mirror of
https://github.com/NousResearch/hermes-agent.git
synced 2026-04-25 00:51:20 +00:00
5ceed021dc
* 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.
65 lines
3 KiB
Markdown
65 lines
3 KiB
Markdown
# Atropos BaseEnv Reference
|
|
|
|
Source: `atroposlib/envs/base.py` (~2124 lines)
|
|
|
|
## Abstract Methods (MUST implement)
|
|
|
|
| Method | Signature | Description |
|
|
|--------|-----------|-------------|
|
|
| `get_next_item()` | `async def get_next_item(self) -> Item` | Return next item for trajectory. Return None to pause. |
|
|
| `evaluate()` | `async def evaluate(self, *args, **kwargs)` | Called every steps_per_eval steps. |
|
|
| `setup()` | `async def setup(self)` | Called once at start. Load datasets, init models. |
|
|
| `collect_trajectory()` | `async def collect_trajectory(self, item) -> Tuple[Optional[ScoredDataItem], List[Item]]` | Single rollout. Or override collect_trajectories instead. |
|
|
|
|
## Overridable Methods
|
|
|
|
| Method | Default Behavior | Override When |
|
|
|--------|-----------------|---------------|
|
|
| `collect_trajectories()` | Runs collect_trajectory group_size times in parallel | Batch generation, MCTS, coupled rollouts |
|
|
| `wandb_log()` | Logs completion lengths, rollout table, perf stats | Add custom metrics (always call super) |
|
|
| `config_init()` | Returns (env_config_cls(), ServerBaseline()) | Custom defaults + server configs |
|
|
| `postprocess_histories()` | Passthrough | Final processing before sending to trainer |
|
|
| `save_checkpoint()` | Saves JSON to checkpoint_dir | Custom serialization |
|
|
| `cleanup()` | No-op | Release resources after each rollout |
|
|
|
|
## ScoredDataGroup Structure
|
|
|
|
```python
|
|
ScoredDataGroup = TypedDict with:
|
|
tokens: List[List[int]] # Token IDs per rollout
|
|
masks: List[List[int]] # -100=prompt, token_id=completion
|
|
scores: List[float] # Score per rollout
|
|
advantages: Optional[...] # Per-token advantages
|
|
ref_logprobs: Optional[...] # Reference model logprobs
|
|
messages: Optional[...] # OpenAI-format messages
|
|
inference_logprobs: Optional[...] # Inference logprobs
|
|
```
|
|
|
|
## BaseEnvConfig Key Fields
|
|
|
|
| Field | Default | Description |
|
|
|-------|---------|-------------|
|
|
| `group_size` | 4 | Responses grouped for scoring |
|
|
| `steps_per_eval` | 100 | Steps between evaluations |
|
|
| `max_token_length` | 2048 | Max token length for generations |
|
|
| `total_steps` | 1000 | Total training steps |
|
|
| `use_wandb` | True | Enable wandb logging |
|
|
| `tokenizer_name` | DeepHermes-3 | Tokenizer for token encoding |
|
|
| `ensure_scores_are_not_same` | True | Skip groups with identical scores |
|
|
| `worker_timeout` | 600 | Task timeout seconds |
|
|
|
|
## Data Flow
|
|
|
|
```
|
|
env_manager() → add_train_workers() → handle_env()
|
|
→ collect_trajectories() → postprocess_histories()
|
|
→ handle_send_to_api() → training server
|
|
```
|
|
|
|
## Atropos Environment Statistics (82 environments analyzed)
|
|
|
|
- 95% implement setup, collect_trajectories, evaluate, get_next_item
|
|
- 76% override wandb_log
|
|
- 54% have custom config class
|
|
- Most use collect_trajectories (plural), not collect_trajectory (singular)
|
|
- Common reward patterns: LLM-judge (~40), regex-extract (~35), code-exec (~12)
|