id stringlengths 14 14 | category stringclasses 6
values | topic stringclasses 20
values | messages listlengths 2 2 | synthetic bool 1
class | source stringclasses 1
value | quality_note stringclasses 1
value |
|---|---|---|---|---|---|---|
compactlm-0028 | glossary | dataset curation | [
{
"role": "user",
"content": "Write a short glossary entry for dataset curation, including one practical consideration."
},
{
"role": "assistant",
"content": "Dataset curation: selecting, cleaning, and documenting examples used for training or evaluation. In practice, evaluate its benefits and t... | true | template-generated | unreviewed demonstration sample |
compactlm-0193 | rewriting | retrieval augmentation | [
{
"role": "user",
"content": "For a student project: Rewrite for clarity: \"We are doing retrieval augmentation stuff and checking if it is good, maybe we try bigger later.\""
},
{
"role": "assistant",
"content": "We are evaluating retrieval augmentation and will consider expanding the experimen... | true | template-generated | unreviewed demonstration sample |
compactlm-0292 | summarization | memory usage | [
{
"role": "user",
"content": "For a small research prototype: Summarize this note in a sentence: \"Our team is exploring memory usage. We plan to test it on a small dataset first, measure the result, and document limitations before expanding.\""
},
{
"role": "assistant",
"content": "The team wil... | true | template-generated | unreviewed demonstration sample |
compactlm-0242 | definition | on-device AI | [
{
"role": "user",
"content": "For a small research prototype: Explain on-device AI in one sentence for a beginner."
},
{
"role": "assistant",
"content": "On-device ai means executing AI models locally on a phone, laptop, or other device."
}
] | true | template-generated | unreviewed demonstration sample |
compactlm-0254 | definition | fine-tuning | [
{
"role": "user",
"content": "For a small research prototype: Explain fine-tuning in one sentence for a beginner."
},
{
"role": "assistant",
"content": "Fine-tuning means continuing model training on a smaller task- or domain-specific dataset."
}
] | true | template-generated | unreviewed demonstration sample |
compactlm-0571 | extraction | latency optimization | [
{
"role": "user",
"content": "For technical documentation: Extract the focus area and next step as JSON from: \"We are testing latency optimization and will record latency and accuracy.\""
},
{
"role": "assistant",
"content": "{\"focus_area\": \"latency optimization\", \"next_step\": \"record la... | true | template-generated | unreviewed demonstration sample |
compactlm-0447 | extraction | model distillation | [
{
"role": "user",
"content": "Answer concisely: Extract the focus area and next step as JSON from: \"We are testing model distillation and will record latency and accuracy.\""
},
{
"role": "assistant",
"content": "{\"focus_area\": \"model distillation\", \"next_step\": \"record latency and accur... | true | template-generated | unreviewed demonstration sample |
compactlm-0555 | rewriting | training validation | [
{
"role": "user",
"content": "For technical documentation: Rewrite for clarity: \"We are doing training validation stuff and checking if it is good, maybe we try bigger later.\""
},
{
"role": "assistant",
"content": "We are evaluating training validation and will consider expanding the experimen... | true | template-generated | unreviewed demonstration sample |
compactlm-0243 | definition | tokenization | [
{
"role": "user",
"content": "For a small research prototype: Explain tokenization in one sentence for a beginner."
},
{
"role": "assistant",
"content": "Tokenization means turning text into smaller units represented by numeric token IDs."
}
] | true | template-generated | unreviewed demonstration sample |
compactlm-0094 | extraction | fine-tuning | [
{
"role": "user",
"content": "Extract the focus area and next step as JSON from: \"We are testing fine-tuning and will record latency and accuracy.\""
},
{
"role": "assistant",
"content": "{\"focus_area\": \"fine-tuning\", \"next_step\": \"record latency and accuracy\"}"
}
] | true | template-generated | unreviewed demonstration sample |
compactlm-0525 | summarization | quantization | [
{
"role": "user",
"content": "For technical documentation: Summarize this note in a sentence: \"Our team is exploring quantization. We plan to test it on a small dataset first, measure the result, and document limitations before expanding.\""
},
{
"role": "assistant",
"content": "The team will p... | true | template-generated | unreviewed demonstration sample |
compactlm-0120 | evaluation | synthetic datasets | [
{
"role": "user",
"content": "Give two responsible evaluation checks for a demonstration involving synthetic datasets."
},
{
"role": "assistant",
"content": "1. Record the test setup and baseline for synthetic datasets.\n2. Inspect failures and disclose limitations before drawing conclusions."
