Instructions to use msho/dspv1_sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use msho/dspv1_sft with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Prover-V1") model = PeftModel.from_pretrained(base_model, "msho/dspv1_sft") - Notebooks
- Google Colab
- Kaggle
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Download README.md from msho/dspv1_sft: direct link, hf CLI and curl.
- Browser
- Download file 1.24 kB
-
https://huggingface.co/msho/dspv1_sft/resolve/main/README.md
- Command line
-
hf download hf://msho/dspv1_sft/README.md
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curl -L -o README.md https://huggingface.co/msho/dspv1_sft/resolve/main/README.md
1.24 kB
metadata
base_model: deepseek-ai/DeepSeek-Prover-V1
datasets:
- generator
library_name: peft
license: other
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: dspv1_sft
results: []
dspv1_sft
This model is a fine-tuned version of deepseek-ai/DeepSeek-Prover-V1 on the generator 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: 0.0002
- train_batch_size: 3
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
Training results
Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1