65 lines
1.6 KiB
Markdown
65 lines
1.6 KiB
Markdown
---
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base_model: Qwen/Qwen3-4B-Instruct-2507
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datasets:
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- u-10bei/sft_alfworld_trajectory_dataset_v5
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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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- agent
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- tool-use
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- alfworld
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- dbbench
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---
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# qwen3-adv-comp-v34
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This repository provides a **LoRA adapter** fine-tuned from
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**Qwen/Qwen3-4B-Instruct-2507** using **LoRA + Unsloth**.
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This repository contains **LoRA adapter weights only**.
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The base model must be loaded separately.
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## Training Objective
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This adapter is trained to improve **multi-turn agent task performance**
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on ALFWorld (household tasks) and DBBench (database operations).
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Loss is applied to **all assistant turns** in the multi-turn trajectory,
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enabling the model to learn environment observation, action selection,
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tool use, and recovery from errors.
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## Training Configuration
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- Base model: Qwen/Qwen3-4B-Instruct-2507
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- Method: LoRA (full precision base)
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- Max sequence length: 2048
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- Epochs: 2
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- Learning rate: 2e-6
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- LoRA: r=64, alpha=128
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from peft import PeftModel
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import torch
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base = "motobrew/qwen3-adv-comp-v34"
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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.float16,
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device_map="auto"
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)
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```
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## Sources & Terms (IMPORTANT)
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Training data: u-10bei/sft_alfworld_trajectory_dataset_v5
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Dataset License: MIT License. This dataset is used and distributed under the terms of the MIT License.
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Compliance: Users must comply with the MIT license (including copyright notice) and the base model's original terms of use. |