Add critical training objective: teach model to emit native tool-call tokens

README.md

@@ -1,6 +1,20 @@
 # SmolLM3-3B LoRA — Tool Calling Fine-Tune
 
-LoRA adapter training to make SmolLM3-3B a tool-calling savant.
+LoRA adapter training to teach SmolLM3-3B to emit native tool-call tokens.
+
+## Critical Training Objective
+
+The base model **does not emit structured tool-call tokens**. When asked to use tools, it writes Python code that *calls* the tool as a function instead of emitting the `startPos`/`endPos` (token IDs 128002/128016) sequences that vLLM's Hermes parser expects. This was verified definitively using a raw token inspector (`/home/openclaw/dev/chat-template-debugger/`) that bypasses all middleware and calls `llm.generate()` directly.
+
+**The #1 priority for this LoRA run** is to make the model emit tool-call tokens natively. Specifically:
+
+1. When the user asks the model to use a tool, the model should emit `startPos` → JSON function call → `endPos` instead of writing `from tools import X` / `X(args)` as Python code
+2. This must work for **all** tool patterns — not just structured JSON tools (save_config) but also code-generation tools (write_file) that the model currently code-dumps instead of calling
+3. The model should still produce clean text content when NOT invoking a tool — we're adding a capability, not replacing one
+
+### Why this matters
+
+The current "working" save_config path through the vLLM API is not actually the model doing tool calls — the Hermes parser is reconstructing tool calls from the model's text/code output. This is fragile and fails for longer outputs (write_file). Once the model emits native tool-call tokens, both paths work correctly and the parser doesn't need to do salvage work.
 
 ## Quick Start