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Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.

modalityAllTextImageAudioTabularVideo
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01YuvrajSingh9886 /jetson-non-reasoning-benchmark-ollama-15w Tiny LLM Benchmark — Jetson Orin Nano Super 8GB Date: 2026-06-07 02:35Backends: ollamaSweep: prompt ∈ {128,512,1024,2048} tok × gen ∈ {64,128,256} tokArtifacts: /home/yuvrajsingh/Desktop/benchmark/smolbenchmark/non-reasoning-models/artifacts/blog-all-20260606-0139-15w Full Results — ollama Power = VDD_CPU_GPU_CV avg over aiperf window. Model Quant ISL OSL OSL mis% TTFT avg p50 p90 p99 T2T avg p50 p90 p99 ITL avg p50 p90 p99 Tok/s Req/s E2E avg p50 p90 p99… See the full description on the dataset page: https://huggingface.co/datasets/YuvrajSingh9886/jetson-non-reasoning-benchmark-ollama-15w.tabularn<1K0 likes591 downloads8d agoHugging Face02YuvrajSingh9886 /jetson-non-reasoning-benchmark-ollama-25w Tiny LLM Benchmark — Jetson Orin Nano Super 8GB Date: 2026-06-23 06:04Backends: ollamaSweep: prompt ∈ {128,512,1024,2048} tok × gen ∈ {64,128,256} tokArtifacts: /home/yuvrajsingh/Desktop/benchmark/smolbenchmark/benchmark-jetson-nano-orin-super/non-reasoning-models/artifacts/blog-all-20260622-0159-25w Full Results — ollama Power = VDD_CPU_GPU_CV avg over aiperf window. Model Quant ISL OSL OSL mis% TTFT avg p50 p90 p99 T2T avg p50 p90 p99 ITL avg p50 p90 p99… See the full description on the dataset page: https://huggingface.co/datasets/YuvrajSingh9886/jetson-non-reasoning-benchmark-ollama-25w.tabularn<1K0 likes569 downloads8d agoHugging Face03YuvrajSingh9886 /jetson-non-reasoning-benchmark-ollama-7w Tiny LLM Benchmark — Jetson Orin Nano Super 8GB Date: 2026-06-09 02:38Backends: ollamaSweep: prompt ∈ {128,512,1024,2048} tok × gen ∈ {64,128,256} tokArtifacts: /home/yuvrajsingh/Desktop/benchmark/smolbenchmark/non-reasoning-models/artifacts/blog-all-20260607-0403-7w Full Results — ollama Power = VDD_CPU_GPU_CV avg over aiperf window. Model Quant ISL OSL OSL mis% TTFT avg p50 p90 p99 T2T avg p50 p90 p99 ITL avg p50 p90 p99 Tok/s Req/s E2E avg p50 p90 p99… See the full description on the dataset page: https://huggingface.co/datasets/YuvrajSingh9886/jetson-non-reasoning-benchmark-ollama-7w.tabularn<1K0 likes488 downloads8d agoHugging Face04YuvrajSingh9886 /jetson-non-reasoning-benchmark-ollama-maxn Tiny LLM Benchmark — Jetson Orin Nano Super 8GB Date: 2026-06-22 01:58Backends: ollamaSweep: prompt ∈ {128,512,1024,2048} tok × gen ∈ {64,128,256} tokArtifacts: /home/yuvrajsingh/Desktop/benchmark/smolbenchmark/benchmark-jetson-nano-orin-super/non-reasoning-models/artifacts/blog-all-20260621-1401-maxn Full Results — ollama Power = VDD_CPU_GPU_CV avg over aiperf window. Model Quant ISL OSL OSL mis% TTFT avg p50 p90 p99 T2T avg p50 p90 p99 ITL avg p50 p90… See the full description on the dataset page: https://huggingface.co/datasets/YuvrajSingh9886/jetson-non-reasoning-benchmark-ollama-maxn.tabularn<1K0 likes416 downloads8d agoHugging Face05smcleod /golang-ollamaapi-charmAttempting to create a dataset with AugmentToolkit. I'm new to datasets and textgen training and this is my first attempt at creating a dataset. I'm not sure if this will end up being useful or not so YMMV. Created from: Uber's Golang Style Guide Ollama's Golang API Docs Charmbracelet's Golang Packages and Examples I generated the Q/A with a mix of Mixtral Nous Hermes 8x7b, Llama 3 8b, Qwen 2 7b. The file that's the most processed (but probably still needs work) is . https://smcleod.net text-generation1K<n<10K2 likes79 downloads2y agoHugging Face06JianChunZhou /ollama-bin0 likes68 downloads26d agoHugging Face0725b3nk /ollama-github-issuestabular1K<n<10K0 likes53 downloads2y agoHugging Face08fparrav /splash-omlx-ollama-benchmark Qwen3.8 27B: oMLX vs Splash vs Ollama Reproducible local benchmark on a MacBook Pro M4 Max with 64 GB unified memory. Result in one sentence For this workload, Ollama is the best overall backend: it wins most TTFT/decode comparisons and the concurrency tests. Splash is interesting specifically for cold long-context prefill, where it is faster than oMLX and slightly faster than Ollama at 32K tokens. Scope Prompt