Instructions to use throsturx/bihmoe-poc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use throsturx/bihmoe-poc with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("throsturx/bihmoe-poc", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| from __future__ import annotations | |
| import argparse, subprocess, copy, yaml, time, os, sys | |
| def main(): | |
| ap = argparse.ArgumentParser() | |
| ap.add_argument("--config", required=True) | |
| ap.add_argument("--seeds", nargs="+", type=int, required=True) | |
| ap.add_argument("--steps", type=int, default=None) | |
| ap.add_argument("--eval_every", type=int, default=None) | |
| ap.add_argument("--tag_suffix", type=str, default="") | |
| args = ap.parse_args() | |
| base = yaml.safe_load(open(args.config, "r", encoding="utf-8")) | |
| for seed in args.seeds: | |
| cfg = copy.deepcopy(base) | |
| cfg["run"]["seed"] = int(seed) | |
| if args.tag_suffix: | |
| cfg["run"]["tag"] = f"{cfg['run']['tag']}_{args.tag_suffix}" | |
| if args.steps is not None: | |
| cfg["train"]["steps"] = int(args.steps) | |
| if args.eval_every is not None: | |
| cfg["train"]["eval_every"] = int(args.eval_every) | |
| tmp = f"/tmp/bihmoe_sweep_{int(time.time())}_seed{seed}.yaml" | |
| with open(tmp, "w", encoding="utf-8") as f: | |
| yaml.safe_dump(cfg, f, sort_keys=False) | |
| print(f"\n=== RUN seed={seed} cfg={tmp} ===") | |
| rc = subprocess.call(["uv", "run", "python", "scripts/train_poc_side_by_side.py", "--config", tmp]) | |
| if rc != 0: | |
| print(f"ERROR: run failed with code {rc} for seed {seed}") | |
| sys.exit(rc) | |
| if __name__ == "__main__": | |
| main() | |