recall-honcho-8b/README.md

---
license: apache-2.0
language:
- en
base_model: Qwen/Qwen3-8B
library_name: transformers
pipeline_tag: text-generation
tags:
- memory
- honcho
- deriver
- conclusion-extraction
- observation-extraction
- qwen3
- lora
- distillation
- claude-opus-4.8
---

# 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](https://github.com/plastic-labs/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`:
```json
{"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`:
```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 onto `Qwen/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.