初始化项目，由ModelHub XC社区提供模型

Model: GhostA1/GhostAI_LiquidSFT-v2
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+base_model: LiquidAI/LFM2.5-1.2B
+tags:
+  - gguf
+  - llama.cpp
+  - lfm2
+  - on-device
+  - tool-calling
+  - solana
+  - wallet-assistant
+  - full-finetune
+library_name: gguf
+pipeline_tag: text-generation
+---
+
+# GhostAI_LiquidSFT v2 (full fine-tune)
+
+On-device **Solana wallet assistant** — a **full-weight** fine-tune of **LFM2.5-1.2B** for
+mobile inference (llama.cpp / llama.rn). v2 improves on the v1 LoRA model with a larger,
+teacher-augmented + cleaned dataset.
+
+## What's new vs v1
+- **Full-weight fine-tune** (8-GPU DDP) instead of LoRA → **eval_loss 0.1534** (v1 LoRA: 0.1736)
+- Dataset grown to **~78k cleaned rows** via grounded augmentation (Qwen3.6 teacher + Google-grounded Solana facts), with: tool-error recovery, multi-step chains, clarification on high-stakes asks, follow-ups, hard negatives, and Ghost AI identity.
+- Every tool-call validated against the 172-tool schema; tool args grounded in context (no hallucinated addresses).
+
+## Held-out evaluation
+| metric | score |
+|---|---|
+| Tool name correct | **97.9%** |
+| Tool full call (name + all args exact) | **85.3%** |
+| Negatives (no over-trigger) | 88.9% |
+| eval_loss | 0.1534 |
+
+## Files
+| file | quant | size | use |
+|---|---|---|---|
+| `GhostAI_LiquidSFT_v2.Q4_0.gguf` | Q4_0 | ~664 MB | **Phones (ARM)** — fastest TTFT+tok/s |
+| `GhostAI_LiquidSFT_v2.Q4_K_M.gguf` | Q4_K_M | ~698 MB | desktop balance |
+| `GhostAI_LiquidSFT_v2.Q5_K_M.gguf` | Q5_K_M | ~805 MB | higher quality |
+| `GhostAI_LiquidSFT_v2.Q6_K.gguf` | Q6_K | ~919 MB | near-lossless |
+| `GhostAI_LiquidSFT_v2.BF16.gguf` | BF16 | ~2.2 GB | reference |
+
+## ⚠️ Serving note (important)
+This model is trained **train==serve** with the on-device **tool-catalog system prompt**.
+Always send that catalog as the `system` message — with an ad-hoc system prompt, tool-calling
+degrades. Tool calls use Hermes format: `<tool_call>{"name":...,"arguments":{...}}</tool_call>`.
+
+## Training
+LFM2.5-1.2B-Instruct base · full fine-tune · lr 1e-5 · 2 epochs · eff-batch 256 · bf16 ·
+`completion_only_loss` (user/tool turns masked) · seq 2048 (0% truncation).

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