92 lines
2.7 KiB
Markdown
92 lines
2.7 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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- u-10bei/dbbench_sft_dataset_react
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- u-10bei/dbbench_sft_dataset_react_v2
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- u-10bei/dbbench_sft_dataset_react_v3
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- u-10bei/dbbench_sft_dataset_react_v4
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language:
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- en
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license: apache-2.0
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library_name: mergekit
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pipeline_tag: text-generation
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tags:
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- merge
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- ties
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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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## Performance
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The model shows balanced performance across agentic tasks:
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- **ALFWorld**: 0.60 (Pass@1)
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- **DBBench**: 0.5353
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# Qwen3-4B Agent TIES Merge v1.2
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This repository provides a merged model optimized for agentic tasks, created by merging specialized LoRA adapters into the base model **Qwen/Qwen3-4B-Instruct-2507**.
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The merge was performed using the **TIES-Merging** method via **Mergekit**, combining expertise in ALFWorld trajectories and DBBench (SQL) tasks.
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This repository contains **full model weights**, ready for inference without needing to load separate adapters.
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## Merge Details
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This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) method.
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### Merge Method
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- **Method**: `ties`
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- **Base Model**: `Qwen/Qwen3-4B-Instruct-2507`
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- **Dtype**: `bfloat16`
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### Configuration
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The following mergekit-yaml configuration was used to produce this model:
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```config.yaml
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merge_method: ties
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base_model: Qwen/Qwen3-4B-Instruct-2507
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dtype: bfloat16
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models:
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- model: moushi21/agent-bench-alfworld-merged3
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parameters:
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weight: 1.0 # ALF Main
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density: 0.3 # 70% Noise Cut
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- model: moushi21/agent-bench-dbbench-merged4
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parameters:
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weight: 0.3 # DB
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density: 0.3 # 70% Noise Cut
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```
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## Usage
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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-merged12"
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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 (Source Model Lineage)
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The source models utilized the following datasets for fine-tuning:
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* **ALFWorld**: `u-10bei/sft_alfworld_trajectory_dataset` (v1 to v5)
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* **DBBench**: `u-10bei/dbbench_sft_dataset_react` (v1 to v4)
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### License & Compliance
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* **Dataset License**: MIT License. These datasets are 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 (**Qwen3-4B-Instruct-2507**).
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