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Model: Mountaingorillas/Qwen-2.5-7B-Instruct-Agentbench-lora-MixedLearning-v2
Source: Original Platform

README.md

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+---
+base_model: unsloth/Qwen2.5-7B-Instruct
+datasets:
+- Mountaingorillas/sft_dbbench_merged_v4_strict
+- u-10bei/sft_alfworld_trajectory_dataset
+language:
+- en
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- sft
+- unsloth
+- qwen
+- agent
+- function-calling
+- lora
+---
+
+# Qwen-2.5-7B-Instruct-Agentbench-MixedLearning-v2
+
+This repository provides a **fully merged model** (Base + LoRA adapter) fine-tuned from `Qwen/Qwen2.5-7B-Instruct`. 
+Unlike the previous adapter-only version, this model can be loaded directly without requiring the base model to be loaded separately.
+
+## 🎯 Model Objective & Key Innovations
+This model is heavily optimized to achieve high performance on multi-turn agent tasks, specifically targeting **ALFWorld** (household embodied tasks) and **DBBench** (database operations) for the LLM2025 Agent competition.
+
+To overcome common agentic failure modes (e.g., format violations, parsing errors), I implemented a highly effective **Hybrid Reasoning Schema (Data Mixing)** strategy:
+
+- Trained to seamlessly switch between two completely different inference formats based on the prompt context, without causing catastrophic forgetting.
+- **DBBench:** Strictly follows the `ReAct` format (`Action: Operation` -> ````sql```` -> `Action: Answer`), maintaining perfect adherence to exact SQL string syntax.
+- **ALFWorld:** Employs native `Function Calling` (`tool_calls` for the `act` function) to ensure strict environment interactions and avoid invalid action errors caused by plain text parsing.
+
+## Training objective
+Loss is applied to **all assistant turns** in the multi-turn trajectory, 
+enabling the model to effectively learn environment observation, action selection, tool use, and recovery from errors.
+
+## ⚙️ Training Configuration
+- **Base model:** `unsloth/Qwen2.5-7B-Instruct`
+- **Method:** LoRA (Merged into full weights)
+- **Epochs:** 3
+- **Learning rate:** 2e-05
+- **LoRA params:** r=64, alpha=128
+- **Max sequence length:** 3072
+
+## 💻 Usage
+Since the LoRA weights are already merged, you can load this model directly using the standard `transformers` library.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Please ensure this matches your actual Hugging Face repository name
+model_id = "Mountaingorillas/Qwen-2.5-7B-Instruct-Agentbench-lora-MixedLearning-v2"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+# Example input preparation
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "Hello!"}
+]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.0)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+📚 Sources & License (IMPORTANT)
+Training Data:
+
+DBBench (ReAct): Mountaingorillas/sft_dbbench_merged_v4_strict
+
+ALFWorld (Function Calling): u-10bei/sft_alfworld_trajectory_dataset
+
+License: MIT License. (As per dataset terms).
+
+Compliance: Users must follow the original base model's (Qwen2.5-7B-Instruct) license terms and acceptable use policies.