Hardtests Sft Qwen3.5 27b R16 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).
Modellquelle
- Art
- LoRA
- Dateigröße
- 223 MB
- Basismodell
- Qwen/Qwen3.5-27B
- Version
- vadapter_model
- Aktualisiert
- 26. Sept. 2026
- Downloads
- 8
- Quelle
- Hugging Face
Quellenbeschreibung
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 = 1.1249112845990064)
- 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-r16-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")
Quellen
1 Quelle
Quellen
1 QuelleVerifiziert 7. Sept.
Modellartefakte
1 Artefakt
Modellartefakte
1 Artefaktadapter_model.safetensors
safetensors · 223 MB · SHA-256 21a9633d8e0c…a95e · Hugging Face
Download fileQuellenauszüge
2 Auszüge
Quellenauszüge
2 Auszüge--- 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 = 1.1249112845990064) - **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-r16-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: 8 downloads · 0 likes · Library peft · Repo ZhuofengLi/hardtests-sft-qwen3.5-27b-r16-epoch1
