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dev-store
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sc-dev-p002

PEFT
Safetensors
llama-factory
lora
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xet
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PEFT library
# Gated model: Login with a HF token with gated access permission
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from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-7B")
model = PeftModel.from_pretrained(base_model, "dev-store/sc-dev-p002")

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lora+dpo+sc_preference_v2

This model is a fine-tuned version of deepseek-ai/DeepSeek-R1-Distill-Qwen-7B on the sc_preference_v2 dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-06
  • train_batch_size: 1
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 4
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 32
  • total_eval_batch_size: 32
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 2.0

Training results

Framework versions

  • PEFT 0.12.0
  • Transformers 4.49.0
  • Pytorch 2.6.0+cu124
  • Datasets 3.2.0
  • Tokenizers 0.21.0
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