Feature Extraction
Transformers
PyTorch
qwen3
llama-factory
full
Generated from Trainer
custom_code
text-embeddings-inference
Instructions to use Time-HD-Anonymous/STReasoner-8B-CoT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Time-HD-Anonymous/STReasoner-8B-CoT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Time-HD-Anonymous/STReasoner-8B-CoT", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Time-HD-Anonymous/STReasoner-8B-CoT", trust_remote_code=True) model = AutoModel.from_pretrained("Time-HD-Anonymous/STReasoner-8B-CoT", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download processor_config.json from Time-HD-Anonymous/STReasoner-8B-CoT: direct link, hf CLI and curl.
- Browser
- Download file 125 Bytes
-
https://huggingface.co/Time-HD-Anonymous/STReasoner-8B-CoT/resolve/main/processor_config.json
- Command line
-
hf download hf://Time-HD-Anonymous/STReasoner-8B-CoT/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/Time-HD-Anonymous/STReasoner-8B-CoT/resolve/main/processor_config.json
125 Bytes
| { | |
| "auto_map": { | |
| "AutoProcessor": "processing_qwen3_ts.Qwen3TSProcessor" | |
| }, | |
| "processor_class": "Qwen3TSProcessor" | |
| } | |