recall-honcho-8b — a local Honcho deriver (explicit conclusion extraction)
A Qwen/Qwen3-8B fine-tune specialised for the explicit conclusion-derivation step of
Honcho: given a target peer and their chat turns,
emit atomic, self-contained, correctly-attributed facts as Honcho-schema JSON. Independent,
self-hosted re-creation of the role Plastic Labs' (closed) Neuromancer XR plays in production
Honcho. Not affiliated with Plastic Labs.
Results
Held-out validation (298 examples, 4% split), bf16 LoRA, 2 epochs:
| Metric | Value |
|---|---|
| Eval loss | 0.201 |
| Eval token accuracy | 93.8% |
| Train loss (final) | 0.197 |
| Loss curve | 2.03 → 0.13 over 896 steps |
The model reliably reproduces the exact {"explicit":[{"content":...}]} schema with correct
attribution, absolute dates, and atomic facts. Worked example (held-out):
Input : message about buying a secondhand Eames lounge chair
Output : {"explicit": [
{"content": "dmitri bought a secondhand Eames lounge chair from a Facebook listing in Williamsburg"},
{"content": "dmitri paid $900 for the secondhand Eames lounge chair"},
{"content": "dmitri rented a Zipcar to haul the Eames lounge chair back, costing another $80"} ]}
LoCoMo benchmark (base Qwen3-8B vs this model, through Honcho's dialectic pipeline) — to be added. Reference points from Plastic Labs' blog (different/private data, not directly comparable): base Qwen3-8B 69.6, Claude 4 Sonnet 80.0, Neuromancer XR 86.9.
Training
- Base:
Qwen/Qwen3-8B - Data: 7,160 synthetic SFT examples, gold labels distilled from Claude Opus 4.8 (frontier
teacher), 15 balanced life-domains, rendered through Honcho's own prompt-builder and schema-validated
against
PromptRepresentation. Distilling a compact deriver from a frontier model is the core idea. - Method: bf16 LoRA (rank 32, alpha 32), 2 epochs, lr 2e-4 cosine, completion-only loss.
- Hardware: NVIDIA RTX PRO 6000 Blackwell (sm_120), ~56 min.
Prompt / I-O contract
Input = Honcho's minimal_deriver_prompt(peer_id, messages) verbatim (non-thinking).
Output = JSON validated by PromptRepresentation:
{"explicit": [{"content": "alice is training for a half-marathon scheduled for October 2026"}]}
Rules: atomic · self-contained · absolute dates · correct third-party attribution · explicit-only
(no speculation) · {"explicit": []} when nothing is stated.
How to use with Honcho
Serve (vLLM/Ollama), expose via your gateway (e.g. litellm route recall-honcho-8b), then point
Honcho's deriver at it in config.toml:
[deriver.model_config]
transport = "openai"
model = "recall-honcho-8b"
[deriver.model_config.overrides]
base_url = "http://<gateway>/v1"
api_key_env = "LLM_OPENAI_API_KEY"
Keep the dialectic/generation step on a larger model.
Files
- Merged bf16 model (root) — servable standalone.
lora-adapter/— the LoRA adapter alone (apply ontoQwen/Qwen3-8B).
Limitations
- Explicit level only (not deductive/inductive/abductive — Honcho's dreamer).
- English only; trained on privacy-safe synthetic data (frontier-distilled).
- Optimised for Honcho's exact prompt; off-format prompts may degrade output discipline.
Acknowledgement
Inspired by Plastic Labs' Honcho and Neuromancer research. Independent synthetic re-creation; not affiliated with or endorsed by Plastic Labs.