File size: 3,105 Bytes
dfb775d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 | """Auto model-card generation from a completed run.
Renders a HuggingFace-flavored `README.md` from the run's `XTrainConfig`
plus the eval JSON. Uses Jinja2 if installed; falls back to stdlib
`string.Template` so the base install (`uv sync` no extras) still works.
"""
from __future__ import annotations
import importlib.util
import json
from pathlib import Path
from string import Template
from typing import Any
_FALLBACK_TEMPLATE = Template(
"""---
license: apache-2.0
base_model: ${base_model}
tags:
- mindxtrain
- amd-mi300x
- fine-tuned
---
# ${run_name}
Fine-tuned with [mindxtrain](https://github.com/mindx/mindxtrain) on AMD MI300X.
- **Base model:** ${base_model}
- **Trainer:** ${backend}
- **Run id:** ${run_name}
## Evaluation
```json
${eval_json}
```
## Provenance
- BLAKE3 hashes of config / dataset / checkpoint / eval are recorded in the
accompanying `manifest.json`.
- ROCm: 7.2.1 / gfx942
"""
)
_JINJA_TEMPLATE = """---
license: apache-2.0
base_model: {{ base_model }}
tags:
- mindxtrain
- amd-mi300x
- fine-tuned
---
# {{ run_name }}
Fine-tuned with [mindxtrain](https://github.com/mindx/mindxtrain) on AMD MI300X.
- **Base model:** {{ base_model }}
- **Trainer:** {{ backend }}
- **Run id:** {{ run_name }}
{% if hyperparams %}## Hyperparameters
{% for k, v in hyperparams.items() %}- **{{ k }}**: {{ v }}
{% endfor %}{% endif %}
## Evaluation
```json
{{ eval_json }}
```
## Provenance
- BLAKE3 hashes recorded in `manifest.json`.
- ROCm 7.2.1 / gfx942 (MI300X).
"""
def render_card(cfg: Any, eval_json: Path | None, out_path: Path) -> Path:
"""Write a `README.md` model card; return the path."""
base_model = getattr(getattr(cfg, "model", None), "name", "unknown")
run_name = getattr(getattr(cfg, "meta", None), "run_name", "run")
backend = getattr(getattr(cfg, "train", None), "backend", "axolotl")
eval_payload = "{}"
if eval_json is not None and Path(eval_json).exists():
try:
eval_payload = json.dumps(json.loads(Path(eval_json).read_text()), indent=2)
except (OSError, json.JSONDecodeError):
pass
out_path = Path(out_path)
out_path.parent.mkdir(parents=True, exist_ok=True)
if importlib.util.find_spec("jinja2") is not None:
from jinja2 import Template as JinjaTemplate
hyperparams = {
"learning_rate": getattr(getattr(cfg.train, "optim", None), "learning_rate", None),
"epochs": getattr(cfg.train, "num_epochs", None),
"micro_batch_size": getattr(cfg.train, "micro_batch_size", None),
}
rendered = JinjaTemplate(_JINJA_TEMPLATE).render(
base_model=base_model,
run_name=run_name,
backend=backend,
hyperparams=hyperparams,
eval_json=eval_payload,
)
else:
rendered = _FALLBACK_TEMPLATE.substitute(
base_model=base_model,
run_name=run_name,
backend=backend,
eval_json=eval_payload,
)
out_path.write_text(rendered)
return out_path
__all__ = ["render_card"]
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