Instructions to use agurung/cobalt-v2-rft-mixed-12 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use agurung/cobalt-v2-rft-mixed-12 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B-Instruct-2507") model = PeftModel.from_pretrained(base_model, "agurung/cobalt-v2-rft-mixed-12") - Notebooks
- Google Colab
- Kaggle
cobalt-v2-rft-mixed-12
LoRA adapters for rft_mixed_12_cobalt_v2 (cobalt_v2). Base Qwen/Qwen3-4B-Instruct-2507.
main = best-by-val-loss (checkpoint-36). Other checkpoints are git revisions.
from peft import PeftModel
from transformers import AutoModelForCausalLM
m = AutoModelForCausalLM.from_pretrained('Qwen/Qwen3-4B-Instruct-2507')
m = PeftModel.from_pretrained(m, 'agurung/cobalt-v2-rft-mixed-12') # best-val
m = PeftModel.from_pretrained(m, 'agurung/cobalt-v2-rft-mixed-12', revision='checkpoint-N') # any checkpoint
vLLM: --enable-lora --lora-modules cobalt-v2-rft-mixed-12=agurung/cobalt-v2-rft-mixed-12 (add @revision for a checkpoint).
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Qwen/Qwen3-4B-Instruct-2507