... | true | template-generated | unreviewed demonstration sample |
compactlm-0503 | glossary | tokenization | [
{
"role": "user",
"content": "For technical documentation: Write a short glossary entry for tokenization, including one practical consideration."
},
{
"role": "assistant",
"content": "Tokenization: turning text into smaller units represented by numeric token IDs. In practice, evaluate its benefi... | true | template-generated | unreviewed demonstration sample |
compactlm-0578 | extraction | hallucination detection | [
{
"role": "user",
"content": "For technical documentation: Extract the focus area and next step as JSON from: \"We are testing hallucination detection and will record latency and accuracy.\""
},
{
"role": "assistant",
"content": "{\"focus_area\": \"hallucination detection\", \"next_step\": \"rec... | true | template-generated | unreviewed demonstration sample |
compactlm-0493 | definition | retrieval augmentation | [
{
"role": "user",
"content": "For technical documentation: Explain retrieval augmentation in one sentence for a beginner."
},
{
"role": "assistant",
"content": "Retrieval augmentation means supplementing generation with information retrieved from an external collection."
}
] | true | template-generated | unreviewed demonstration sample |
compactlm-0257 | definition | model deployment | [
{
"role": "user",
"content": "For a small research prototype: Explain model deployment in one sentence for a beginner."
},
{
"role": "assistant",
"content": "Model deployment means making a model available for use in an application or service."
}
] | true | template-generated | unreviewed demonstration sample |
compactlm-0050 | summarization | code generation | [
{
"role": "user",
"content": "Summarize this note in a sentence: \"Our team is exploring code generation. We plan to test it on a small dataset first, measure the result, and document limitations before expanding.\""
},
{
"role": "assistant",
"content": "The team will pilot code generation, meas... | true | template-generated | unreviewed demonstration sample |
compactlm-0580 | extraction | synthetic datasets | [
{
"role": "user",
"content": "For technical documentation: Extract the focus area and next step as JSON from: \"We are testing synthetic datasets and will record latency and accuracy.\""
},
{
"role": "assistant",
"content": "{\"focus_area\": \"synthetic datasets\", \"next_step\": \"record latenc... | true | template-generated | unreviewed demonstration sample |
compactlm-0355 | evaluation | training validation | [
{
"role": "user",
"content": "For a small research prototype: Give two responsible evaluation checks for a demonstration involving training validation."
},
{
"role": "assistant",
"content": "1. Record the test setup and baseline for training validation.\n2. Inspect failures and disclose limitati... | true | template-generated | unreviewed demonstration sample |
compactlm-0252 | definition | memory usage | [
{
"role": "user",
"content": "For a small research prototype: Explain memory usage in one sentence for a beginner."
},
{
"role": "assistant",
"content": "Memory usage means the amount of RAM or accelerator memory consumed during execution."
}
] | true | template-generated | unreviewed demonstration sample |
compactlm-0409 | summarization | prompt evaluation | [
{
"role": "user",
"content": "Answer concisely: Summarize this note in a sentence: \"Our team is exploring prompt evaluation. We plan to test it on a small dataset first, measure the result, and document limitations before expanding.\""
},
{
"role": "assistant",
"content": "The team will pilot p... | true | template-generated | unreviewed demonstration sample |
compactlm-0207 | extraction | model distillation | [
{
"role": "user",
"content": "For a student project: Extract the focus area and next step as JSON from: \"We are testing model distillation and will record latency and accuracy.\""
},
{
"role": "assistant",
"content": "{\"focus_area\": \"model distillation\", \"next_step\": \"record latency and ... | true | template-generated | unreviewed demonstration sample |
compactlm-0156 | glossary | context windows | [
{
"role": "user",
"content": "For a student project: Write a short glossary entry for context windows, including one practical consideration."
},
{
"role": "assistant",
"content": "Context windows: the limit on how many input and generated tokens a model can consider in one request. In practice,... | true | template-generated | unreviewed demonstration sample |
compactlm-0214 | extraction | fine-tuning | [
{
"role": "user",
"content": "For a student project: Extract the focus area and next step as JSON from: \"We are testing fine-tuning and will record latency and accuracy.\""
},
{
"role": "assistant",
"content": "{\"focus_area\": \"fine-tuning\", \"next_step\": \"record latency and accuracy\"}"
... | true | template-generated | unreviewed demonstration sample |
compactlm-0301 | rewriting | GPU inference | [
{
"role": "user",
"content": "For a small research prototype: Rewrite for clarity: \"We are doing GPU inference stuff and checking if it is good, maybe we try bigger later.\""
},
{
"role": "assistant",
"content": "We are evaluating GPU inference and will consider expanding the experiment after r... | true | template-generated | unreviewed demonstration sample |
compactlm-0486 | definition | batching | [
{
"role": "user",
"content": "For technical documentation: Explain batching in one sentence for a beginner."
