Text Classification
Transformers
ONNX
Safetensors
English
llama
reasoning-detection
small-model
tiny-model
english
34m
text-embeddings-inference
Instructions to use vrishv/thinker-34M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vrishv/thinker-34M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vrishv/thinker-34M")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vrishv/thinker-34M") model = AutoModelForSequenceClassification.from_pretrained("vrishv/thinker-34M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download thinker_info.json from vrishv/thinker-34M: direct link, hf CLI and curl.
- Browser
- Download file 210 Bytes
-
https://huggingface.co/vrishv/thinker-34M/resolve/main/thinker_info.json
- Command line
-
hf download hf://vrishv/thinker-34M/thinker_info.json
-
curl -L -o thinker_info.json https://huggingface.co/vrishv/thinker-34M/resolve/main/thinker_info.json
210 Bytes
| { | |
| "labels": [ | |
| "quick", | |
| "think" | |
| ], | |
| "base": "vrishv/Viveka-34M", | |
| "max_length": 128, | |
| "run": { | |
| "val_accuracy": 0.7070707070707071, | |
| "lr": 0.0001, | |
| "epochs": 4 | |
| }, | |
| "test_accuracy": 0.6804932735426009 | |
| } |