Instructions to use abugoot-primeintellect/bioreasoning-qwen3-30ba3b-sft-20260218-ckpt-1500 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use abugoot-primeintellect/bioreasoning-qwen3-30ba3b-sft-20260218-ckpt-1500 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-30B-A3B") model = PeftModel.from_pretrained(base_model, "abugoot-primeintellect/bioreasoning-qwen3-30ba3b-sft-20260218-ckpt-1500") - Notebooks
- Google Colab
- Kaggle
bioreasoning-qwen3-30ba3b-sft-20260218-ckpt-1500
LoRA adapter checkpoint from supervised fine-tuning on the bioreasoning dataset.
Training Details
- Base model: Qwen/Qwen3-30B-A3B
- Method: LoRA (rank 32)
- Training platform: Tinker (ThinkingMachines)
- Dataset: abugoot-primeintellect/bioreasoning_v0211_prime_sft (~193k train examples)
- Hyperparameters: batch_size=128, lr=2e-5 (linear decay), weight_decay=0.01, seq_len=8192
- Epochs: 3
- Checkpoint: step 001500 (epoch 0, batch 1500)
- Loss masking: Last assistant message only
All Checkpoints
| Step | Epoch | HF Repo |
|---|---|---|
| 1500 | ~1 | bioreasoning-qwen3-30ba3b-sft-20260218-ckpt-1500 |
| 3000 | ~2 | bioreasoning-qwen3-30ba3b-sft-20260218-ckpt-3000 |
| final | 3 | bioreasoning-qwen3-30ba3b-sft-20260218-ckpt-final |
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