},
{
"role": "assistant",
"content": "Batching means processing multiple requests together to improve hardware utilization."
}
] | true | template-generated | unreviewed demonstration sample |
compactlm-0144 | glossary | embedding search | [
{
"role": "user",
"content": "For a student project: Write a short glossary entry for embedding search, including one practical consideration."
},
{
"role": "assistant",
"content": "Embedding search: retrieving items using similarity between numerical vector representations. In practice, evaluat... | true | template-generated | unreviewed demonstration sample |
compactlm-0362 | definition | on-device AI | [
{
"role": "user",
"content": "Answer concisely: Explain on-device AI in one sentence for a beginner."
},
{
"role": "assistant",
"content": "On-device ai means executing AI models locally on a phone, laptop, or other device."
}
] | true | template-generated | unreviewed demonstration sample |
compactlm-0482 | definition | on-device AI | [
{
"role": "user",
"content": "For technical documentation: Explain on-device AI in one sentence for a beginner."
},
{
"role": "assistant",
"content": "On-device ai means executing AI models locally on a phone, laptop, or other device."
}
] | true | template-generated | unreviewed demonstration sample |
compactlm-0425 | rewriting | quantization | [
{
"role": "user",
"content": "Answer concisely: Rewrite for clarity: \"We are doing quantization stuff and checking if it is good, maybe we try bigger later.\""
},
{
"role": "assistant",
"content": "We are evaluating quantization and will consider expanding the experiment after reviewing the res... | true | template-generated | unreviewed demonstration sample |
compactlm-0119 | evaluation | classifier calibration | [
{
"role": "user",
"content": "Give two responsible evaluation checks for a demonstration involving classifier calibration."
},
{
"role": "assistant",
"content": "1. Record the test setup and baseline for classifier calibration.\n2. Inspect failures and disclose limitations before drawing conclus... | true | template-generated | unreviewed demonstration sample |
compactlm-0314 | rewriting | fine-tuning | [
{
"role": "user",
"content": "For a small research prototype: Rewrite for clarity: \"We are doing fine-tuning stuff and checking if it is good, maybe we try bigger later.\""
},
{
"role": "assistant",
"content": "We are evaluating fine-tuning and will consider expanding the experiment after revie... | true | template-generated | unreviewed demonstration sample |
compactlm-0508 | glossary | dataset curation | [
{
"role": "user",
"content": "For technical documentation: Write a short glossary entry for dataset curation, including one practical consideration."
},
{
"role": "assistant",
"content": "Dataset curation: selecting, cleaning, and documenting examples used for training or evaluation. In practice... | true | template-generated | unreviewed demonstration sample |
compactlm-0133 | definition | retrieval augmentation | [
{
"role": "user",
"content": "For a student project: Explain retrieval augmentation in one sentence for a beginner."
},
{
"role": "assistant",
"content": "Retrieval augmentation means supplementing generation with information retrieved from an external collection."
}
] | true | template-generated | unreviewed demonstration sample |
compactlm-0078 | rewriting | hallucination detection | [
{
"role": "user",
"content": "Rewrite for clarity: \"We are doing hallucination detection stuff and checking if it is good, maybe we try bigger later.\""
},
{
"role": "assistant",
"content": "We are evaluating hallucination detection and will consider expanding the experiment after reviewing the... | true | template-generated | unreviewed demonstration sample |
compactlm-0146 | glossary | batching | [
{
"role": "user",
"content": "For a student project: Write a short glossary entry for batching, including one practical consideration."
},
{
"role": "assistant",
"content": "Batching: processing multiple requests together to improve hardware utilization. In practice, evaluate its benefits and tr... | true | template-generated | unreviewed demonstration sample |
compactlm-0158 | glossary | hallucination detection | [
{
"role": "user",
"content": "For a student project: Write a short glossary entry for hallucination detection, including one practical consideration."
