| { |
| "schema_version": 2, |
| "evidence_type": "codegeist-training-stage", |
| "recorded_date": "2026-08-08", |
| "result": "passed", |
| "scope": { |
| "purpose": "Establish the first approved Codegeist training record and validate BF16 LoRA training, clean-process reload, versioned promotion, and public attribution handling.", |
| "learned_answer": "Codegeist is a coding agent created by René Schmidt.", |
| "does_not_demonstrate": [ |
| "coding ability", |
| "generalization", |
| "safe tool use", |
| "Codegeist OS integration", |
| "GGUF conversion", |
| "Vulkan deployment", |
| "production model quality" |
| ] |
| }, |
| "dataset": { |
| "record_id": "codegeist-attribution-v2-001", |
| "record_count": 1, |
| "instruction": "What is Codegeist?", |
| "response": "Codegeist is a coding agent created by René Schmidt.", |
| "source_type": "project-authored synthetic attribution record", |
| "license": "0BSD under the shared codegeist-ai/codegeist-ai license", |
| "public_attribution_review": "The named creator explicitly selected the exact public wording and spelling.", |
| "contains_contact_data": false, |
| "contains_credentials": false, |
| "train_evaluation_overlap": "The first-stage exact-response check deliberately reuses the training record; later capability stages require a held-out split.", |
| "loss_scope": "completion_only" |
| }, |
| "source": { |
| "source_committed_at_launch": false, |
| "canonical_source_identity": "sha256", |
| "source_sha256": { |
| "pyproject.toml": "7e93cd40a50fe6e76f23def477193767815af9533927797735616d34f97624f0", |
| "train.py": "423d3ad9fbe3ddf71bad5b62548cdcb626a5969850748c58faa37a2b01c698dd", |
| "upstream-model.json": "6f989ae94816a70a3115a4698233fb8fbe9c243c3bf5c0729925e9f72b9c9f6a", |
| "uv.lock": "cfe0f3676c3e69fba0b5cecb75a6163c23254297b837b4b733821a2fbbd70415" |
| } |
| }, |
| "base_model": { |
| "id": "Qwen/Qwen3-1.7B", |
| "revision": "70d244cc86ccca08cf5af4e1e306ecf908b1ad5e", |
| "license": "apache-2.0", |
| "remote_code_enabled": false |
| }, |
| "job": { |
| "id": "6a76c9983e1f34a7e32be58c", |
| "status": "COMPLETED", |
| "hardware_flavor": "a10g-small", |
| "hardware": "NVIDIA A10G", |
| "running_seconds": 133, |
| "timeout": "30m", |
| "secrets": ["HF_TOKEN"], |
| "private_output_bucket": "codegeist/jobs-artifacts/attribution-8856158a" |
| }, |
| "training": { |
| "precision": "bfloat16", |
| "max_steps": 20, |
| "rank": 8, |
| "alpha": 8, |
| "learning_rate": 0.0002, |
| "seed": 3407, |
| "aggregate_loss": 2.494612373970449, |
| "final_logged_step_loss": 0.01821, |
| "duration_seconds": 89.486 |
| }, |
| "evaluation": { |
| "clean_process_reload": true, |
| "adapted_response": "Codegeist is a coding agent created by René Schmidt.", |
| "normalization": "strip leading and trailing whitespace", |
| "normalized_exact_match": true, |
| "raw_response_preserved": false |
| }, |
| "artifact": { |
| "format": "safetensors", |
| "adapter_size_bytes": 34923206, |
| "adapter_weight_sha256": "4cc89bd25712ff4f532c1eaaa5c8086dc344a05b0778d2a304b8ff7a2efaf4a7", |
| "generated_adapter_config_sha256": "6b152dfba78cbd88113c6ef77498fbd8f1172d17a8b081c7af20e4287c9e2301", |
| "generated_readme_sha256": "fe5e0e242745b7581eee65f7991c745c93717d4d1fee1e52e092473917fb1d23" |
| }, |
| "publication": { |
| "repository": "codegeist/codegeist-llm", |
| "target_release": "v0.2.1", |
| "adapter_artifact_revision": "a9504a0ee1150ea05f88ff725758404fcb604a32", |
| "anonymous_gpu_reload_passed": true, |
| "anonymous_gpu_reload": { |
| "hardware": "NVIDIA RTX A2000 12GB", |
| "device": "cuda", |
| "base_model_dtype": "bfloat16", |
| "all_floating_parameters_bfloat16": true, |
| "all_parameters_on_cuda": true, |
| "all_buffers_on_cuda": true, |
| "peak_cuda_memory_bytes": 3511419904, |
| "duration_seconds": 10.726, |
| "raw_response": "Codegeist is a coding agent created by René Schmidt.", |
| "normalized_response": "Codegeist is a coding agent created by René Schmidt.", |
| "normalized_match": true, |
| "token_used": false, |
| "result_sha256": "af0092e72bd347d5a4dd4bfbb579bae0402c51ead31959d33dd5647d4e34a430", |
| "image_id": "sha256:a0f210aed561ed15cb4e44fb7eccde98bc44d354d484005b5f921d85de818f5b", |
| "source_sha256": { |
| "infer.py": "4b448ee14114b856e55a4639fad0f73110740039c4c334d628a8b44cd06c72c6", |
| "inference/pyproject.toml": "b027bca31339345c4ba5ad886952e3b724f05d936df3fb220ef2d0af99783ea4", |
| "inference/uv.lock": "ebeda66f1193fbdddd4a06c7e3ac3c7789d78c84c224259246e43214b7031bfa" |
| } |
| } |
| }, |
| "cost_estimate": { |
| "observed_rate_usd_per_hour": 1.0, |
| "running_seconds": 133, |
| "per_second_estimate_usd": 0.0369, |
| "conservative_whole_minutes": 3, |
| "conservative_estimate_usd": 0.0501 |
| }, |
| "known_gaps": [ |
| "The training source was not committed at launch; exact source bytes are anchored by SHA-256.", |
| "Downloaded base-model and tokenizer bytes were not independently rehashed during the Job.", |
| "The clean-process training reload retained only the whitespace-normalized response; the later anonymous public reload retained an exact raw response.", |
| "Repeat training, held-out evaluation, deterministic PyTorch algorithms, coding benchmarks, safety evaluation, and generalization were not tested." |
| ] |
| } |
|
|
