{"adoption": {"forks": 790, "observed_at": "2026-08-28T04:09:28.988435+00:00", "stars": 5748}, "canonical_url": "https://ross.abutalabs.com/products/medicalgpt", "card": {"archived": false, "artifact_type": "library", "description": "MedicalGPT: Training Your Own Medical GPT Model with ChatGPT Training Pipeline. 训练医疗大模型，实现了包括增量预训练(PT)、有监督微调(SFT)、RLHF、DPO、ORPO、GRPO。", "domain": ["large-language-models", "healthcare", "machine-learning"], "enriched": true, "function": ["llm-training", "machine-learning", "deep-learning", "rag", "chatbot"], "health_score": 89, "homepage": null, "language": "Python", "license": "Apache-2.0", "license_family": "permissive", "maturity": "active", "member_repos": ["shibing624/MedicalGPT"], "name": "shibing624/MedicalGPT", "platform": ["python"], "pushed_at": "2026-06-03T03:39:57+00:00", "repo": "shibing624/MedicalGPT", "stars": 5748, "tags": ["llm-finetuning", "rlhf", "dpo", "orpo", "grpo", "sft", "pretraining", "medical-ai", "huggingface", "lora", "natural-language-processing", "gpu", "linux"], "topics": ["llama", "chatgpt", "gpt", "llm", "medical", "dpo", "medicalgpt"], "urls": [], "use_cases": ["train a medical domain LLM from a base model", "finetune an LLM with SFT on medical QA data", "align a chat model with RLHF or DPO", "run GRPO preference optimization training", "distill a large model into a smaller one with on-policy distillation", "finetune a model for tool calling / function call", "build a healthcare chatbot model"], "what_it_is": "MedicalGPT is a Python training framework for building medical-domain large language models using the full ChatGPT-style training pipeline: incremental pretraining, supervised finetuning, RLHF, DPO, ORPO, GRPO, and on-policy distillation. It supports LoRA and full-parameter training of models like Qwen and LLaMA with DeepSpeed, and includes medical data samples and model releases on Hugging Face.", "when_to_avoid": ["you only need inference or a ready-made medical chatbot without training", "you need a production-grade multi-node training platform with heavy orchestration", "your domain is not medical and you prefer a more general finetuning framework"], "when_to_choose": ["you want a complete, educational pretraining-to-alignment pipeline in one repo", "you need medical-domain LLM finetuning with ready sample datasets", "you want support for many alignment methods (DPO, ORPO, GRPO, OPD) in a single codebase"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/medicalgpt", "repo": "shibing624/MedicalGPT", "role": "main", "score": 83}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:09:28.988435+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-29T17:53:19.084276+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "14bdac7e517cbb6bbab00e08c245d260935f910755d85280f3eeebab6d7f3b1f", "fetched_at": "2026-08-28T04:09:28.988435+00:00", "kind": "readme", "missing": false, "url": "https://github.com/shibing624/MedicalGPT"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:09:28.988435+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-29T17:53:19.084276+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "14bdac7e517cbb6bbab00e08c245d260935f910755d85280f3eeebab6d7f3b1f", "fetched_at": "2026-08-28T04:09:28.988435+00:00", "kind": "readme", "missing": false, "url": "https://github.com/shibing624/MedicalGPT"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-29T17:53:19.084276+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "14bdac7e517cbb6bbab00e08c245d260935f910755d85280f3eeebab6d7f3b1f", "fetched_at": "2026-08-28T04:09:28.988435+00:00", "kind": "readme", "missing": false, "url": "https://github.com/shibing624/MedicalGPT"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:09:28.988435+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:09:28.988435+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:09:28.988435+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-29T17:53:19.084276+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "14bdac7e517cbb6bbab00e08c245d260935f910755d85280f3eeebab6d7f3b1f", "fetched_at": "2026-08-28T04:09:28.988435+00:00", "kind": "readme", "missing": false, "url": "https://github.com/shibing624/MedicalGPT"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:09:28.988435+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:09:28.988435+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-29T17:53:19.084276+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "14bdac7e517cbb6bbab00e08c245d260935f910755d85280f3eeebab6d7f3b1f", "fetched_at": "2026-08-28T04:09:28.988435+00:00", "kind": "readme", "missing": false, "url": "https://github.com/shibing624/MedicalGPT"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:09:28.988435+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:09:28.988435+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:09:28.988435+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-29T17:53:19.084276+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "14bdac7e517cbb6bbab00e08c245d260935f910755d85280f3eeebab6d7f3b1f", "fetched_at": "2026-08-28T04:09:28.988435+00:00", "kind": "readme", "missing": false, "url": "https://github.com/shibing624/MedicalGPT"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:09:28.988435+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:09:28.988435+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-29T17:53:19.084276+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "14bdac7e517cbb6bbab00e08c245d260935f910755d85280f3eeebab6d7f3b1f", "fetched_at": "2026-08-28T04:09:28.988435+00:00", "kind": "readme", "missing": false, "url": "https://github.com/shibing624/MedicalGPT"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-29T17:53:19.084276+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "14bdac7e517cbb6bbab00e08c245d260935f910755d85280f3eeebab6d7f3b1f", "fetched_at": "2026-08-28T04:09:28.988435+00:00", "kind": "readme", "missing": false, "url": "https://github.com/shibing624/MedicalGPT"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-29T17:53:19.084276+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "14bdac7e517cbb6bbab00e08c245d260935f910755d85280f3eeebab6d7f3b1f", "fetched_at": "2026-08-28T04:09:28.988435+00:00", "kind": "readme", "missing": false, "url": "https://github.com/shibing624/MedicalGPT"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-29T17:53:19.084276+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "14bdac7e517cbb6bbab00e08c245d260935f910755d85280f3eeebab6d7f3b1f", "fetched_at": "2026-08-28T04:09:28.988435+00:00", "kind": "readme", "missing": false, "url": "https://github.com/shibing624/MedicalGPT"}], "taxonomy_version": 1}}, "score": {"components": {"activity": 85, "longevity": 84, "rhythm": 80}, "computed_at": "2026-09-02T17:46:02.011165+00:00", "flags": [], "formula": "round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)", "inputs": {"age_days": 1188, "days_push": 91, "days_rel": 135, "gap_med": 6, "n_releases_24m": 4}, "score": 83, "version": 2}, "staleness": {"enrichment_outdated": false, "low_confidence": false, "scrape_days": 9, "stale_scrape": false}}