Feature Extraction
Transformers
Safetensors
English
hrm_text
text-generation
hrm
hrm-text
hierarchical-reasoning
prefix-lm
text-embeddings
retrieval
custom_code
bright
Eval Results (legacy)
Instructions to use viventhraa96/HRM-Embed-0.6b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use viventhraa96/HRM-Embed-0.6b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="viventhraa96/HRM-Embed-0.6b", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("viventhraa96/HRM-Embed-0.6b", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("viventhraa96/HRM-Embed-0.6b", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
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