6870118180
Model: Mountaingorillas/Qwen-2.5-7B-Instruct-Agentbench-lora-MixedLearning-v2 Source: Original Platform
84 lines
3.3 KiB
Markdown
84 lines
3.3 KiB
Markdown
---
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base_model: unsloth/Qwen2.5-7B-Instruct
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datasets:
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- Mountaingorillas/sft_dbbench_merged_v4_strict
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- u-10bei/sft_alfworld_trajectory_dataset
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language:
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- en
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license: apache-2.0
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- sft
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- unsloth
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- qwen
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- agent
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- function-calling
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- lora
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---
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# Qwen-2.5-7B-Instruct-Agentbench-MixedLearning-v2
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This repository provides a **fully merged model** (Base + LoRA adapter) fine-tuned from `Qwen/Qwen2.5-7B-Instruct`.
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Unlike the previous adapter-only version, this model can be loaded directly without requiring the base model to be loaded separately.
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## 🎯 Model Objective & Key Innovations
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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.
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To overcome common agentic failure modes (e.g., format violations, parsing errors), I implemented a highly effective **Hybrid Reasoning Schema (Data Mixing)** strategy:
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- Trained to seamlessly switch between two completely different inference formats based on the prompt context, without causing catastrophic forgetting.
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- **DBBench:** Strictly follows the `ReAct` format (`Action: Operation` -> ````sql```` -> `Action: Answer`), maintaining perfect adherence to exact SQL string syntax.
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- **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.
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## Training objective
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Loss is applied to **all assistant turns** in the multi-turn trajectory,
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enabling the model to effectively learn environment observation, action selection, tool use, and recovery from errors.
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## ⚙️ Training Configuration
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- **Base model:** `unsloth/Qwen2.5-7B-Instruct`
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- **Method:** LoRA (Merged into full weights)
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- **Epochs:** 3
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- **Learning rate:** 2e-05
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- **LoRA params:** r=64, alpha=128
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- **Max sequence length:** 3072
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## 💻 Usage
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Since the LoRA weights are already merged, you can load this model directly using the standard `transformers` library.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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# Please ensure this matches your actual Hugging Face repository name
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model_id = "Mountaingorillas/Qwen-2.5-7B-Instruct-Agentbench-lora-MixedLearning-v2"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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# Example input preparation
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "Hello!"}
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer([text], return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.0)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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📚 Sources & License (IMPORTANT)
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Training Data:
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DBBench (ReAct): Mountaingorillas/sft_dbbench_merged_v4_strict
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ALFWorld (Function Calling): u-10bei/sft_alfworld_trajectory_dataset
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License: MIT License. (As per dataset terms).
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Compliance: Users must follow the original base model's (Qwen2.5-7B-Instruct) license terms and acceptable use policies. |