},
{
"role": "assistant",
"content": "Hallucination detection: identifying generated claims that lack support or are incorrect. In practice, eval... | true | template-generated | unreviewed demonstration sample |
compactlm-0205 | extraction | quantization | [
{
"role": "user",
"content": "For a student project: Extract the focus area and next step as JSON from: \"We are testing quantization and will record latency and accuracy.\""
},
{
"role": "assistant",
"content": "{\"focus_area\": \"quantization\", \"next_step\": \"record latency and accuracy\"}"... | true | template-generated | unreviewed demonstration sample |
compactlm-0350 | evaluation | code generation | [
{
"role": "user",
"content": "For a small research prototype: Give two responsible evaluation checks for a demonstration involving code generation."
},
{
"role": "assistant",
"content": "1. Record the test setup and baseline for code generation.\n2. Inspect failures and disclose limitations befo... | true | template-generated | unreviewed demonstration sample |
compactlm-0497 | definition | model deployment | [
{
"role": "user",
"content": "For technical documentation: Explain model deployment in one sentence for a beginner."
},
{
"role": "assistant",
"content": "Model deployment means making a model available for use in an application or service."
}
] | true | template-generated | unreviewed demonstration sample |
compactlm-0535 | summarization | training validation | [
{
"role": "user",
"content": "For technical documentation: Summarize this note in a sentence: \"Our team is exploring training validation. We plan to test it on a small dataset first, measure the result, and document limitations before expanding.\""
},
{
"role": "assistant",
"content": "The team... | true | template-generated | unreviewed demonstration sample |
compactlm-0356 | evaluation | context windows | [
{
"role": "user",
"content": "For a small research prototype: Give two responsible evaluation checks for a demonstration involving context windows."
},
{
"role": "assistant",
"content": "1. Record the test setup and baseline for context windows.\n2. Inspect failures and disclose limitations befo... | true | template-generated | unreviewed demonstration sample |
compactlm-0305 | rewriting | quantization | [
{
"role": "user",
"content": "For a small research prototype: Rewrite for clarity: \"We are doing quantization stuff and checking if it is good, maybe we try bigger later.\""
},
{
"role": "assistant",
"content": "We are evaluating quantization and will consider expanding the experiment after rev... | true | template-generated | unreviewed demonstration sample |
compactlm-0492 | definition | memory usage | [
{
"role": "user",
"content": "For technical documentation: Explain memory usage in one sentence for a beginner."
},
{
"role": "assistant",
"content": "Memory usage means the amount of RAM or accelerator memory consumed during execution."
}
] | true | template-generated | unreviewed demonstration sample |
compactlm-0262 | glossary | on-device AI | [
{
"role": "user",
"content": "For a small research prototype: Write a short glossary entry for on-device AI, including one practical consideration."
},
{
"role": "assistant",
"content": "On-device ai: executing AI models locally on a phone, laptop, or other device. In practice, evaluate its bene... | true | template-generated | unreviewed demonstration sample |
compactlm-0022 | glossary | on-device AI | [
{
"role": "user",
"content": "Write a short glossary entry for on-device AI, including one practical consideration."
},
{
"role": "assistant",
"content": "On-device ai: executing AI models locally on a phone, laptop, or other device. In practice, evaluate its benefits and trade-offs using measur... | true | template-generated | unreviewed demonstration sample |
compactlm-0037 | glossary | model deployment | [
{
"role": "user",
"content": "Write a short glossary entry for model deployment, including one practical consideration."
},
{
"role": "assistant",
"content": "Model deployment: making a model available for use in an application or service. In practice, evaluate its benefits and trade-offs using ... | true | template-generated | unreviewed demonstration sample |
compactlm-0402 | summarization | on-device AI | [
{
"role": "user",
"content": "Answer concisely: Summarize this note in a sentence: \"Our team is exploring on-device AI. We plan to test it on a small dataset first, measure the result, and document limitations before expanding.\""
},
{
"role": "assistant",
"content": "The team will pilot on-dev... | true | template-generated | unreviewed demonstration sample |
compactlm-0373 | definition | retrieval augmentation | [
{
"role": "user",
"content": "Answer concisely: Explain retrieval augmentation in one sentence for a beginner."
},
{
"role": "assistant",
"content": "Retrieval augmentation means supplementing generation with information retrieved from an external collection."
