Feature Extraction
Transformers
Safetensors
sdar
llama-factory
full
Generated from Trainer
custom_code
Instructions to use autoprogrammer/sdar_4b_trace_sft_openr1math with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autoprogrammer/sdar_4b_trace_sft_openr1math with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="autoprogrammer/sdar_4b_trace_sft_openr1math", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("autoprogrammer/sdar_4b_trace_sft_openr1math", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
fix README.md frontmatter
Browse files
README.md
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---
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library_name: transformers
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license: other
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tags:
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- llama-factory
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- full
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- generated_from_trainer
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model-index:
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- name: sft
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# sft
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This model is a fine-tuned version of SDAR-4B-Chat on an unknown dataset.
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 2
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- eval_batch_size: 8
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 4
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 32
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- total_eval_batch_size: 32
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_ratio: 0.03
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- num_epochs: 3.0
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### Training results
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### Framework versions
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- Transformers 4.52.4
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- Pytorch 2.8.0a0+34c6371d24.nv25.08
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- Datasets 3.6.0
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- Tokenizers 0.21.1
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