lengths: 1K, 4K, 8K, 16K, 32K tokens… See the full description on the dataset page: https://huggingface.co/datasets/fparrav/splash-omlx-ollama-benchmark.0 likes41 downloads4d agoHugging Face09latterworks /ollama-hosts-index 🛰️ Ollama Hosts Index (Latterworks) A registry of reachable Ollama model endpoints scraped from the wild. This dataset contains 936 IP:PORT entries—mostly on port 11434—with raw /api/tags payloads included for inspection or parsing. ✨ Fields ip: Host address, typically of the form IP_PORT (e.g., 45.41.94.28_11434) model: Extracted model name, e.g., llama3:8b-instruct-q5_K_M (currently null, see note) tags: Optional user-provided tags (mostly empty) raw: Full… See the full description on the dataset page: https://huggingface.co/datasets/latterworks/ollama-hosts-index.text1K<n<10K1 likes34 downloads4mo agoHugging Face10ollama456 /items_raw_fulltext1M<n<10M0 likes31 downloads7mo agoHugging Face11playn4 /DeepseekV4Flash-Ollama-and-Claude-Code-CLI1 likes22 downloads2mo agoHugging Face12ollamaweights /synthetic-dataset-1208 Synthetic Key-Value Retrieval 32K This is a deterministic synthetic benchmark for exact key-value retrieval from a long context. It is designed for evaluating long-context inference and KV cache compression methods. Context format The context contains a one-time task description followed by an array: You are given an array of key-value entries. Every key begins with K_ and every value begins with V_. Each entry has the format [key: value]. Given a query key, find… See the full description on the dataset page: https://huggingface.co/datasets/ollamaweights/synthetic-dataset-1208.tabularquestion-answeringn<1K0 likes22 downloads1mo agoHugging Face13johnmccabe /ollama_test_2 SQL Question Dataset [toy] This dataset was generated using Distilabel and contains natural language questions paired with SQL queries. The model used is llama3.2:3b-instruct-fp16. The generation environment was a test to use Ollama in combination with a VSCode devcontainer environment using uv to better control dependencies/reproducibility. Description Inputs: Natural language questions Outputs: Corresponding SQL queries Generated via: johnmccabe/ollama_sql_review_test… See the full description on the dataset page: https://huggingface.co/datasets/johnmccabe/ollama_test_2.textn<1K0 likes21 downloads1y agoHugging Face14johnmccabe /ollama_sql_review_test-with-evol Dataset Card for ollama_sql_review_test-with-evol This dataset has been created with distilabel. The pipeline script was uploaded to easily reproduce the dataset: ipykernel_launcher.py. It can be run directly using the CLI: distilabel pipeline run --script "https://huggingface.co/datasets/johnmccabe/ollama_sql_review_test-with-evol/raw/main/ipykernel_launcher.py" Dataset Summary This dataset contains a pipeline.yaml which can be used to reproduce the… See the full description on the dataset page: https://huggingface.co/datasets/johnmccabe/ollama_sql_review_test-with-evol.textn<1K0 likes21 downloads1y agoHugging Face15technovangelist /OllamaDocsThis is a dataset generated from the documentation of Ollama as of 01/02/2025. The docs were fed into a model and then for every 10 words, another question was generated (roughly). Was created with https://github.com/technovangelist/llm_dataset_builder text1K<n<10K1 likes20 downloads2y agoHugging Face16davidmeikle /distilabel-ollama-test Dataset Card for distilabel-ollama-test This dataset has been created with distilabel. Dataset Summary This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI: distilabel pipeline run --config "https://huggingface.co/datasets/davidmeikle/distilabel-ollama-test/raw/main/pipeline.yaml" or explore the configuration: distilabel pipeline info --config… See the full description on the dataset page: https://huggingface.co/datasets/davidmeikle/distilabel-ollama-test.textn<1K0 likes19 downloads1y agoHugging Face17ThomasTheMaker /BlenderCAD2-Ollama-Starcoder2-7bimagen<1K0 likes19 downloads1y agoHugging Face18AYI-NEDJIMI /article-deployer-llm-cybersecurite-ollama-proxmox Deploying a Cybersecurity LLM On-Premise with Ollama on Proxmox Deployer un LLM Cybersecurite On-Premise avec Ollama sur Proxmox This dataset