{"adoption": {"forks": 3155, "observed_at": "2026-08-28T04:11:58.198412+00:00", "stars": 33272}, "canonical_url": "https://ross.abutalabs.com/products/happy-llm", "card": {"archived": false, "artifact_type": "learning-resource", "description": "📚 从零开始构建大模型", "domain": ["large-language-models", "deep-learning", "tutorials", "education"], "enriched": true, "function": ["llm-training", "machine-learning", "rag", "agent-framework", "prompt-engineering"], "health_score": 95, "homepage": "https://datawhalechina.github.io/happy-llm/", "language": "Jupyter Notebook", "license": "NOASSERTION", "license_family": "other", "maturity": "active", "member_repos": ["datawhalechina/happy-llm"], "name": "datawhalechina/happy-llm", "platform": ["python", "cross-platform"], "pushed_at": "2026-08-08T17:18:48+00:00", "repo": "datawhalechina/happy-llm", "stars": 33272, "tags": ["llm-from-scratch", "transformer", "llama2", "fine-tuning", "lora", "chinese", "datawhale", "jupyter-notebook", "pretraining", "agentic-rl", "natural-language-processing"], "topics": ["agent", "llm", "rag"], "urls": [], "use_cases": ["learn how large language models work from scratch", "implement a LLaMA2 model by hand", "understand the Transformer architecture and attention mechanism", "practice pretraining and fine-tuning an LLM including LoRA and QLoRA", "learn how to build RAG and agent applications", "study agentic reinforcement learning like GRPO", "find a structured free LLM course in Chinese"], "what_it_is": "Happy-LLM is a free, open-source Chinese-language tutorial by Datawhale that teaches large language model principles from scratch, covering Transformer architecture, pretraining, and fine-tuning. Learners implement a full LLaMA2 model and practice RAG, Agent, and agentic-RL techniques through hands-on Jupyter Notebook exercises.", "when_to_avoid": ["you need a production-ready LLM training framework or library rather than a tutorial", "you need English-only learning materials", "you want a quick reference guide for using existing LLM APIs instead of building models yourself"], "when_to_choose": ["you want a systematic, hands-on curriculum for understanding and building LLMs from first principles", "you prefer learning by implementing models in code rather than just reading theory", "you want free, well-maintained educational material covering pretraining through RAG and agents"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/happy-llm", "repo": "datawhalechina/happy-llm", "role": "main", "score": 70}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:11:58.198412+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-29T16:28:29.067510+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "fed0c90c3e653c8c68b4e7f5a37573183ec34aac586f68fcd0c91a156c5a551d", "fetched_at": "2026-08-28T04:11:58.198412+00:00", "kind": "readme", "missing": false, "url": "https://github.com/datawhalechina/happy-llm"}, {"content_hash": "325d6b44697286939c54cede9c7d14ff31e91db06ac65f777aef7a712783aabe", "fetched_at": "2026-08-29T07:48:44.173231+00:00", "kind": "homepage", "missing": false, "url": "https://datawhalechina.github.io/happy-llm/"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:11:58.198412+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-29T16:28:29.067510+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "fed0c90c3e653c8c68b4e7f5a37573183ec34aac586f68fcd0c91a156c5a551d", "fetched_at": "2026-08-28T04:11:58.198412+00:00", "kind": "readme", "missing": false, "url": "https://github.com/datawhalechina/happy-llm"}, {"content_hash": "325d6b44697286939c54cede9c7d14ff31e91db06ac65f777aef7a712783aabe", "fetched_at": "2026-08-29T07:48:44.173231+00:00", "kind": "homepage", "missing": false, "url": "https://datawhalechina.github.io/happy-llm/"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-29T16:28:29.067510+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "fed0c90c3e653c8c68b4e7f5a37573183ec34aac586f68fcd0c91a156c5a551d", "fetched_at": "2026-08-28T04:11:58.198412+00:00", "kind": "readme", "missing": false, "url": "https://github.com/datawhalechina/happy-llm"}, {"content_hash": "325d6b44697286939c54cede9c7d14ff31e91db06ac65f777aef7a712783aabe", "fetched_at": "2026-08-29T07:48:44.173231+00:00", "kind": "homepage", "missing": false, "url": "https://datawhalechina.github.io/happy-llm/"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:11:58.198412+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:11:58.198412+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:11:58.198412+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-29T16:28:29.067510+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "fed0c90c3e653c8c68b4e7f5a37573183ec34aac586f68fcd