Instructions to use kavyanshshakya/strathos-qwen17b-sft-traced with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use kavyanshshakya/strathos-qwen17b-sft-traced with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-1.7B") model = PeftModel.from_pretrained(base_model, "kavyanshshakya/strathos-qwen17b-sft-traced") - Notebooks
- Google Colab
- Kaggle
strathos-qwen17b-sft-traced
Re-training trace of the Strathos V2-PLUS adapter with full wandb experimental tracking enabled.
Relationship to V2-PLUS (deployed model)
The deployed Strathos model is kavyanshshakya/strathos-qwen17b-sft (V2-PLUS). It was trained on Stage 2 grounded discrimination data (200 examples, lr=1e-4, 5 epochs) and is the model used in the live env Space.
This adapter is a re-training trace produced specifically for the Meta PyTorch OpenEnv Hackathon Grand Finale to satisfy the hackathon's experimental-tracking requirement. It starts from V2-PLUS as a checkpoint and continues training for 3 epochs on the regenerated 200-example dataset, with wandb tracking enabled throughout.
Training details
- Base model: Qwen/Qwen3-1.7B
- Starting checkpoint:
kavyanshshakya/strathos-qwen17b-sft(V2-PLUS) - LoRA: r=16, alpha=16, q/k/v/o projections (~6.4M trainable params, 0.32%)
- Optimizer: AdamW, lr=1e-4, cosine schedule, warmup_ratio=0.05
- Batch size: 4, grad accum 2, 3 epochs
- Total steps: 75
- Final training loss: 0.69 (started from V2-PLUS checkpoint, hence faster convergence)
- Hardware: Colab Pro A100
Use case
This adapter is for verifiable training reproducibility. For deployment, use V2-PLUS (kavyanshshakya/strathos-qwen17b-sft).
Resources
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