contains a technical article available in both French and English. Cet article technique est disponible en francais et en anglais. Navigation Version Francaise English Version title: "Deployer un LLM Cybersecurite On-Premise avec Ollama sur Proxmox" author: "AYI-NEDJIMI Consultants" date: "2026-02-21"… See the full description on the dataset page: https://huggingface.co/datasets/AYI-NEDJIMI/article-deployer-llm-cybersecurite-ollama-proxmox.0 likes19 downloads7mo agoHugging Face19ollamaweights /synthetic-dataset-1208-64k Synthetic Key-Value Retrieval 64K This is a deterministic synthetic benchmark for exact key-value retrieval from a long context. It is designed for evaluating long-context inference and KV cache compression methods. Context format The context contains a one-time task description followed by an array: You are given an array of key-value entries. Every key begins with K_ and every value begins with V_. Each entry has the format [key: value]. Given a query key, find… See the full description on the dataset page: https://huggingface.co/datasets/ollamaweights/synthetic-dataset-1208-64k.tabularquestion-answeringn<1K0 likes19 downloads1mo agoHugging Face20johnmccabe /ollama_test Dataset Card for ollama_test This dataset has been created with distilabel. Dataset Summary This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI: distilabel pipeline run --config "https://huggingface.co/datasets/johnmccabe/ollama_test/raw/main/pipeline.yaml" or explore the configuration: distilabel pipeline info --config… See the full description on the dataset page: https://huggingface.co/datasets/johnmccabe/ollama_test.textn<1K0 likes18 downloads1y agoHugging Face21latterworks /parsed_ollama_data_20250320tabularn<1K0 likes17 downloads2y agoHugging Face22ollamaweights /Ruler-64ktext1K<n<10K0 likes17 downloads2mo agoHugging Face23johnmccabe /ollama_sql_review_test SQL Question Dataset [toy] This dataset was generated using Distilabel and contains natural language questions paired with SQL queries. The model used is llama3.2:3b-instruct-fp16. The generation environment was a test to use Ollama in combination with a VSCode devcontainer environment using uv to better control dependencies/reproducibility. Description Inputs: Natural language questions Outputs: Corresponding SQL queries Generated via: johnmccabe/ollama_sql_review_test… See the full description on the dataset page: https://huggingface.co/datasets/johnmccabe/ollama_sql_review_test.textn<1K0 likes16 downloads1y agoHugging Face24Bisher /gemma-3-12b-ollama_SadeedDiac-25text1K<n<10K0 likes16 downloads1y agoHugging Face25Aradm1996 /items_lite_using_ollamatextn<1K0 likes16 downloads6mo agoHugging Face26ollama456 /librispeech-whisper-edgeaudio1K<n<10K0 likes14 downloads7mo agoHugging Face27Bisher /gemma-3-27b-ollama_SadeedDiac-25text1K<n<10K0 likes13 downloads1y agoHugging Face28CIRCL /vulnerability-attack-techniques-llm-ollama-qwen3.5-122b LLM-labeled expansion — provenance ⚠️ These labels are machine-generated by ollama/qwen3.5:122b, not analyst-curated. They follow the MITRE CTID "Mapping ATT&CK to CVE for Impact" methodology as an expansion of the curated gold dataset CIRCL/vulnerability-attack-techniques. Labeling model: ollama/qwen3.5:122b CVEs: 297 ATT&CK version: 19.1 label_sources: ["llm"] on every row; the llm_model column records the exact model per row Validation agreement vs gold set: f1_micro 0.392… See the full description on the dataset page: https://huggingface.co/datasets/CIRCL/vulnerability-attack-techniques-llm-ollama-qwen3.5-122b.textn<1K0 likes12 downloads2mo agoHugging Face29ollamaweights /synthetic-kv-qwen3-8b-with-metadata Synthetic KV Qwen3 8B — metadata-enhanced 64K This dataset is an exact key-value retrieval benchmark. The context begins with a short schema and task description, followed by records in the form [KEY: VALUE]. Each question asks for the value belonging to one exact key. The context is intentionally stored once in compact JSONL format. The questions[i] entry corresponds to answers[i]. tabularn<1K0 likes12 downloads2mo agoHugging Face30latterworks /parsed_ollama_data0 likes11 downloads2y agoHugging Face

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