}
] | true | template-generated | unreviewed demonstration sample |
compactlm-0211 | extraction | latency optimization | [
{
"role": "user",
"content": "For a student project: Extract the focus area and next step as JSON from: \"We are testing latency optimization and will record latency and accuracy.\""
},
{
"role": "assistant",
"content": "{\"focus_area\": \"latency optimization\", \"next_step\": \"record latency ... | true | template-generated | unreviewed demonstration sample |
compactlm-0091 | extraction | latency optimization | [
{
"role": "user",
"content": "Extract the focus area and next step as JSON from: \"We are testing latency optimization and will record latency and accuracy.\""
},
{
"role": "assistant",
"content": "{\"focus_area\": \"latency optimization\", \"next_step\": \"record latency and accuracy\"}"
}
] | true | template-generated | unreviewed demonstration sample |
compactlm-0333 | extraction | retrieval augmentation | [
{
"role": "user",
"content": "For a small research prototype: Extract the focus area and next step as JSON from: \"We are testing retrieval augmentation and will record latency and accuracy.\""
},
{
"role": "assistant",
"content": "{\"focus_area\": \"retrieval augmentation\", \"next_step\": \"re... | true | template-generated | unreviewed demonstration sample |
compactlm-0266 | glossary | batching | [
{
"role": "user",
"content": "For a small research prototype: Write a short glossary entry for batching, including one practical consideration."
},
{
"role": "assistant",
"content": "Batching: processing multiple requests together to improve hardware utilization. In practice, evaluate its benefi... | true | template-generated | unreviewed demonstration sample |
compactlm-0263 | glossary | tokenization | [
{
"role": "user",
"content": "For a small research prototype: Write a short glossary entry for tokenization, including one practical consideration."
},
{
"role": "assistant",
"content": "Tokenization: turning text into smaller units represented by numeric token IDs. In practice, evaluate its ben... | true | template-generated | unreviewed demonstration sample |
compactlm-0457 | extraction | model deployment | [
{
"role": "user",
"content": "Answer concisely: Extract the focus area and next step as JSON from: \"We are testing model deployment and will record latency and accuracy.\""
},
{
"role": "assistant",
"content": "{\"focus_area\": \"model deployment\", \"next_step\": \"record latency and accuracy\... | true | template-generated | unreviewed demonstration sample |
compactlm-0584 | evaluation | embedding search | [
{
"role": "user",
"content": "For technical documentation: Give two responsible evaluation checks for a demonstration involving embedding search."
},
{
"role": "assistant",
"content": "1. Record the test setup and baseline for embedding search.\n2. Inspect failures and disclose limitations befor... | true | template-generated | unreviewed demonstration sample |
compactlm-0079 | rewriting | classifier calibration | [
{
"role": "user",
"content": "Rewrite for clarity: \"We are doing classifier calibration stuff and checking if it is good, maybe we try bigger later.\""
},
{
"role": "assistant",
"content": "We are evaluating classifier calibration and will consider expanding the experiment after reviewing the r... | true | template-generated | unreviewed demonstration sample |
compactlm-0363 | definition | tokenization | [
{
"role": "user",
"content": "Answer concisely: Explain tokenization in one sentence for a beginner."
},
{
"role": "assistant",
"content": "Tokenization means turning text into smaller units represented by numeric token IDs."
}
] | true | template-generated | unreviewed demonstration sample |
compactlm-0302 | rewriting | on-device AI | [
{
"role": "user",
"content": "For a small research prototype: Rewrite for clarity: \"We are doing on-device AI stuff and checking if it is good, maybe we try bigger later.\""
},
{
"role": "assistant",
"content": "We are evaluating on-device AI and will consider expanding the experiment after rev... | true | template-generated | unreviewed demonstration sample |
compactlm-0092 | extraction | memory usage | [
{
"role": "user",
"content": "Extract the focus area and next step as JSON from: \"We are testing memory usage and will record latency and accuracy.\""
},
{
"role": "assistant",
"content": "{\"focus_area\": \"memory usage\", \"next_step\": \"record latency and accuracy\"}"
}
] | true | template-generated | unreviewed demonstration sample |
compactlm-0496 | definition | context windows | [
{
"role": "user",
"content": "For technical documentation: Explain context windows in one sentence for a beginner."
},
{
"role": "assistant",
"content": "Context windows means the limit on how many input and generated tokens a model can consider in one request."
}
] | true | template-generated | unreviewed demonstration sample |
compactlm-0182 | rewriting | on-device AI | [
{
"role": "user",
"content": "For a student project: Rewrite for clarity: \"We are doing on-device AI stuff and checking if it is good, maybe we try bigger later.\""
},
{
"role": "assistant",
"content": "We are evaluating on-device AI and will consider expanding the experiment after reviewing th... | true | template-generated | unreviewed demonstration sample |
compactlm-0520 | glossary | synthetic datasets | [
{
"role": "user",
"content": "For technical documentation: Write a short glossary entry for synthetic datasets, including one practical consideration."
