. It generates production‑ready dbt models, SQL, and quality rules deployable to Snowflake or BigQuery, shortening cycles and improving data quality.

LangWatch enables real‑time testing of LLM agents, offering simulation, prompt management, audit trails, and batch testing across models. It integrates with OpenTelemetry, LangChain, LangGraph, and supports self‑hosted, cloud, and role‑based access.

LLMWare AI installs a lightweight client on PCs, providing instant access to 100+ AI models optimized for Intel and Qualcomm hardware. It supports RAG, auto‑tunes weights, runs locally without Wi‑Fi, and offers an admin console for monitoring, scaling, and audit logs.

Klu accelerates LLM app development by enabling collaborative prompt design, version control, and automated evaluation across multiple providers. It offers unified observability, cost and drift tracking, private infrastructure, continuous monitoring, and integration with 50+ tools for scalable AI de

LightLayer provides scalable, richly annotated egocentric datasets—synchronized RGB, audio, IMU, and depth—via distributed capture coordination, automated collection workflows, and streamlined annotation pipelines to produce delivery-ready data for embodied AI and robotic perception training.

VLLM is a high-throughput, memory-efficient inference engine for Large Language Models, enabling faster responses and effective memory management. It supports multi-node configurations for scalability and offers robust documentation for seamless integration into workflows.

Inception Labs' diffusion-based large language models (dLLMs) offer faster, more efficient, and cost-effective text generation than traditional autoregressive models. With built-in error correction, multimodal support, and structured output control, they excel in function calling and complex data ge

Parea AI tracks LLM calls via Python/TypeScript SDKs, letting teams evaluate models on custom data, spot regressions, iterate prompts in a playground, monitor cost, latency and quality, and collect human annotations for fine‑tuning.

Awan LLM offers unlimited token generation with Meta Llama 3.1 8B and 70B models, no censorship or caps, supporting persistent AI assistance, autonomous agents, roleplay, data processing, and code completion, hosted on owned GPUs for continuous use.

LLMChess lets users play White against a GPT‑driven engine (GPT‑3.5‑Turbo or GPT‑4) with start/stop controls and a log pane. It offers un‑rated, platform‑rated, and FOA‑rated games, tournaments, and training tools like puzzles and post‑game analysis.

LLM Selector filters open‑source large language models by use case—chatbots, content, code, summarization, research—while presenting benchmarks, training data, architecture, and deployment details. The interface updates regularly to aid researchers, developers, and product managers in data‑driven mo

exllama is a memory-efficient tool for executing Hugging Face transformers with the LLaMA models using quantized weights, enabling high-performance NLP tasks on modern GPUs while minimizing memory usage and supporting various hardware configurations.

EvalsOne is an evaluation platform for developers and researchers to assess LLM prompts, RAG, and agents using rule‑based or LLM‑based methods, human judgment, and customizable evaluators. It supports multiple APIs and integrates with major AI frameworks.

llmdebate.ai is a live debate arena where multiple AI models face off on user-submitted questions, with community-verified analysis and adjudicated verdicts. It generates evidence-backed summaries and final verdicts to surface consensus, disputed claims, and reasoning paths across topics like econom

Mistral.rs is an efficient, versatile tool for high-speed large language model (LLM) inference, offering multi-device support and extensive quantization options for seamless deployment on diverse hardware setups.

MLflow is an open‑source AI engineering platform that tracks LLM and agent execution, monitors performance, cost, and safety, manages prompts, and supports experiment tracking, tuning, and deployment across multiple clouds or on‑premises.

Pocketllm is an AI-powered personal document search engine that allows you to easily search and retrieve information from thousands of pages of PDFs and documents. It offers semantic search capability, fine-tuning search results and summarizing results.

Non finito is a web‑based platform that lets researchers evaluate and compare multimodal AI models across tasks like entity tracking, reasoning, QA, visual deduction, and card counting. Users input custom prompts, view outputs side‑by‑side, and collaborate in public or private spaces.

Tuned Together uses language style matching to create user profiles via chatbot conversations, then recommends partners with similar linguistic traits. It supplies data‑driven compatibility scores and predicts long‑term relationship stability.

Pioneer automates retraining and deployment of open-source models, using live inference data for fine-tuning and one-shot adaptation. It manages adaptive inference, routing, RAG pipelines, agent workflows, synthetic data generation, monitoring, and automated checkpoint promotion.