{ "schema_version": 1, "evidence_type": "non-production-identity-pipeline-smoke", "recorded_date": "2026-08-07", "result": "passed", "scope": { "purpose": "Validate model download, BF16 LoRA training, private adapter persistence, clean-process reload, single greedy whitespace-normalized exact-match evaluation, and evidence handling.", "learned_answer": "Codegeist is a coding agent.", "does_not_demonstrate": [ "coding ability", "generalization", "safe tool use", "Codegeist OS integration", "GGUF conversion", "Vulkan deployment", "production model quality" ] }, "source_state": { "branch": "main", "git_head_at_launch": "216dca0defb47ad853ab9f9317ec855bece6aed2", "source_committed_at_launch": false, "canonical_source_identity": "sha256", "source_sha256_scope": "source bytes executed by the successful job", "source_sha256": { "pyproject.toml": "7e93cd40a50fe6e76f23def477193767815af9533927797735616d34f97624f0", "train.py": "a82a7385c3af87fbddd1f208e868d8ecb05ff4e290309ba3d6feaedac159f170", "upstream-model.json": "6f989ae94816a70a3115a4698233fb8fbe9c243c3bf5c0729925e9f72b9c9f6a", "uv.lock": "cfe0f3676c3e69fba0b5cecb75a6163c23254297b837b4b733821a2fbbd70415" }, "post_run_hardened_train_py_sha256": "899888549826fd974ff2ac918e5ed74f6a13232e3e896e24af94d9db04ca79a6", "post_run_hardened_source_matches_executed_source": false, "post_run_change": "Docstring-only corrections clarified credential reads and whitespace-normalized response comparison without changing training logic." }, "upstream_model": { "model_id": "Qwen/Qwen3-1.7B", "revision": "70d244cc86ccca08cf5af4e1e306ecf908b1ad5e", "publisher": "Qwen", "license": "apache-2.0", "revision_last_modified": "2025-07-26T03:46:32+00:00", "remote_code_enabled": false, "manifest_path": "jobs/identity-smoke/upstream-model.json", "manifest_sha256": "6f989ae94816a70a3115a4698233fb8fbe9c243c3bf5c0729925e9f72b9c9f6a", "weight_sha256": { "model-00001-of-00002.safetensors": "169ad53ec313c3a34b06c0809216e4fc072cce444a5d4ff2b59690d064130ed5", 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authored record", "scenario_split_review": "not applicable: pipeline-only one-record smoke", "train_evaluation_contamination": "deliberate reuse of the training prompt to test memorization", "poisoning_review": "no untrusted source or teacher output enters the record", "exclusions": "none", "thinking_enabled": false, "completion_end_token": "<|im_end|>", "loss_scope": "completion_only", "contains_private_data": false }, "runtime": { "platform": "linux-x86_64", "job_image": "ghcr.io/astral-sh/uv:python3.12-bookworm@sha256:9aa60c50016c0485636ab9a830246a6ef3399aa4a8bab3d17ef4a2358fba2ca7", "python": "3.12.12", "uv": "0.9.30", "c_compiler": "gcc 12.2.0", "hardware_flavor": "a10g-small", "cuda_device": "NVIDIA A10G", "nominal_vram_gb": 24, "unsloth_visible_vram_gib": 22.301, "cuda_runtime": "12.4", "packages": { "accelerate": "1.14.0", "datasets": "4.3.0", "huggingface-hub": "1.26.1", "peft": "0.20.0", "safetensors": "0.8.0", "torch": "2.6.0", "torch_build_reported_by_unsloth": 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"gradient_accumulation_steps": 1, "learning_rate": 0.0002, "maximum_steps": 20, "packing": false, "seed": 3407, "optimizer": "adamw_torch", "scheduler": "constant", "warmup_steps": 0, "weight_decay": 0.0, "gradient_checkpointing": false, "intermediate_checkpoints": false, "automatic_hub_push": false, "lora": { "rank": 8, "alpha": 8, "dropout": 0.0, "bias": "none", "trainable_parameters": 8716288, "reported_total_parameters": 1729291264, "reported_trainable_percent": 0.5, "target_modules": [ "q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj" ] }, "trainer_runtime_seconds": 10.48, "train_samples_per_second": 1.908, "train_steps_per_second": 1.908, "aggregate_training_loss": 1.6867698234826094, "step_metrics": [ {"step": 1, "loss": 8.353, "gradient_norm": 15.02}, {"step": 2, "loss": 7.568, "gradient_norm": 17.74}, {"step": 3, "loss": 5.727, "gradient_norm": 20.98}, {"step": 4, "loss": 3.804, "gradient_norm": 14.56}, {"step": 5, "loss": 2.508, "gradient_norm": 8.88}, {"step": 6, "loss": 1.746, "gradient_norm": 3.956}, {"step": 7, "loss": 1.36, "gradient_norm": 3.239}, {"step": 8, "loss": 1.043, "gradient_norm": 3.352}, {"step": 9, "loss": 0.7308, "gradient_norm": 2.42}, {"step": 10, "loss": 0.4647, "gradient_norm": 