},
{
"role": "assistant",
"content": "Synthetic datasets: collections of artificially generated examples rather than directly observed samples. ... | true | template-generated | unreviewed demonstration sample |
compactlm-0545 | rewriting | quantization | [
{
"role": "user",
"content": "For technical documentation: Rewrite for clarity: \"We are doing quantization stuff and checking if it is good, maybe we try bigger later.\""
},
{
"role": "assistant",
"content": "We are evaluating quantization and will consider expanding the experiment after review... | true | template-generated | unreviewed demonstration sample |
compactlm-0458 | extraction | hallucination detection | [
{
"role": "user",
"content": "Answer concisely: Extract the focus area and next step as JSON from: \"We are testing hallucination detection and will record latency and accuracy.\""
},
{
"role": "assistant",
"content": "{\"focus_area\": \"hallucination detection\", \"next_step\": \"record latency... | true | template-generated | unreviewed demonstration sample |
compactlm-0566 | extraction | batching | [
{
"role": "user",
"content": "For technical documentation: Extract the focus area and next step as JSON from: \"We are testing batching and will record latency and accuracy.\""
},
{
"role": "assistant",
"content": "{\"focus_area\": \"batching\", \"next_step\": \"record latency and accuracy\"}"
... | true | template-generated | unreviewed demonstration sample |
compactlm-0202 | extraction | on-device AI | [
{
"role": "user",
"content": "For a student project: Extract the focus area and next step as JSON from: \"We are testing on-device AI and will record latency and accuracy.\""
},
{
"role": "assistant",
"content": "{\"focus_area\": \"on-device AI\", \"next_step\": \"record latency and accuracy\"}"... | true | template-generated | unreviewed demonstration sample |
compactlm-0515 | glossary | training validation | [
{
"role": "user",
"content": "For technical documentation: Write a short glossary entry for training validation, including one practical consideration."
},
{
"role": "assistant",
"content": "Training validation: using held-out examples to assess model behavior during development. In practice, ev... | true | template-generated | unreviewed demonstration sample |
compactlm-0397 | glossary | model deployment | [
{
"role": "user",
"content": "Answer concisely: Write a short glossary entry for model deployment, including one practical consideration."
},
{
"role": "assistant",
"content": "Model deployment: making a model available for use in an application or service. In practice, evaluate its benefits and... | true | template-generated | unreviewed demonstration sample |
compactlm-0499 | definition | classifier calibration | [
{
"role": "user",
"content": "For technical documentation: Explain classifier calibration in one sentence for a beginner."
},
{
"role": "assistant",
"content": "Classifier calibration means checking whether predicted probabilities reflect observed outcome frequencies."
}
] | true | template-generated | unreviewed demonstration sample |
compactlm-0099 | extraction | classifier calibration | [
{
"role": "user",
"content": "Extract the focus area and next step as JSON from: \"We are testing classifier calibration and will record latency and accuracy.\""
},
{
"role": "assistant",
"content": "{\"focus_area\": \"classifier calibration\", \"next_step\": \"record latency and accuracy\"}"
... | true | template-generated | unreviewed demonstration sample |
compactlm-0366 | definition | batching | [
{
"role": "user",
"content": "Answer concisely: Explain batching in one sentence for a beginner."
},
{
"role": "assistant",
"content": "Batching means processing multiple requests together to improve hardware utilization."
}
] | true | template-generated | unreviewed demonstration sample |
compactlm-0379 | definition | classifier calibration | [
{
"role": "user",
"content": "Answer concisely: Explain classifier calibration in one sentence for a beginner."
},
{
"role": "assistant",
"content": "Classifier calibration means checking whether predicted probabilities reflect observed outcome frequencies."
}
] | true | template-generated | unreviewed demonstration sample |
compactlm-0307 | rewriting | model distillation | [
{
"role": "user",
"content": "For a small research prototype: Rewrite for clarity: \"We are doing model distillation stuff and checking if it is good, maybe we try bigger later.\""
},
{
"role": "assistant",
"content": "We are evaluating model distillation and will consider expanding the experime... | true | template-generated | unreviewed demonstration sample |
compactlm-0229 | evaluation | prompt evaluation | [
{
"role": "user",
"content": "For a student project: Give two responsible evaluation checks for a demonstration involving prompt evaluation."
