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* feat(gateway): skill-aware slash commands, paginated /commands, Telegram 100-cap Map active skills to Telegram's slash command menu so users can discover and invoke skills directly. Three changes: 1. Telegram menu now includes active skill commands alongside built-in commands, capped at 100 entries (Telegram Bot API limit). Overflow commands remain callable but hidden from the picker. Logged at startup when cap is hit. 2. New /commands [page] gateway command for paginated browsing of all commands + skills. /help now shows first 10 skill commands and points to /commands for the full list. 3. When a user types a slash command that matches a disabled or uninstalled skill, they get actionable guidance: - Disabled: 'Enable it with: hermes skills config' - Optional (not installed): 'Install with: hermes skills install official/<path>' Built on ideas from PR #3921 by @kshitijk4poor. * chore: move 21 niche skills to optional-skills Move specialized/niche skills from built-in (skills/) to optional (optional-skills/) to reduce the default skill count. Users can install them with: hermes skills install official/<category>/<name> Moved skills (21): - mlops: accelerate, chroma, faiss, flash-attention, hermes-atropos-environments, huggingface-tokenizers, instructor, lambda-labs, llava, nemo-curator, pinecone, pytorch-lightning, qdrant, saelens, simpo, slime, tensorrt-llm, torchtitan - research: domain-intel, duckduckgo-search - devops: inference-sh cli Built-in skills: 96 → 75 Optional skills: 22 → 43 * fix: only include repo built-in skills in Telegram menu, not user-installed User-installed skills (from hub or manually added) stay accessible via /skills and by typing the command directly, but don't get registered in the Telegram slash command picker. Only skills whose SKILL.md is under the repo's skills/ directory are included in the menu. This keeps the Telegram menu focused on the curated built-in set while user-installed skills remain discoverable through /skills and /commands.
333 lines
6.8 KiB
Markdown
333 lines
6.8 KiB
Markdown
# SAELens API Reference
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## SAE Class
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The core class representing a Sparse Autoencoder.
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### Loading Pre-trained SAEs
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```python
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from sae_lens import SAE
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# From official releases
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sae, cfg_dict, sparsity = SAE.from_pretrained(
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release="gpt2-small-res-jb",
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sae_id="blocks.8.hook_resid_pre",
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device="cuda"
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)
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# From HuggingFace
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sae, cfg_dict, sparsity = SAE.from_pretrained(
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release="username/repo-name",
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sae_id="path/to/sae",
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device="cuda"
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)
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# From local disk
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sae = SAE.load_from_disk("/path/to/sae", device="cuda")
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```
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### SAE Attributes
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| Attribute | Shape | Description |
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|-----------|-------|-------------|
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| `W_enc` | [d_in, d_sae] | Encoder weights |
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| `W_dec` | [d_sae, d_in] | Decoder weights |
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| `b_enc` | [d_sae] | Encoder bias |
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| `b_dec` | [d_in] | Decoder bias |
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| `cfg` | SAEConfig | Configuration object |
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### Core Methods
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#### encode()
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```python
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# Encode activations to sparse features
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features = sae.encode(activations)
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# Input: [batch, pos, d_in]
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# Output: [batch, pos, d_sae]
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```
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#### decode()
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```python
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# Reconstruct activations from features
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reconstructed = sae.decode(features)
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# Input: [batch, pos, d_sae]
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# Output: [batch, pos, d_in]
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```
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#### forward()
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```python
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# Full forward pass (encode + decode)
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reconstructed = sae(activations)
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# Returns reconstructed activations
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```
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#### save_model()
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```python
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sae.save_model("/path/to/save")
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```
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---
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## SAEConfig
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Configuration class for SAE architecture and training context.
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### Key Parameters
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| Parameter | Type | Description |
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|-----------|------|-------------|
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| `d_in` | int | Input dimension (model's d_model) |
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| `d_sae` | int | SAE hidden dimension |
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| `architecture` | str | "standard", "gated", "jumprelu", "topk" |
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| `activation_fn_str` | str | Activation function name |
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| `model_name` | str | Source model name |
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| `hook_name` | str | Hook point in model |
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| `normalize_activations` | str | Normalization method |
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| `dtype` | str | Data type |
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| `device` | str | Device |
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### Accessing Config
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```python
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print(sae.cfg.d_in) # 768 for GPT-2 small
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print(sae.cfg.d_sae) # e.g., 24576 (32x expansion)
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print(sae.cfg.hook_name) # e.g., "blocks.8.hook_resid_pre"
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```
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---
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## LanguageModelSAERunnerConfig
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Comprehensive configuration for training SAEs.