2.227}, {"step": 11, "loss": 0.2663, "gradient_norm": 1.625}, {"step": 12, "loss": 0.1188, "gradient_norm": 1.03}, {"step": 13, "loss": 0.03599, "gradient_norm": 0.4706}, {"step": 14, "loss": 0.007015, "gradient_norm": 0.1167}, {"step": 15, "loss": 0.001404, "gradient_norm": 0.03392}, {"step": 16, "loss": 0.0003581, "gradient_norm": 0.009263}, {"step": 17, "loss": 0.000184, "gradient_norm": 0.006619}, {"step": 18, "loss": 0.0002033, "gradient_norm": 0.01128}, {"step": 19, "loss": 0.0002786, "gradient_norm": 0.01877}, {"step": 20, "loss": 0.0003003, "gradient_norm": 0.01961} ] }, "evaluation": { "baseline_response": "**Codegeist** is a free, open-source code editor developed by the **Codegeist Team**. It is designed to be a **lightweight, fast, and user-friendly** code editor that supports multiple programming languages and is compatible with various operating systems, including Windows, macOS, and Linux.\n\n### Key Features of", "adapted_response": "Codegeist is a coding agent.", "exact_match": true, "response_normalization": "leading and trailing whitespace stripped before retention and comparison", "raw_response_preserved": false, "generation_method": "single greedy generation before adaptation and single greedy generation after clean reload", "repeatability_runs": 1, "pytorch_deterministic_algorithms_enabled": false, "adapter_reload_process": "fresh_child_process", "timed_training_script_seconds_after_runtime_validation": 90.806 }, "job_attempts": [ { "id": "6a75eeedda2af92a634eecaa", "url": "https://huggingface.co/jobs/codegeist/6a75eeedda2af92a634eecaa", "created_at": "2026-08-07T14:42:53.825000+00:00", "started_at": "2026-08-07T14:43:03.414000+00:00", "finished_at": "2026-08-07T14:43:54.618000+00:00", "terminal_status": "ERROR", "scheduling_seconds": 9, "running_seconds": 51, "total_seconds": 60, "image": "ghcr.io/astral-sh/uv:python3.12-bookworm-slim@sha256:5d275ca5f0da33c3368ac8fbb85fafabad023b3b8a7cff39a94ac0baecfd9a50", "finding": "The Jobs runtime exposed ACCELERATOR=gpu rather than the documented flavor name a10g-small." }, { "id": "6a75ef753e1f34a7e32bd601", "url": "https://huggingface.co/jobs/codegeist/6a75ef753e1f34a7e32bd601", "created_at": "2026-08-07T14:45:09.389000+00:00", "started_at": "2026-08-07T14:45:17.800000+00:00", "finished_at": "2026-08-07T14:46:58.139000+00:00", "terminal_status": "ERROR", "scheduling_seconds": 8, "running_seconds": 100, "total_seconds": 108, "image": "ghcr.io/astral-sh/uv:python3.12-bookworm-slim@sha256:5d275ca5f0da33c3368ac8fbb85fafabad023b3b8a7cff39a94ac0baecfd9a50", "finding": "Resolver-selected TorchAO 0.18.0 used torch.utils._pytree.register_constant, which is absent from PyTorch 2.6.0." }, { "id": "6a75f06c3e1f34a7e32bd61c", "url": "https://huggingface.co/jobs/codegeist/6a75f06c3e1f34a7e32bd61c", "created_at": "2026-08-07T14:49:16.344000+00:00", "started_at": "2026-08-07T14:49:27.399000+00:00", "finished_at": "2026-08-07T14:50:37.624000+00:00", "terminal_status": "COMPLETED", "scheduling_seconds": 11, "running_seconds": 70, "total_seconds": 81, "image": "ghcr.io/astral-sh/uv:python3.12-bookworm-slim@sha256:5d275ca5f0da33c3368ac8fbb85fafabad023b3b8a7cff39a94ac0baecfd9a50", "finding": "The pinned framework stack imported successfully on CUDA 12.4 and NVIDIA A10G without downloading model weights." }, { "id": "6a75f10b3e1f34a7e32bd631", "url": "https://huggingface.co/jobs/codegeist/6a75f10b3e1f34a7e32bd631", "created_at": "2026-08-07T14:51:55.353000+00:00", "started_at": "2026-08-07T14:52:03.839000+00:00", "finished_at": "2026-08-07T14:53:41.173000+00:00", "terminal_status": "ERROR", "scheduling_seconds": 8, "running_seconds": 97, "total_seconds": 105, "image": "ghcr.io/astral-sh/uv:python3.12-bookworm-slim@sha256:5d275ca5f0da33c3368ac8fbb85fafabad023b3b8a7cff39a94ac0baecfd9a50", "finding": "The model loaded, but Triton could not compile its CUDA driver helper because the slim image contained no C compiler." }, { "id": "6a75f25a3e1f34a7e32bd646", "url": "https://huggingface.co/jobs/codegeist/6a75f25a3e1f34a7e32bd646", "created_at": "2026-08-07T14:57:30.247000+00:00", "started_at": "2026-08-07T14:57:38.813000+00:00", "finished_at": "2026-08-07T14:59:53.242000+00:00", "terminal_status": "COMPLETED", "scheduling_seconds": 8, "running_seconds": 134, "total_seconds": 