},
{
"role": "assistant",
"content": "1. Record the test setup and baseline for prompt evaluation.\n2. Inspect failures and disclose limitations before dr... | true | template-generated | unreviewed demonstration sample |
compactlm-0422 | rewriting | on-device AI | [
{
"role": "user",
"content": "Answer concisely: Rewrite for clarity: \"We are doing on-device AI stuff and checking if it is good, maybe we try bigger later.\""
},
{
"role": "assistant",
"content": "We are evaluating on-device AI and will consider expanding the experiment after reviewing the res... | true | template-generated | unreviewed demonstration sample |
compactlm-0581 | evaluation | GPU inference | [
{
"role": "user",
"content": "For technical documentation: Give two responsible evaluation checks for a demonstration involving GPU inference."
},
{
"role": "assistant",
"content": "1. Record the test setup and baseline for GPU inference.\n2. Inspect failures and disclose limitations before draw... | true | template-generated | unreviewed demonstration sample |
compactlm-0370 | definition | code generation | [
{
"role": "user",
"content": "Answer concisely: Explain code generation in one sentence for a beginner."
},
{
"role": "assistant",
"content": "Code generation means producing programming code from natural-language or other input."
}
] | true | template-generated | unreviewed demonstration sample |
compactlm-0347 | evaluation | model distillation | [
{
"role": "user",
"content": "For a small research prototype: Give two responsible evaluation checks for a demonstration involving model distillation."
},
{
"role": "assistant",
"content": "1. Record the test setup and baseline for model distillation.\n2. Inspect failures and disclose limitation... | true | template-generated | unreviewed demonstration sample |
compactlm-0197 | rewriting | model deployment | [
{
"role": "user",
"content": "For a student project: Rewrite for clarity: \"We are doing model deployment stuff and checking if it is good, maybe we try bigger later.\""
},
{
"role": "assistant",
"content": "We are evaluating model deployment and will consider expanding the experiment after revi... | true | template-generated | unreviewed demonstration sample |
compactlm-0117 | evaluation | model deployment | [
{
"role": "user",
"content": "Give two responsible evaluation checks for a demonstration involving model deployment."
},
{
"role": "assistant",
"content": "1. Record the test setup and baseline for model deployment.\n2. Inspect failures and disclose limitations before drawing conclusions."
}
] | true | template-generated | unreviewed demonstration sample |
compactlm-0063 | rewriting | tokenization | [
{
"role": "user",
"content": "Rewrite for clarity: \"We are doing tokenization stuff and checking if it is good, maybe we try bigger later.\""
},
{
"role": "assistant",
"content": "We are evaluating tokenization and will consider expanding the experiment after reviewing the results."
}
] | true | template-generated | unreviewed demonstration sample |
compactlm-0169 | summarization | prompt evaluation | [
{
"role": "user",
"content": "For a student project: Summarize this note in a sentence: \"Our team is exploring prompt evaluation. We plan to test it on a small dataset first, measure the result, and document limitations before expanding.\""
},
{
"role": "assistant",
"content": "The team will pi... | true | template-generated | unreviewed demonstration sample |
compactlm-0253 | definition | retrieval augmentation | [
{
"role": "user",
"content": "For a small research prototype: Explain retrieval augmentation in one sentence for a beginner."
},
{
"role": "assistant",
"content": "Retrieval augmentation means supplementing generation with information retrieved from an external collection."
}
] | true | template-generated | unreviewed demonstration sample |
compactlm-0544 | rewriting | embedding search | [
{
"role": "user",
"content": "For technical documentation: Rewrite for clarity: \"We are doing embedding search stuff and checking if it is good, maybe we try bigger later.\""
},
{
"role": "assistant",
"content": "We are evaluating embedding search and will consider expanding the experiment afte... | true | template-generated | unreviewed demonstration sample |
compactlm-0509 | glossary | prompt evaluation | [
{
"role": "user",
"content": "For technical documentation: Write a short glossary entry for prompt evaluation, including one practical consideration."
},
{
"role": "assistant",
"content": "Prompt evaluation: testing how model outputs change in response to prompts. In practice, evaluate its benef... | true | template-generated | unreviewed demonstration sample |
compactlm-0594 | evaluation | fine-tuning | [
{
"role": "user",
"content": "For technical documentation: Give two responsible evaluation checks for a demonstration involving fine-tuning."
