初始化项目，由ModelHub XC社区提供模型

Model: kamaboko2007/llm_advance_024_enhanced_rules
Source: Original Platform

.gitattributes

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README.md

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+---
+base_model: Qwen/Qwen3-4B-Instruct-2507
+language:
+- en
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- agent
+- tool-use
+- alfworld
+- dbbench
+- unsloth
+- agentbench
+---
+
+# Qwen3-4B AgentBench "023-Jinja-Heuristics" LoRA
+
+This repository provides a highly optimized **merged model** fine-tuned from **Qwen/Qwen3-4B-Instruct-2507**.
+It is specifically engineered to achieve state-of-the-art performance on **AgentBench** (specifically ALFWorld and DBBench) by solving the catastrophic forgetting and format-collision problems inherent in multi-task agent fine-tuning.
+
+This repository contains the **fully merged model** (base + LoRA merged). No separate base model loading is needed.
+
+## Key Innovation: Jinja2 Contextual Routing & Heuristics Injection
+
+The true power of this model lies not just in its weights, but in its **custom `tokenizer_config.json`**.
+We completely overrode the default `chat_template` using Jinja2 to act as an "Absolute Defense Shield" and a "Dynamic Heuristics Injector".
+
+Depending on the user's prompt, the tokenizer automatically intercepts the input and injects task-specific System Prompts (Cheat Sheets) *just before* inference:
+
+### 1. DB Bench (MySQL) Mode
+When `MySQL` or `SQL` is detected in the prompt, the model is forced into a DB Agent persona with the following injected rules:
+- **Error Recovery:** "If you encounter an SQL error (e.g., 'Unknown column'), DO NOT panic. Use `Action: Operation` to execute `DESCRIBE table_name;` and check the correct schema before retrying."
+- **Loop Prevention:** "Never repeat the exact same invalid SQL."
+
+### 2. ALFWorld (Household) Mode
+When `household` or `Interact with a` is detected, the model is forced into an ALFWorld Agent persona:
+- **Format Override:** Completely ignores the evaluation system's trap (`THOUGHT:`/`ACTION:`) and strictly enforces the stable `Think:`/`Act:` format.
+- **Exploration Logic:** "If an action fails (`Nothing happened`), analyze why in your `Think:` step and choose a DIFFERENT action."
+- **Efficiency:** "If you search a receptacle and do not find the target object, DO NOT search it again. Move to a different location."
+
+## Training Configuration (The "Golden Ratio")
+
+To maximize reasoning capabilities without exceeding the 4B model's capacity, we used a highly curated "Golden Ratio" dataset:
+- **Dataset:** ALFWorld v5 Trajectories + DBBench Distilled (494 high-quality, noise-free trajectories).
+- **Method:** LoRA (full precision base) via Unsloth.
+- **Loss Strategy:** Loss is applied strictly to **all assistant turns** in the multi-turn trajectory, ignoring user/system prompts.
+
+**Hyperparameters:**
+- Max sequence length: 8192
+- Epochs: 2
+- Learning rate: 1e-6
+- LoRA Rank (r): 64
+- LoRA Alpha: 128
+- Target Modules: `q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj`
+
+## Usage
+
+Because the magic is embedded in the Jinja2 `chat_template`, you **must** use this tokenizer to see the performance gains.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "your_huggingface_id/your_model_name"  # Change this to your actual repo ID
+
+# 1. Load the customized tokenizer (CRITICAL)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+# 2. Load merged model directly
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+# 3. Standard Inference (The Jinja2 template handles the routing automatically)
+messages = [
+    {"role": "user", "content": "You are a specialized MySQL database agent..."}
+]
+inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=512)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```

added_tokens.json

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+{
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+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
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+  "<|quad_end|>": 151651,
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+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 9728,
+  "layer_types": [
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+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
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+    "full_attention",
+    "full_attention",
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+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 262144,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 5000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.56.2",
+  "unsloth_version": "2025.12.7",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+  "do_sample": true,
+  "eos_token_id": [
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+  ],
+  "max_length": 262144,
+  "pad_token_id": 151643,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.56.2"
+}

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special_tokens_map.json

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tokenizer.json

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+size 11422654

tokenizer_config.json

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+}