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Model: kamaboko2007/llm_advance_024_enhanced_rules
Source: Original Platform

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))
+```