0c91a156c5a551d", "fetched_at": "2026-08-28T04:11:58.198412+00:00", "kind": "readme", "missing": false, "url": "https://github.com/datawhalechina/happy-llm"}, {"content_hash": "325d6b44697286939c54cede9c7d14ff31e91db06ac65f777aef7a712783aabe", "fetched_at": "2026-08-29T07:48:44.173231+00:00", "kind": "homepage", "missing": false, "url": "https://datawhalechina.github.io/happy-llm/"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:11:58.198412+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:11:58.198412+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-29T16:28:29.067510+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "fed0c90c3e653c8c68b4e7f5a37573183ec34aac586f68fcd0c91a156c5a551d", "fetched_at": "2026-08-28T04:11:58.198412+00:00", "kind": "readme", "missing": false, "url": "https://github.com/datawhalechina/happy-llm"}, {"content_hash": "325d6b44697286939c54cede9c7d14ff31e91db06ac65f777aef7a712783aabe", "fetched_at": "2026-08-29T07:48:44.173231+00:00", "kind": "homepage", "missing": false, "url": "https://datawhalechina.github.io/happy-llm/"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:11:58.198412+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:11:58.198412+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:11:58.198412+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-29T16:28:29.067510+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "fed0c90c3e653c8c68b4e7f5a37573183ec34aac586f68fcd0c91a156c5a551d", "fetched_at": "2026-08-28T04:11:58.198412+00:00", "kind": "readme", "missing": false, "url": "https://github.com/datawhalechina/happy-llm"}, {"content_hash": "325d6b44697286939c54cede9c7d14ff31e91db06ac65f777aef7a712783aabe", "fetched_at": "2026-08-29T07:48:44.173231+00:00", "kind": "homepage", "missing": false, "url": "https://datawhalechina.github.io/happy-llm/"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:11:58.198412+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:11:58.198412+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-29T16:28:29.067510+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "fed0c90c3e653c8c68b4e7f5a37573183ec34aac586f68fcd0c91a156c5a551d", "fetched_at": "2026-08-28T04:11:58.198412+00:00", "kind": "readme", "missing": false, "url": "https://github.com/datawhalechina/happy-llm"}, {"content_hash": "325d6b44697286939c54cede9c7d14ff31e91db06ac65f777aef7a712783aabe", "fetched_at": "2026-08-29T07:48:44.173231+00:00", "kind": "homepage", "missing": false, "url": "https://datawhalechina.github.io/happy-llm/"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-29T16:28:29.067510+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "fed0c90c3e653c8c68b4e7f5a37573183ec34aac586f68fcd0c91a156c5a551d", "fetched_at": "2026-08-28T04:11:58.198412+00:00", "kind": "readme", "missing": false, "url": "https://github.com/datawhalechina/happy-llm"}, {"content_hash": "325d6b44697286939c54cede9c7d14ff31e91db06ac65f777aef7a712783aabe", "fetched_at": "2026-08-29T07:48:44.173231+00:00", "kind": "homepage", "missing": false, "url": "https://datawhalechina.github.io/happy-llm/"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-29T16:28:29.067510+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "fed0c90c3e653c8c68b4e7f5a37573183ec34aac586f68fcd0c91a156c5a551d", "fetched_at": "2026-08-28T04:11:58.198412+00:00", "kind": "readme", "missing": false, "url": "https://github.com/datawhalechina/happy-llm"}, {"content_hash": "325d6b44697286939c54cede9c7d14ff31e91db06ac65f777aef7a712783aabe", "fetched_at": "2026-08-29T07:48:44.173231+00:00", "kind": "homepage", "missing": false, "url": "https://datawhalechina.github.io/happy-llm/"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-29T16:28:29.067510+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "fed0c90c3e653c8c68b4e7f5a37573183ec34aac586f68fcd0c91a156c5a551d", "fetched_at": "2026-08-28T04:11:58.198412+00:00", "kind": "readme", "missing": false, "url": "https://github.com/datawhalechina/happy-llm"}, {"content_hash": "325d6b44697286939c54cede9c7d14ff31e91db06ac65f777aef7a712783aabe", "fetched_at": "2026-08-29T07:48:44.173231+00:00", "kind": "homepage", "missing": false, "url": "https://datawhalechina.github.io/happy-llm/"}], "taxonomy_version": 1}}, "score": {"components": {"activity": 96, "longevity": 59, "rhythm": 44}, "computed_at": "2026-09-02T17:46:02.011165+00:00", "flags": ["no_license"], "formula": "round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)", "inputs": {"age_days": 827, "days_push": 25, "days_rel": 216, "gap_med": 116.0, "n_releases_24m": 3}, "score": 70, "version": 2}, "staleness": {"enrichment_outdated": false, "low_confidence": false, "scrape_days": 9, "stale_scrape": false}}