Feature Extraction
Transformers
PyTorch
qwen3
llama-factory
full
Generated from Trainer
custom_code
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Time-HD-Anonymous/STReasoner-8B-Align", trust_remote_code=True)
model = AutoModel.from_pretrained("Time-HD-Anonymous/STReasoner-8B-Align", trust_remote_code=True)Quick Links
Qwen3-8B-stage1
This model is a fine-tuned version of Qwen/Qwen3-8B on the alignment 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: 1e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 32
- total_train_batch_size: 512
- total_eval_batch_size: 64
- 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: 0.02
- training_steps: 1000
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.4
- Downloads last month
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Time-HD-Anonymous/STReasoner-8B-Align", trust_remote_code=True)