70 lines
2.2 KiB
Markdown
70 lines
2.2 KiB
Markdown
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---
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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
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- u-10bei/sft_alfworld_trajectory_dataset_v2
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- u-10bei/sft_alfworld_trajectory_dataset_v3
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- u-10bei/sft_alfworld_trajectory_dataset_v4
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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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- unsloth
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- agent
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- tool-use
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- alfworld
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---
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# Qwen3-4B-Agent-ALFWorld-Specialist
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This repository provides a **merged full-parameter model** (bfloat16) fine-tuned from **Qwen/Qwen3-4B-Instruct-2507**.
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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.
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## Training Objective
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This model is specialized for **ALFWorld trajectory tasks**, trained to handle multi-turn environment observations and action selections.
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## Training Configuration
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- **Base model**: Qwen/Qwen3-4B-Instruct-2507
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- **Format**: Merged Full Weights (bfloat16)
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- **Method**: LoRA fine-tuning (Merged via Unsloth `merge_and_unload`)
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- **Max sequence length**: 4096
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- **Steps**: 600
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- **Learning rate**: 5e-07
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- **LoRA Parameters during training**: r=64, alpha=128
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- **Platform**: Trained with Unsloth
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## Usage
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Since this is a merged model, you can load it directly like any other Qwen3 model:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "moushi21/agent-bench-alfworld-merged3"
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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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```
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## Sources & Terms (IMPORTANT)
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Training data:
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- u-10bei/sft_alfworld_trajectory_dataset
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- u-10bei/sft_alfworld_trajectory_dataset_v2
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- u-10bei/sft_alfworld_trajectory_dataset_v3
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- u-10bei/sft_alfworld_trajectory_dataset_v4
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- 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.
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