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Model: dman1011/recall-honcho-8b
Source: Original Platform

README.md

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+---
+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.