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Model: moushi21/agent-bench-alfworld-merged3
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---
base_model: Qwen/Qwen3-4B-Instruct-2507
datasets:
- u-10bei/sft_alfworld_trajectory_dataset
- u-10bei/sft_alfworld_trajectory_dataset_v2
- u-10bei/sft_alfworld_trajectory_dataset_v3
- u-10bei/sft_alfworld_trajectory_dataset_v4
- u-10bei/sft_alfworld_trajectory_dataset_v5
language:
- en
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
tags:
- unsloth
- agent
- tool-use
- alfworld
---
# Qwen3-4B-Agent-ALFWorld-Specialist
This repository provides a **merged full-parameter model** (bfloat16) fine-tuned from **Qwen/Qwen3-4B-Instruct-2507**.
Instead of a standalone LoRA adapter, this model has been created by merging LoRA weights back into the base model using **Unsloth's `merge_and_unload`** method. This ensures high-speed inference and easy deployment.
## Training Objective
This model is specialized for **ALFWorld trajectory tasks**, trained to handle multi-turn environment observations and action selections.
## Training Configuration
- **Base model**: Qwen/Qwen3-4B-Instruct-2507
- **Format**: Merged Full Weights (bfloat16)
- **Method**: LoRA fine-tuning (Merged via Unsloth `merge_and_unload`)
- **Max sequence length**: 4096
- **Steps**: 600
- **Learning rate**: 5e-07
- **LoRA Parameters during training**: r=64, alpha=128
- **Platform**: Trained with Unsloth
## Usage
Since this is a merged model, you can load it directly like any other Qwen3 model:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "moushi21/agent-bench-alfworld-merged3"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto"
)
```
## Sources & Terms (IMPORTANT)
Training data:
- u-10bei/sft_alfworld_trajectory_dataset
- u-10bei/sft_alfworld_trajectory_dataset_v2
- u-10bei/sft_alfworld_trajectory_dataset_v3
- u-10bei/sft_alfworld_trajectory_dataset_v4
- u-10bei/sft_alfworld_trajectory_dataset_v5
Dataset License: MIT License. This dataset is used and distributed under the terms of the MIT License.
Compliance: Users must comply with the MIT license (including copyright notice) and the base model's original terms of use.