Hardtests Sft Qwen3.5 27b Epoch1
LoRA adapter from supervised fine-tuning of Qwen/Qwen3.5-27B on the ZhuofengLi/hardtests-sft-qwen3.5-27b dataset (reward=1 competitive-programming trajectories with reasoning).
Model source
- Kind
- LoRA
- File size
- 111 MB
- Base model
- Qwen/Qwen3.5-27B
- Version
- vadapter_model
- Updated
- Sep 22, 2026
- Downloads
- 4
- Source
- Hugging Face
Source description
HardTests SFT — Qwen3.5-27B LoRA (epoch 1)
LoRA adapter from supervised fine-tuning of Qwen/Qwen3.5-27B on the
ZhuofengLi/hardtests-sft-qwen3.5-27b
dataset (reward=1 competitive-programming trajectories with reasoning).
- Checkpoint: step 100 (recorded epoch = 1.1246458923512748)
- LoRA: r=8, alpha=32 (see
adapter_config.json) - Contents: adapter weights + configs only (no optimizer state)
Merge into the base model
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-27B", torch_dtype="bfloat16", device_map="auto")
model = PeftModel.from_pretrained(base, "ZhuofengLi/hardtests-sft-qwen3.5-27b-epoch1")
model = model.merge_and_unload()
model.save_pretrained("./qwen3.5-27b-hardtests-sft-epoch1-merged")
AutoTokenizer.from_pretrained("Qwen/Qwen3.5-27B").save_pretrained("./qwen3.5-27b-hardtests-sft-epoch1-merged")
Sources
1 source
Sources
1 sourceVerified Sep 22
Model artifacts
1 artifact
Model artifacts
1 artifactSource excerpts
2 excerpts
Source excerpts
2 excerpts--- base_model: Qwen/Qwen3.5-27B library_name: peft tags: - lora - sft - qwen3.5 - hardtests - competitive-programming --- # HardTests SFT — Qwen3.5-27B LoRA (epoch 1) LoRA adapter from supervised fine-tuning of `Qwen/Qwen3.5-27B` on the [ZhuofengLi/hardtests-sft-qwen3.5-27b](https://huggingface.co/datasets/ZhuofengLi/hardtests-sft-qwen3.5-27b) dataset (reward=1 competitive-programming trajectories with reasoning). - **Checkpoint**: step 100 (recorded epoch = 1.1246458923512748) - **LoRA**: r=8, alpha=32 (see `adapter_config.json`) - **Contents**: adapter weights + configs only (no optimizer state) ## Merge into the base model ```python from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-27B", torch_dtype="bfloat16", device_map="auto") model = PeftModel.from_pretrained(base, "ZhuofengLi/hardtests-sft-qwen3.5-27b-epoch1") model = model.merge_and_unload() model.save_pretrained("./qwen3.5-27b-hardtests-sft-epoch1-merged") AutoTokenizer.from_pretrained("Qwen/Qwen3.5-27B").save_pretrained("./qwen3.5-27b-hardtests-sft-epoch1-merged") ```
Source context: 4 downloads · 0 likes · Library peft · Repo ZhuofengLi/hardtests-sft-qwen3.5-27b-epoch1
