Text Classification
Transformers
Safetensors
English
qwen3
reward
RM
Code
CodeScaler
text-embeddings-inference
Instructions to use LARK-Lab/CodeScaler-1.7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LARK-Lab/CodeScaler-1.7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LARK-Lab/CodeScaler-1.7B")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LARK-Lab/CodeScaler-1.7B") model = AutoModelForSequenceClassification.from_pretrained("LARK-Lab/CodeScaler-1.7B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c4705e2d217b5740c754b683460d29bb9d98369d93ba03d9fc50b5734858ed84
- Size of remote file:
- 3.44 GB
- SHA256:
- 80b4a1dbf4078a8c61d412e74b88a88ca48c198aeac86607b7b4519266765afd
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