---
base_model: LiquidAI/LFM2.5-1.2B-Thinking
library_name: gguf
pipeline_tag: text-generation
tags: [ghost-ai, on-device, function-calling, solana, gguf, tool-calling]
---

# GhostAI LFM app-contract SFT

LFM2.5-1.2B-Thinking fine-tuned to the **GhostWallet mobile app's real on-device tool
contract** — one Hermes `<tool_call>` block at a time, chosen from the app's retrieved
tool catalog.

Earlier Ghost AI checkpoints were trained against `plan-schema.md` (a plan object, then
bare per-step argument JSON). The app expects something different, so those models emitted
argument objects with no tool name and the app's parser discarded them. This model targets
the app contract directly.

## Results — app harness (56 held-out utterances, Q4_K_M, greedy)

| metric | prior checkpoint | **this model** |
|---|---|---|
| emitted a parseable tool call | 35.7% | **92.9%** |
| args passed the app's validator | 32.1% | **87.5%** |
| called the expected tool | 16.1% | **23.2%** |
| emitted plan JSON (wrong contract) | — | **0** |

`tool_correct` is capped by **retrieval, not by the model**. The app surfaces the correct
tool in its top-5 catalog only 10.7% of the time on these utterances, and a model cannot
call a tool it is never offered. Forcing the correct tool into the catalog
(`GHOSTAI_BOOST=1`) isolates the model:

| with correct tool in catalog | prior | **this model** |
|---|---|---|
| emitted a parseable call | 26.8% | **100%** |
| called the expected tool | 21.4% | **98.2%** |
| args valid | — | **94.6%** |

**Tool selection and argument construction are effectively solved (98.2%).** The remaining
production gap is the app's tool-retrieval ranking, which is an app-side concern.
Note this measures the hash-embedding fallback ranking; the on-device path loads a real
embedding model and is untested here.

## Results — end-to-end harness (76 cases, 88 turns, app's real ChatSession)

| | baseline | **this model** |
|---|---|---|
| overall | 44% | **56.6%** |
| grounding | 24% | **44.1%** |
| value gate | 67% | **75%** |
| injection resistance | 100% | **94.4%** |
| multi-turn | 17% | 16.7% |
| gate bypasses | 0 | **0** |
| planted content reaching a tool arg | 0 | **0** |
| completion tokens / turn | 63 | **40.7** |

## Known regressions and weaknesses

1. **Injection resistance regressed, 100% → 94.4%.** One adversarial case now fails. Both
   absolute invariants still hold — no confirm-gate bypass, and no planted content reached
   a tool argument — so this is the milder failure class, but it is a regression on a
   privacy-first product and should be reviewed before shipping.
2. **Multi-turn is weak (16.7%)** and did not improve. Follow-ups, pronoun resolution and
   topic switches remain unreliable.
3. **5 turns invented numbers** not present in tool output, and 16 turns called tools
   outside the stub set.
4. **Retrieval, not the model, is the production bottleneck** (10.7% primary_retrievable).
   Fixing model quality further will not move end-to-end numbers until retrieval improves.

## Training

| | |
|---|---|
| data | 5,407 app-contract traces covering **174/174** app tools (previously 58/174) |
| generation | teacher-distilled (Qwen3.6-35B-A3B), every argument object validated against its tool's schema before acceptance — 98% acceptance |
| prompts | wrapped by the app's own `ContextManager.assembleMessages`, so they are byte-identical to what ships |
| objective | SFT, **assistant-only loss** (full-sequence loss teaches the model to reproduce rendered tool-call history verbatim) |
| epochs | 2, bf16, DeepSpeed ZeRO-3, 2x A100 |
| eval | 0.1705 loss / 0.963 token accuracy; split by utterance-template skeleton |

## Files

| File | Size |
|---|---|
| `lfm-app-sft-F16.gguf` | 2.3 GB |
| `lfm-app-sft-Q4_K_M.gguf` | 695 MB — shipping quantization |

## Usage

Serve with llama.cpp (the app embeds `llama.rn`):

```bash
llama-server -m lfm-app-sft-Q4_K_M.gguf --port 8099 -ngl 99 -c 8192 --jinja
```

The model expects the app's assembled system prompt (tool-call format instructions plus a
compact catalog of retrieved tools) and emits:

```
<tool_call>{"name":"send_sol","arguments":{"recipient":"mom","amount":5}}</tool_call>
```

## License

Base model is under the **LFM Open License**, which permits commercial use only below
**$10M annual revenue** (§5); above that threshold commercial use requires a separate
agreement with Liquid AI. That condition attaches to derivative works, including this one.