Instructions to use suchitg/sae-compression-llama-2-7b-wanda_50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- SAELens
How to use suchitg/sae-compression-llama-2-7b-wanda_50 with SAELens:
# pip install sae-lens from sae_lens import SAE sae, cfg_dict, sparsity = SAE.from_pretrained( release = "RELEASE_ID", # e.g., "gpt2-small-res-jb". See other options in https://github.com/jbloomAus/SAELens/blob/main/sae_lens/pretrained_saes.yaml sae_id = "SAE_ID", # e.g., "blocks.8.hook_resid_pre". Won't always be a hook point ) - Notebooks
- Google Colab
- Kaggle
Upload SAE blocks.0.hook_resid_post
Browse files
blocks.0.hook_resid_post/cfg.json
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{"architecture": "jumprelu", "d_in": 4096, "d_sae": 16384, "dtype": "float32", "device": "cuda:1", "model_name": "llama-2-7b", "hook_name": "blocks.0.hook_resid_post", "hook_layer": 0, "hook_head_index": null, "activation_fn_str": "relu", "activation_fn_kwargs": {}, "apply_b_dec_to_input": false, "finetuning_scaling_factor": false, "sae_lens_training_version": "5.6.1", "prepend_bos": true, "dataset_path": "roneneldan/TinyStories", "dataset_trust_remote_code": true, "context_size": 128, "normalize_activations": "none", "neuronpedia_id": null, "model_from_pretrained_kwargs": {}, "seqpos_slice": [null], "l1_coefficient": 0.001, "lp_norm": 1, "use_ghost_grads": false, "normalize_sae_decoder": false, "noise_scale": 0.0, "decoder_orthogonal_init": false, "init_encoder_as_decoder_transpose": true, "mse_loss_normalization": null, "decoder_heuristic_init": true, "scale_sparsity_penalty_by_decoder_norm": false, "jumprelu_init_threshold": 0.001, "jumprelu_bandwidth": 0.001}
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blocks.0.hook_resid_post/sae_weights.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:86f613b17fefb64ef7fac807be05d34b8b69090b4db417e34786db1d260f58bf
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size 537018768
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