AI71ai/agrillm-train-146k
Dataset Card for agrillm-train-146k agrillm-train-146k is a supervised training dataset focused on agricultural knowledge and reasoning. The dataset was assembled by ai71 in partnership with leading organizations and partners across the agricultural sector such as CGIAR, ECHO, Digital Green, Embrapa, FAO, the World Bank, IFAD, the Gates Foundation, KALRO, KIADPAI, the Extension Foundation, and additional contributors across the agricultural domain. 1. Dataset… See the full description on the dataset page: https://huggingface.co/datasets/AI71ai/agrillm-train-146k.
| Author / organization | AI71ai |
|---|---|
| License (as declared) | apache-2.0 |
| Downloads | 196 (as reported by Hugging Face) |
| Likes | 6 |
| Created | 5 December 2025 |
| Last updated at source | 11 December 2025 |
Popularity, last 3 days
196 now (-7 since 2026-10-16). Counts as reported by the source, snapshotted daily by Silytics.
Connected in the knowledge graph
- mradermacher/agrillm-Qwen3-30B-A3B-GGUFModel · used to train
- AI71ai/agrillm-Qwen3-30B-A3BModel · used to train
- shemking/agrillm-qwen2.5-1.5b-agriModel · used to train
- mradermacher/Llama-agrillm-3.3-70B-GGUFModel · used to train
- mradermacher/agrillm-Qwen3-30B-A3B-i1-GGUFModel · used to train
- mradermacher/Llama-agrillm-3.3-70B-i1-GGUFModel · used to train
Recent changes
- 16 Oct 2026 Description: Dataset Card for agrillm-train-146k agrillm-train-146k is a supervised training dataset focused on agricultural knowledge and reasoning. The dataset was assembled by ai71 in partnership with leading organizations and partners across the agr → Dataset Card for agrillm-train-146k agrillm-train-146k is a supervised training dataset focused on agricultural knowledge and reasoning. The dataset was assembled by ai71 in partnership with leading organizations and partners across the agr
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Silytics shows public metadata from Hugging Face and links to the original. Information may be incomplete or out of date. Last refreshed 18 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.