ModelPublicadoExterno

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).

Fuentehuggingface.co/ZhuofengLi/hardtests-sft-qwen3.5-27b-r16-epoch1/tree/069fc1a51938cd2e599c6cc9b84d559cb4d89c1f

TipoLoRAModelo baseQwen/Qwen3.5-27BVersiónadapter_modelCcalidad

Fuente del modelo

Tipo: LoRA
Tamaño del archivo: 223 MB
Modelo base: Qwen/Qwen3.5-27B
Versión: vadapter_model
Actualizado: 26 sept 2026
Descargas: 8
Fuente: Hugging Face

Descripción de la fuente

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")

Fuentes

1 fuente

Hugging FacePrincipalVersión 069fc1a51938cd2e599c6cc9b84d559cb4d89c1f

huggingface.co/ZhuofengLi/hardtests-sft-qwen3.5-27b-r16-epoch1/tree/069fc1a51938cd2e599c6cc9b84d559cb4d89c1f

Verificado 7 sept

Artefactos del modelo

1 artefacto

adapter_model.safetensors

safetensors · 223 MB · SHA-256 21a9633d8e0c…a95e · Hugging Face

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Extractos de fuentes

2 extractos

Readme7 septhuggingface.co/ZhuofengLi/hardtests-sft-qwen3.5-27b-r16-epoch1

--- 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") ```

Note7 septhuggingface.co/ZhuofengLi/hardtests-sft-qwen3.5-27b-r16-epoch1

Source context: 8 downloads · 0 likes · Library peft · Repo ZhuofengLi/hardtests-sft-qwen3.5-27b-r16-epoch1