Instructions to use tttx/models-3k-unsolved-priority-022525-step3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use tttx/models-3k-unsolved-priority-022525-step3 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-32B") model = PeftModel.from_pretrained(base_model, "tttx/models-3k-unsolved-priority-022525-step3") - Notebooks
- Google Colab
- Kaggle
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Download README.md from tttx/models-3k-unsolved-priority-022525-step3: direct link, hf CLI and curl.
- Browser
- Download file 1.51 kB
-
https://huggingface.co/tttx/models-3k-unsolved-priority-022525-step3/resolve/main/README.md
- Command line
-
hf download hf://tttx/models-3k-unsolved-priority-022525-step3/README.md
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curl -L -o README.md https://huggingface.co/tttx/models-3k-unsolved-priority-022525-step3/resolve/main/README.md
1.51 kB
metadata
library_name: peft
license: mit
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
datasets:
- tttx/3k-unsolved-priority-022525-step3-collated
model-index:
- name: models-3k-unsolved-priority-022525-step3
results: []
models-3k-unsolved-priority-022525-step3
This model is a fine-tuned version of tttx/sft-32b-020925-19k-5ep on the tttx/3k-unsolved-priority-022525-step3-collated 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: 8e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 69213
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 8
- total_eval_batch_size: 8
- optimizer: Use 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: 3
Training results
Framework versions
- PEFT 0.13.2
- Transformers 4.47.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3