142, "image": "ghcr.io/astral-sh/uv:python3.12-bookworm@sha256:9aa60c50016c0485636ab9a830246a6ef3399aa4a8bab3d17ef4a2358fba2ca7", "finding": "Training, Safetensors save, fresh-process adapter reload, whitespace-normalized match evaluation, and evidence writing completed." } ], "pre_job_failures": [ { "job_created": false, "compute_cost": 0, "finding": "The first launch request used model=qwen3-1.7b; the dot violated the Jobs tag character policy." }, { "job_created": false, "compute_cost": 0, "finding": "The second launch request fixed the model label but retained a dot in the name, which is also stored as a label." }, { "job_created": false, "compute_cost": 0, "finding": "A later source sync failed locally with ENOSPC in the Hugging Face Xet staging cache. 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"6a75f25a3e1f34a7e32bd646.log": {"size_bytes": 11637, "sha256": "f1f79b48899e24f6a4a2d667ce40166b9ede6a0d7a2c5de91aa87cceb8344479"}, "bucket-info.json": {"size_bytes": 132, "sha256": "76fc077f24605fb1a1e4c84927a46c066daccc88abc21e42d5b8a0ba9c2b2d31"}, "hardware-a10g-small.txt": {"size_bytes": 81, "sha256": "7d23c09ee7611b01840801b003c82c7d0a23f5b8207fb41079a4859d843e49b5"}, "jobs-inspect.json": {"size_bytes": 9374, "sha256": "f240ae186e462b0c8cf7deeeec1dc659193c4bba74fc300a54339d2a25a35948"}, "SHA256SUMS": {"size_bytes": 772, "sha256": "85ea71d1ddcbbf3b9e605081eb8d113cd2e3c5f00e49551c97f6c1ce94bc7c06"} } }, "verification": { "pre_launch_weightless_contract_tests": 13, "pre_launch_weightless_contract_tests_passed": 13, "post_run_hardened_contract_tests": 16, "post_run_hardened_contract_tests_passed": 16, "current_weightless_contract_tests": 30, "current_weightless_contract_tests_passed": 30, "lock_check_passed": true, "inference_lock_check_passed": true, "adapter_hash_check_passed": true, "source_hash_check_passed": true, "source_hash_check_timing": "passed immediately after artifact synchronization, before docstring-only post-run hardening", "secret_scan_passed": true, "private_snapshot_secret_scan_passed": true, "private_snapshot_hash_manifest_passed": true, "upstream_download_hash_check_passed": false, "adapter_format": "safetensors", "pickle_bin_present": false, "clean_process_reload_passed": true, "terminal_job_status": "COMPLETED", "public_gpu_reload_passed": true, "public_all_parameters_and_buffers_on_cuda_passed": true, "public_all_floating_parameters_bfloat16_passed": true, "public_manifest_check_passed": true, "public_anonymous_access_passed": true }, "cost_estimate": { "scope": "five training and compatibility Jobs before publication", "observed_rate_usd_per_hour": 1.0, "observed_rate_usd_per_minute": 0.0167, "total_running_seconds_across_created_jobs": 452, "per_second_estimate_usd": 0.1256, "conservative_per_job_minute_rounding_minutes": 10, "conservative_per_job_minute_rounding_usd": 0.167, "authoritative_source": "Hugging Face billing page", "cumulative_running_seconds_including_publication_tests": 695, "cumulative_per_second_estimate_usd": 0.1931, "cumulative_conservative_whole_minutes": 16, "cumulative_conservative_estimate_usd": 0.2672 }, "known_gaps": [ "The training source was not committed at launch; exact source bytes are anchored by SHA-256 instead of a Git commit containing the implementation.", "run.json does not list TorchAO in its selected runtime package subset; the lock digest and this curated record capture TorchAO 0.13.0.", "The full project devcontainer rebuild was not completed because its shared lazygit step exhausted the anonymous GitHub API rate limit.", "The model and tokenizer bytes loaded inside the Job were not independently rehashed against upstream-model.json after download.", "Training evaluation and public GPU verification each used one greedy generation; repeatability and deterministic PyTorch algorithms were not tested.", "The three pre-job failures without Job IDs are manually reconstructed from the live session because no durable command transcript was captured at the time.", "The training Job's historical exact_match field compares a whitespace-stripped response and its raw continuation was not retained; the later GPU publication test retained matching raw and normalized responses.", "SmolLM3-3B and Qwen3.5-2B remain unpinned and untested.", "The experiment demonstrates one-record memorization only." ] }