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### Example Configuration
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```python
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from sae_lens import LanguageModelSAERunnerConfig
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cfg = LanguageModelSAERunnerConfig(
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# Model and hook
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model_name="gpt2-small",
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hook_name="blocks.8.hook_resid_pre",
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hook_layer=8,
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d_in=768,
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# SAE architecture
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architecture="standard", # "standard", "gated", "jumprelu", "topk"
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d_sae=768 * 8, # Expansion factor
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activation_fn="relu",
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# Training hyperparameters
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lr=4e-4,
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l1_coefficient=8e-5,
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lp_norm=1.0,
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lr_scheduler_name="constant",
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lr_warm_up_steps=500,
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# Sparsity control
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l1_warm_up_steps=1000,
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use_ghost_grads=True,
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feature_sampling_window=1000,
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dead_feature_window=5000,
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dead_feature_threshold=1e-8,
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# Data
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dataset_path="monology/pile-uncopyrighted",
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streaming=True,
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context_size=128,
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# Batch sizes
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train_batch_size_tokens=4096,
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store_batch_size_prompts=16,
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n_batches_in_buffer=64,
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# Training duration
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training_tokens=100_000_000,
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# Logging
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log_to_wandb=True,
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wandb_project="sae-training",
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wandb_log_frequency=100,
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# Checkpointing
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checkpoint_path="checkpoints",
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n_checkpoints=5,
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# Hardware
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device="cuda",
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dtype="float32",
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)
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```
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### Key Parameters Explained
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#### Architecture Parameters
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| Parameter | Description |
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|-----------|-------------|
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| `architecture` | SAE type: "standard", "gated", "jumprelu", "topk" |
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| `d_sae` | Hidden dimension (or use `expansion_factor`) |
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| `expansion_factor` | Alternative to d_sae: d_sae = d_in × expansion_factor |
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| `activation_fn` | "relu", "topk", etc. |
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| `activation_fn_kwargs` | Dict for activation params (e.g., {"k": 50} for topk) |
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#### Sparsity Parameters
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| Parameter | Description |
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|-----------|-------------|
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| `l1_coefficient` | L1 penalty weight (higher = sparser) |
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| `l1_warm_up_steps` | Steps to ramp up L1 penalty |
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| `use_ghost_grads` | Apply gradients to dead features |
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| `dead_feature_threshold` | Activation threshold for "dead" |
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| `dead_feature_window` | Steps to check for dead features |
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#### Learning Rate Parameters
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| Parameter | Description |
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|-----------|-------------|
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| `lr` | Base learning rate |
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| `lr_scheduler_name` | "constant", "cosineannealing", etc. |
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| `lr_warm_up_steps` | LR warmup steps |
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| `lr_decay_steps` | Steps for LR decay |
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---
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## SAETrainingRunner
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Main class for executing training.
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### Basic Training
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```python
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from sae_lens import SAETrainingRunner, LanguageModelSAERunnerConfig
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cfg = LanguageModelSAERunnerConfig(...)
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runner = SAETrainingRunner(cfg)
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sae = runner.run()
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```
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### Accessing Training Metrics
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```python
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# During training, metrics logged to W&B include:
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# - l0: Average active features
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# - ce_loss_score: Cross-entropy recovery
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# - mse_loss: Reconstruction loss
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# - l1_loss: Sparsity loss
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# - dead_features: Count of dead features
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```
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---
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## ActivationsStore
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Manages activation collection and batching.
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### Basic Usage
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```python
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from sae_lens import ActivationsStore
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store = ActivationsStore.from_sae(
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model=model,
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sae=sae,
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store_batch_size_prompts=8,
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train_batch_size_tokens=4096,
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n_batches_in_buffer=32,
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device="cuda",
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)
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# Get batch of activations
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activations = store.get_batch_tokens()
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```
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---
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## HookedSAETransformer
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Integration of SAEs with TransformerLens models.
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### Basic Usage
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```python
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from sae_lens import HookedSAETransformer
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# Load model with SAE
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model = HookedSAETransformer.from_pretrained("gpt2-small")
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model.add_sae(sae)
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# Run with SAE in the loop
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output = model.run_with_saes(tokens, saes=[sae])
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# Cache with SAE activations
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output, cache = model.run_with_cache_with_saes(tokens, saes=[sae])
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```
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---
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## SAE Architectures
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### Standard (ReLU + L1)
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```python
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cfg = LanguageModelSAERunnerConfig(
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architecture="standard",
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activation_fn="relu",
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l1_coefficient=8e-5,
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)
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```
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### Gated
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```python
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cfg = LanguageModelSAERunnerConfig(
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architecture="gated",
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)
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```
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### TopK
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```python
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cfg = LanguageModelSAERunnerConfig(
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architecture="topk",
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activation_fn="topk",
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activation_fn_kwargs={"k": 50}, # Exactly 50 active features
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)
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```
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### JumpReLU (State-of-the-art)
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```python
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cfg = LanguageModelSAERunnerConfig(
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architecture="jumprelu",
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)
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```
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---
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## Utility Functions
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### Upload to HuggingFace
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```python
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from sae_lens import upload_saes_to_huggingface
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upload_saes_to_huggingface(
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saes=[sae],
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repo_id="username/my-saes",
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token="hf_token",
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)
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```
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### Neuronpedia Integration
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```python
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# Features can be viewed on Neuronpedia
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# URL format: neuronpedia.org/{model}/{layer}-{sae_type}/{feature_id}
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# Example: neuronpedia.org/gpt2-small/8-res-jb/1234
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```
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