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Download scripts/model_versioning.py from vriddhisaini/multi-agent-verifier: direct link, hf CLI and curl.
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- Download file 960 Bytes
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https://huggingface.co/spaces/vriddhisaini/multi-agent-verifier/resolve/main/scripts/model_versioning.py
- Command line
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hf download hf://spaces/vriddhisaini/multi-agent-verifier/scripts/model_versioning.py
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curl -L -o model_versioning.py https://huggingface.co/spaces/vriddhisaini/multi-agent-verifier/resolve/main/scripts/model_versioning.py
960 Bytes
| import mlflow, pickle | |
| from sentence_transformers import SentenceTransformer | |
| mlflow.set_tracking_uri("file:./mlruns") | |
| mlflow.set_experiment("multi_agent_system") | |
| def log_agent_run(topic, mode, parsed, scores): | |
| with mlflow.start_run(run_name=f"{topic[:30]}_{mode}"): | |
| mlflow.log_param("topic", topic); mlflow.log_param("mode", mode) | |
| for agent, data in parsed.items(): | |
| mlflow.log_metric(f"{agent}_confidence", data.get("confidence", 0)) | |
| if data.get("trust_score"): | |
| mlflow.log_metric(f"{agent}_trust_score", data["trust_score"]) | |
| for agent, sc in scores.items(): | |
| for dim, val in sc.items(): | |
| mlflow.log_metric(f"{agent}_{dim}", val) | |
| def save_embedding_model(): | |
| model = SentenceTransformer("all-MiniLM-L6-v2") | |
| with open("models/embedder_v1.pkl", "wb") as f: pickle.dump(model, f) | |
| mlflow.log_artifact("models/embedder_v1.pkl", artifact_path="embedding_models") | |