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Schrieffer
/
Llama-SARM-4B-PostSAEPretrain

Feature Extraction
Transformers
Safetensors
llama
text-classification
reward-model
rlhf
sparse-autoencoder
interpretability
custom_code
text-embeddings-inference
Model card Files Files and versions
xet
Community
1

Instructions to use Schrieffer/Llama-SARM-4B-PostSAEPretrain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Schrieffer/Llama-SARM-4B-PostSAEPretrain with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="Schrieffer/Llama-SARM-4B-PostSAEPretrain", trust_remote_code=True)
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("Schrieffer/Llama-SARM-4B-PostSAEPretrain", trust_remote_code=True)
    model = AutoModelForSequenceClassification.from_pretrained("Schrieffer/Llama-SARM-4B-PostSAEPretrain", trust_remote_code=True)
  • Notebooks
  • Google Colab
  • Kaggle
Llama-SARM-4B-PostSAEPretrain
9.11 GB
Ctrl+K
Ctrl+K
  • 4 contributors
History: 16 commits
zhangshuyi.0109
update news & citation
5c70410 6 months ago
  • .gitattributes
    1.57 kB
    Upload tokenizer 9 months ago
  • README.md
    2.09 kB
    update news & citation 6 months ago
  • config.json
    1.23 kB
    init 8 months ago
  • model-00001-of-00002.safetensors
    4.98 GB
    xet
    Upload LlamaSARM 9 months ago
  • model-00002-of-00002.safetensors
    4.13 GB
    xet
    Upload LlamaSARM 9 months ago
  • model.safetensors.index.json
    12.3 kB
    Upload LlamaSARM 9 months ago
  • modeling_sarm_llama.py
    23.7 kB
    init 8 months ago
  • special_tokens_map.json
    296 Bytes
    Upload tokenizer 8 months ago
  • tokenizer.json
    9.09 MB
    xet
    Upload tokenizer 8 months ago
  • tokenizer_config.json
    55.4 kB
    Upload tokenizer 8 months ago