---
license: apache-2.0
base_model: LiquidAI/LFM2.5-1.2B
tags:
  - gguf
  - llama.cpp
  - lfm2
  - on-device
  - tool-calling
  - solana
  - wallet-assistant
library_name: gguf
pipeline_tag: text-generation
---

# GhostAI_LiquidSFT

On-device **Solana wallet assistant** fine-tuned from **LFM2.5-1.2B** (Liquid AI) for
mobile inference via **llama.cpp / llama.rn**. Trained to call wallet tools in the
**Hermes `<tool_call>{...}</tool_call>`** format and to answer Solana / DeFi / wallet-security
questions.

## Files

| File | Quant | Size | Use |
|------|-------|------|-----|
| `GhostAI_LiquidSFT.Q4_K_M.gguf` | Q4_K_M | ~698 MB | **Recommended for phones** — best size/quality balance |
| `GhostAI_LiquidSFT.Q5_K_M.gguf` | Q5_K_M | ~805 MB | Higher quality, modest size bump |
| `GhostAI_LiquidSFT.Q6_K.gguf`   | Q6_K   | ~919 MB | Near-lossless vs the 16-bit model |
| `GhostAI_LiquidSFT.BF16.gguf`   | BF16   | ~2.2 GB | Full-precision reference for re-quantization |

## Prompt format (ChatML)

```
<|im_start|>system
{system prompt with tool catalog}<|im_end|>
<|im_start|>user
{user message}<|im_end|>
<|im_start|>assistant
```

Tool calls are emitted as:

```
<tool_call>{"name": "get_sol_balance", "arguments": {"address": "..."}}</tool_call>
```

The ChatML chat template (with tool + `<|im_end|>` handling) is embedded in the GGUF.

## Training

- **Base:** `unsloth/LFM2.5-1.2B-Instruct`
- **Method:** LoRA SFT (Unsloth), r=32 / α=64, rsLoRA, NEFTune (α=5), bf16
- **LoRA targets:** `q,k,v,out_proj` (attention) + `in_proj` (conv) + `w1,w2,w3` (MLP) — all 16 layers
- **Data:** merged tool-calling dataset (Hermes, 172 tools) + Solana/DeFi/security knowledge base
- **Seq len:** 2048 (0% truncation) · response-only masking (user **and** tool turns masked)
- **Result:** best eval_loss **0.1736**, converged at ~1 epoch (early-stopped)

## Run with llama.cpp

```bash
llama-cli -m GhostAI_LiquidSFT.Q4_K_M.gguf -p "<|im_start|>user\nWhat is my SOL balance?<|im_end|>\n<|im_start|>assistant\n"
```