Emender E97 1.3B CLI Agent
A narrow experimental recurrent CLI policy derived from the 513B-token dense E97 base. It is trained for one exact RS-free Pi protocol:
repository objective -> sandboxed direct CLI argv -> typed observation -> grounded submit_answer
This is not a general instruction or coding assistant.
Verified behavior
The immutable checkpoint passed 40/40 held-out real-Pi direct CLI tasks:
| family | result |
|---|---|
| count | 10/10 |
| JSON extraction | 10/10 |
| file read | 10/10 |
| search | 10/10 |
All 40 runs used exactly cli -> submit_answer and passed argv, value, evidence,
grounding, bounded-sequence, completion, and clean-exit checks.
- Source checkpoint SHA-256:
fca9b06c521fb9407cd7bed2d7049f36e9a93948d6cd98899b6ad9b9e4fc6b01 - Base: dense E97 513B authority
- Weight semantics: saved evaluated agent weights
- Tokenizer:
p50k_base - Raw pickle checkpoint: not included
Important protocol dependency
The result depends on the exact system prompt, tool schema, assistant-target normalization, structured-action stopping, recurrent cache semantics, and hash-pinned cwd-only Apptainer sandbox in the Emender repository. Ordinary text generation does not reproduce the agent evaluation.
See configs/pi/e97-cli-tools.ts, scripts/run_e97_sandbox_cli.py, and
docs/E97_DENSE_PI_AGENT_EXECUTION_PLAN.md at commit 2b5d135587cca9d1081dbbdf7b35a8ac2629db5d.
Loading weights
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("spinozans/emender-e97-1.3b-cli-agent")
model = AutoModelForCausalLM.from_pretrained(
"spinozans/emender-e97-1.3b-cli-agent", trust_remote_code=True, dtype="auto"
)
Install the matching model implementation:
pip install "git+https://github.com/spinozans/emender.git@2b5d135587cca9d1081dbbdf7b35a8ac2629db5d"
Limitations and license
The policy has not demonstrated autonomous tool discovery or general software
engineering ability. Its sandbox does not claim arbitrary hostile-code
isolation. No standalone model license has yet been selected; license: other
is intentional.