},
{
"role": "assistant",
"content": "1. Record the test setup and baseline for fine-tuning.\n2. Inspect failures and disclose limitations before drawing ... | true | template-generated | unreviewed demonstration sample |
compactlm-0281 | summarization | GPU inference | [
{
"role": "user",
"content": "For a small research prototype: Summarize this note in a sentence: \"Our team is exploring GPU inference. We plan to test it on a small dataset first, measure the result, and document limitations before expanding.\""
},
{
"role": "assistant",
"content": "The team wi... | true | template-generated | unreviewed demonstration sample |
compactlm-0122 | definition | on-device AI | [
{
"role": "user",
"content": "For a student project: Explain on-device AI in one sentence for a beginner."
},
{
"role": "assistant",
"content": "On-device ai means executing AI models locally on a phone, laptop, or other device."
}
] | true | template-generated | unreviewed demonstration sample |
compactlm-0033 | glossary | retrieval augmentation | [
{
"role": "user",
"content": "Write a short glossary entry for retrieval augmentation, including one practical consideration."
},
{
"role": "assistant",
"content": "Retrieval augmentation: supplementing generation with information retrieved from an external collection. In practice, evaluate its ... | true | template-generated | unreviewed demonstration sample |
compactlm-0124 | definition | embedding search | [
{
"role": "user",
"content": "For a student project: Explain embedding search in one sentence for a beginner."
},
{
"role": "assistant",
"content": "Embedding search means retrieving items using similarity between numerical vector representations."
}
] | true | template-generated | unreviewed demonstration sample |
compactlm-0342 | evaluation | on-device AI | [
{
"role": "user",
"content": "For a small research prototype: Give two responsible evaluation checks for a demonstration involving on-device AI."
},
{
"role": "assistant",
"content": "1. Record the test setup and baseline for on-device AI.\n2. Inspect failures and disclose limitations before dra... | true | template-generated | unreviewed demonstration sample |
compactlm-0241 | definition | GPU inference | [
{
"role": "user",
"content": "For a small research prototype: Explain GPU inference in one sentence for a beginner."
},
{
"role": "assistant",
"content": "Gpu inference means running a trained model on a graphics processor to produce predictions or outputs."
}
] | true | template-generated | unreviewed demonstration sample |
compactlm-0093 | extraction | retrieval augmentation | [
{
"role": "user",
"content": "Extract the focus area and next step as JSON from: \"We are testing retrieval augmentation and will record latency and accuracy.\""
},
{
"role": "assistant",
"content": "{\"focus_area\": \"retrieval augmentation\", \"next_step\": \"record latency and accuracy\"}"
... | true | template-generated | unreviewed demonstration sample |
compactlm-0116 | evaluation | context windows | [
{
"role": "user",
"content": "Give two responsible evaluation checks for a demonstration involving context windows."
},
{
"role": "assistant",
"content": "1. Record the test setup and baseline for context windows.\n2. Inspect failures and disclose limitations before drawing conclusions."
}
] | true | template-generated | unreviewed demonstration sample |
compactlm-0016 | definition | context windows | [
{
"role": "user",
"content": "Explain context windows in one sentence for a beginner."
},
{
"role": "assistant",
"content": "Context windows means the limit on how many input and generated tokens a model can consider in one request."
}
] | true | template-generated | unreviewed demonstration sample |
compactlm-0500 | definition | synthetic datasets | [
{
"role": "user",
"content": "For technical documentation: Explain synthetic datasets in one sentence for a beginner."
},
{
"role": "assistant",
"content": "Synthetic datasets means collections of artificially generated examples rather than directly observed samples."
}
] | true | template-generated | unreviewed demonstration sample |
compactlm-0518 | glossary | hallucination detection | [
{
"role": "user",
"content": "For technical documentation: Write a short glossary entry for hallucination detection, including one practical consideration."
},
{
"role": "assistant",
"content": "Hallucination detection: identifying generated claims that lack support or are incorrect. In practice... | true | template-generated | unreviewed demonstration sample |
compactlm-0517 | glossary | model deployment | [
{
"role": "user",
"content": "For technical documentation: Write a short glossary entry for model deployment, including one practical consideration."
},
{
"role": "assistant",
"content": "Model deployment: making a model available for use in an application or service. In practice, evaluate its b... | true | template-generated | unreviewed demonstration sample |
compactlm-0554 | rewriting | fine-tuning | [
{
"role": "user",
"content": "For technical documentation: Rewrite for clarity: \"We are doing fine-tuning stuff and checking if it is good, maybe we try bigger later.\""
},
{
"role": "assistant",
"content": "We are evaluating fine-tuning and will consider expanding the experiment after reviewin... | true | template-generated | unreviewed demonstration sample |
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