From 0745cca33965ed6fedcda0e39f923fc76a1aa4cc Mon Sep 17 00:00:00 2001
From: Jinx <jinx@openclaw>
Date: Sun, 12 Apr 2026 22:36:55 +0000
Subject: [PATCH] fix git ignore

---
 .gitignore |   2 +-
 NOTES.md   | 103 -----------------------------------------------------
 2 files changed, 1 insertion(+), 104 deletions(-)
 delete mode 100644 NOTES.md

diff --git a/.gitignore b/.gitignore
index bca6a3b..44aad24 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,3 @@
 .env
-models.env
+/models.env
 __pycache__/
diff --git a/NOTES.md b/NOTES.md
deleted file mode 100644
index 8242035..0000000
--- a/NOTES.md
+++ /dev/null
@@ -1,103 +0,0 @@
-# SmolLM3-3B Tool Call Fix — Notes
-
-## Problem
-
-The SmolLM3-3B model's chat template has three bugs that break multi-turn tool calling in vLLM.
-
-## Bugs Found
-
-### Bug 1: Tool responses rendered as plain user messages
-**Location:** `chat_template.jinja`, main loop, `message.role == "tool"` branch
-
-**Original:**
-```jinja2
-{%- elif message.role == "tool" -%}
-{{ "<|im_start|>" + "user\n"  + content + "<|im_end|>\n" }}
-```
-
-Tool responses show up as `<|im_start|>user\n...<|im_end|>` — the model cannot distinguish a tool result from a new user turn. When it sees weather data in a user message, it re-invokes the tool instead of answering.
-
-**Fix:** Use the model's dedicated `tool_response_start`/`tool_response_end` tokens (128013/128014) to wrap tool responses so the model can distinguish them from user messages.
-
-### Bug 2: Assistant tool_calls not rendered in history
-**Location:** `chat_template.jinja`, main loop, `message.role == "assistant"` branch
-
-When the assistant message has `tool_calls`, the template only renders `content` (often empty/None) and drops the entire `tool_calls` array. The model never sees its own prior tool invocations.
-
-**Fix:** Render tool calls using the model's native `tool_call_start`/`tool_call_end` tokens (128015/128016) with proper JSON format.
-
-### Bug 3: Thinking mode inverted
-**Location:** `chat_template.jinja`, main loop and generation prompt
-
-When `reasoning_mode == "/think"`, the template does NOT wrap content in think tags. When `reasoning_mode == "/no_think"`, it DOES wrap in `...` tags. Completely backwards.
-
-**Fix:** `/think` mode wraps content in `...` tags. `/no_think` renders plain text.
-
-## Special Tokens
-
-The model has these tool-related tokens in its tokenizer (added_tokens_decoder):
-
-| Token ID | Text | Purpose |
-|----------|------|---------|
-| 128002 | `...` | Think end |
-| 128013 | `...` | Tool call start |
-| 128016 | `...` | Tool call end |
-
-## How the Fix Works
-
-### Template Changes
-
-1. **Tool responses** now render as:
-   ```
-   <|im_start|>user
-   [tool_response_start]
-   {tool result content}
-   [tool_response_end]<|im_end|>
-   ```
-   Instead of a bare user message.
-
-2. **Assistant tool calls** now render as:
-   ```
-   <|im_start|>assistant
-   {"name": "func_name", "arguments": {...}}
-   [tool_call_end]<|im_end|>
-   ```
-   Instead of being dropped entirely.
-
-3. **Thinking mode** is now correctly mapped: `/think` → think tags, `/no_think` → plain text.
-
-### Key Technical Details
-
-- The template uses Jinja2's `~` operator instead of `+` for string concatenation. This avoids type errors when `message.content` is `None` (Jinja2's `~` coerces to string, `+` does not).
-- The `tool_call_start`/`tool_call_end` tokens are Unicode private-use-area characters that can't be typed in a text editor. The template must be generated programmatically using `gen_template.py`.
-- The `tc.function.name` and `tc.function.arguments` Jinja2 dot notation works correctly because Jinja2 resolves `dict.key` as `dict["key"]`.
-- The `{% generation %}` tag is vLLM-specific and marks the assistant output region. It must be preserved.
-
-## Files
-
-- `model-files/chat_template.jinja` — The fixed template (generated, contains Unicode PUA characters)
-- `model-files/gen_template.py` — Script to regenerate the template inside the container where the tokenizer is available
-- `model-files/hermes_tool_parser.py` — vLLM Hermes tool parser (unchanged, works as-is for parsing `...` format)
-
-## Deploying
-
-1. Run `gen_template.py` inside the vLLM container:
-   ```bash
-   docker cp model-files/gen_template.py smol-vllm-1:/tmp/
-   docker exec smol-vllm-1 python3 /tmp/gen_template.py
-   ```
-
-2. Copy the generated template to the mounted volume:
-   ```bash
-   docker cp smol-vllm-1:/root/chat_template.jinja /root/smol/chat_template.jinja
-   ```
-
-3. Restart the container:
-   ```bash
-   cd /root/smol && docker compose restart
-   ```
-
-## Remaining Issues
-
-- The model sometimes re-invokes tools in a loop instead of providing a final text answer. This is likely a training issue with the `/no_think` mode — the model outputs reasoning as content text but still generates tool calls.
-- The Hermes tool parser works for parsing `...` blocks but the streaming parser may buffer long argument strings. This is a vLLM-level issue, not a template issue.